text:
@article{mourguiart2021,
	title = {Multi-species occupancy models: an effective and flexible framework for studies of insect communities},
	volume = {46},
	copyright = {© 2020 The Royal Entomological Society},
	issn = {1365-2311},
	shorttitle = {Multi-species occupancy models},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/een.12991},
	doi = {10.1111/een.12991},
	abstract = {Entomological studies often aim to estimate species distribution, community composition, or species-richness patterns. False absences can, however, bias these estimates and should consequently not be overlooked in insect studies. Multi-species occupancy models (MSOMs) afford a flexible solution to cover the main topics in ecological entomology while dealing with detectability issues. We sampled Orthoptera communities at 81 mountain grasslands sites in France, using three sampling techniques: sighting, listening, and sweep netting. Five plots were sampled per site. This sampling design allowed MSOMs to be used to estimate richness, occupancy, and detection probabilities while accounting for the effect of covariates. We also used MSOMs to evaluate the efficiency of the survey design and to assess the effects of sampling optimisation. The estimates obtained for altitudinal distribution were reliable, with known species distributions confirming the relevance of MSOMs to model the effects of covariates on Orthoptera communities. The species-specific detection probability was often less than one and varied with the detection technique used and the grass height, confirming the need to deal with detection issues in orthopteran studies. We estimated an inventory completeness superior to 0.80 for 93\% of the sites, and an overall detection probability superior to 0.95 for 52\% of the species, suggesting the sampling design was suitable for studying occupancy in Orthoptera communities. We also found that the sweep netting step may be omitted or the number of plots reduced without affecting species detectability or inventory completeness. Those recommendations may help to optimise future sampling strategies.},
	language = {en},
	number = {2},
	urldate = {2021-06-23},
	journal = {Ecological Entomology},
	author = {Mourguiart, Bastien and Couturier, Thibaut and Braud, Yoan and Mansons, Jérôme and Combrisson, Damien and Besnard, Aurélien},
	year = {2021},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/een.12991},
	keywords = {Hierarchical model, imperfect detection, orthoptera communities, sampling efficiency, sampling optimisation, species distribution modelling},
	pages = {163--174},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/KBM8N83T/Mourguiart et al. - 2021 - Multi-species occupancy models an effective and f.pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/UXCM45CB/een.html:text/html},
}

@article{shoo2006,
	title = {Detecting climate change induced range shifts: {Where} and how should we be looking?},
	volume = {31},
	issn = {1442-9993},
	shorttitle = {Detecting climate change induced range shifts},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1442-9993.2006.01539.x},
	doi = {10.1111/j.1442-9993.2006.01539.x},
	abstract = {Abstract: Global climate warming is expected to cause systematic shifts in the distribution of species and consequently increase extinction risk. Conservation managers must be able to detect, measure and accurately predict range shifts in order to mitigate impacts on biodiversity. However, important responses to climate change may go unnoticed or be dismissed if we fail to collect sufficient baseline data and apply the most sensitive analytical tests. Here we use randomizations of a contemporary data set on rainforest birds of north-eastern Australia to quantify the sensitivity of three measures for assessing range shifts along altitudinal gradients. We find that smaller range shifts are detectable by analysing change in the mean altitude of presence records rather than upper or lower range boundaries. For a moderate survey effort of 96 surveys, measurements of change in the mean altitude of 34 species have the capacity to provide strong inference for a mean altitudinal range shift as small as 40 m across the species assemblage. We also show that range shifts measured at range boundaries can be potentially misleading when differences in sampling effort between contemporary and historical data sets are not taken into account.},
	language = {en},
	number = {1},
	urldate = {2021-06-23},
	journal = {Austral Ecology},
	author = {Shoo, Luke P. and Williams, Stephen E. and Hero, Jean-Marc},
	year = {2006},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1442-9993.2006.01539.x},
	keywords = {altitudinal gradient, climate change, mean comparison method, minimum detectable range shift, optimum shift, rainforest bird, range shift, sampling effort, species distribution, species range, statistical power, T-test},
	pages = {22--29},
	annote = {Summary
Determine and compare the effect of sampling effort on the magnitude of minimum detectable range and optimum shifts using t-test method.
Notes
- interesting for discussion about sampling effort, avoiding edge species and utility of optimums comparison rather than range comparison},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/JHCTF65I/Shoo et al. - 2006 - Detecting climate change induced range shifts Whe.pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/BEDYQHJS/j.1442-9993.2006.01539.html:text/html},
}

@article{strebel2014,
	title = {Studying phenology by flexible modelling of seasonal detectability peaks},
	volume = {5},
	copyright = {© 2014 The Authors. Methods in Ecology and Evolution © 2014 British Ecological Society},
	issn = {2041-210X},
	url = {https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.12175},
	doi = {10.1111/2041-210X.12175},
	abstract = {Many animals and plant species have advanced spring phenology in response to climate warming. The majority of avian phenological studies are based on arrival dates. Consequently, knowledge on bird phenology is mainly based on migratory species. In addition, arrival dates of migratory birds may be substantially affected by en route climate conditions, thus failing to provide good indicators for spring phenology on the breeding grounds. Correlating arrival dates with other phenological data or with environmental covariates may be meaningless in these cases. We propose the date of highest singing activity, quantified by detection probability, as a powerful proxy for breeding phenology that is applicable to migratory and sedentary bird species alike. In contrast to arrival dates, breeding phenology is mainly (non-migrants) or at least partially (migrants) influenced by conditions experienced within the breeding area. We developed a new method for flexible estimation of peak detectability date in spring by combining multiseason site occupancy with semi-parametric regression modelling (thin-plate splines). We applied our approach to opportunistic observations of 27 bird species (mostly passerines) in Switzerland. We found substantial differences among species in the date of spring peak detectability: late February to mid-April in sedentary and short-distance migratory species and mid-April to late May in long-distance migrants. Among 10 species with data for {\textgreater}9 years, five showed a trend in detectability peaks towards an earlier spring phenology by nine to 17 days within 10 years. The mean shift over all species was c. 3·5 days per 10 years. Our approach is widely applicable, especially for temporally and spatially large-scale data from monitoring or citizen-science programmes. Besides using the detectability peak as measure of phenology, the estimated seasonal pattern in detectability can help designing monitoring programmes for improved efficiency. Our approach may be applied to any species with pronounced acoustic displays or other behavioural traits strongly influencing detectability during the breeding period. We believe that it can contribute substantially to unravelling how species and communities respond to environmental change.},
	language = {en},
	number = {5},
	urldate = {2021-06-23},
	journal = {Methods in Ecology and Evolution},
	author = {Strebel, Nicolas and Kéry, Marc and Schaub, Michael and Schmid, Hans},
	year = {2014},
	note = {\_eprint: https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.12175},
	keywords = {bayesian, citizen-science data, GAM, imperfect detection, long-term monitoring programmes, optimum shift, peak detectability, phenological shift, site-occupancy model, site-occupancy models, splines, spring detectability peak},
	pages = {483--490},
	annote = {Summary
"We developed a new method for flexible estimation of peak detectability date in spring by combining multiseason site occupancy with semi-parametric regression modelling (thin-plate splines).We applied our approach to opportunistic observations of 27 bird species (mostly passerines) in Switzerland."
Notes
- Writing ideas: "Our method could also be applied to detect change in detectability/phenological peak/optimum [@Strebel\_phenology\_2014]},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/ZSIHDBW8/Strebel et al. - 2014 - Studying phenology by flexible modelling of season.pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/8ZBX5DZS/2041-210X.html:text/html},
}

@article{moussus2010,
	title = {Featuring 10 phenological estimators using simulated data},
	volume = {1},
	copyright = {© 2010 The Authors. Journal compilation © 2010 British Ecological Society},
	issn = {2041-210X},
	url = {https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/j.2041-210X.2010.00020.x},
	doi = {10.1111/j.2041-210X.2010.00020.x},
	abstract = {1. Studies reporting phenological changes in response to climate change are numerous and concern all groups of living organisms. Phenological changes could cause mismatching in food chains, thus inducing important perturbations in ecosystem functioning. Nevertheless, the relevancy of the conclusions drawn from phenological studies strongly depends on the estimation accuracy of such phenological changes. Many different estimators exist and some have already raised major criticism, although they continue to be used. Therefore, there is a crucial need for an extensive study documenting the behaviour of phenological estimators. 2. Here, we compare the estimation efficiency of 10 phenological estimators: different first appearance dates, mean dates, different percentile dates and a smoothing method based on spline functions using simulated phenological data. Root mean-squared errors and bias of the phenological estimations are calculated in relation to different parameters of the simulated phenological data. 3. Results show that first appearance dates behave as a very inaccurate and biased estimator regarding any phenological data set. Mean dates and estimates calculated using the smoothing method provided in general the most accurate estimates of phenological shifts. They were also the most robust to variation in sample sizes and to imperfect detectability. 4. Our results allow us to warn against the use of first appearance dates in future phenological studies and to recommend using mean dates or smoothing techniques to estimate phenological change of entire distributions. We also provide advice concerning phenological monitoring effort. These recommendations should most importantly apply to studies aiming at comparing phenological variation among sites or among species.},
	language = {en},
	number = {2},
	urldate = {2021-06-23},
	journal = {Methods in Ecology and Evolution},
	author = {Moussus, Jean-Pierre and Julliard, Romain and Jiguet, Frédéric},
	year = {2010},
	note = {\_eprint: https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/j.2041-210X.2010.00020.x},
	keywords = {bird monitoring, climate change, GAM, mean comparison method, optimum shift, phenological shift, phenology, simulation, species shift},
	pages = {140--150},
	annote = {Summary
Compare the estimation efficiency of 10 phenological shift estimators (1st date, 10 first dates, 25 first dates, mean dates, Q10, Q25, Q50, Q75, Q90, GAM based methods) through a simulation. Found best RMSE for mean and GAM methods.
Notes
- GAM method described in Moussus et al. 2009},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/UK2GAG7W/Moussus et al. - 2010 - Featuring 10 phenological estimators using simulat.pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/HPF4BLYR/j.2041-210X.2010.00020.html:text/html},
}

@article{terbraak1986,
	title = {Weighted averaging, logistic regression and the {Gaussian} response model},
	volume = {65},
	issn = {1573-5052},
	url = {https://doi.org/10.1007/BF00032121},
	doi = {10.1007/BF00032121},
	abstract = {The indicator value and ecological amplitude of a species with respect to a quantitative environmental variable can be estimated from data on species occurrence and environment. A simple weighted averaging (WA) method for estimating these parameters is compared by simulation with the more elaborate method of Gaussian logistic regression (GLR), a form of the generalized linear model which fits a Gaussian-like species response curve to presence-absence data. The indicator value and the ecological amplitude are expressed by two parameters of this curve, termed the optimum and the tolerance, respectively. When a species is rare and has a narrow ecological amplitude — or when the distribution of quadrats along the environmental variable is reasonably even over the species' range, and the number of quadrats is small — then WA is shown to approach GLR in efficiency. Otherwise WA may give misleading results. GLR is therefore preferred as a practical method for summarizing species' distributions along environmental gradients. Formulas are given to calculate species optima and tolerances (with their standard errors), and a confidence interval for the optimum from the GLR output of standard statistical packages.},
	language = {en},
	number = {1},
	urldate = {2021-06-23},
	journal = {Vegetatio},
	author = {{ter Braak}, Cajo J. F. and Looman, Caspar W. N.},
	month = jan,
	year = {1986},
	keywords = {bias, environmental gradient, gaussian model, GLM, mean comparison method, model performance, optimum modeling, sampling effort},
	pages = {3--11},
	annote = {Summary
Compare weighted averaging (WA, t.test method) and Gaussian logistic regression (GLR, optimum-GLM) in estimating optimums while playing with the repartition and the number of sites along the environmental gradient. Find that WA can be biased when repartition is uneven. GLR is preferable.},
	file = {Springer Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/PRVBBC3Q/ter Braak et Looman - 1986 - Weighted averaging, logistic regression and the Ga.pdf:application/pdf},
}

@article{taheri2021,
	title = {Improvements in reports of species redistribution under climate change are required},
	volume = {7},
	copyright = {Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).. https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.},
	issn = {2375-2548},
	url = {https://advances.sciencemag.org/content/7/15/eabe1110},
	doi = {10.1126/sciadv.abe1110},
	abstract = {Studies have documented climate change–induced shifts in species distributions but uncertainties associated with data and methods are typically unexplored. We reviewed 240 reports of climate-related species-range shifts and classified them based on three criteria. We ask whether observed distributional shifts are compared against random expectations, whether multicausal factors are examined on equal footing, and whether studies provide sufficient documentation to enable replication. We found that only {\textasciitilde}12.1\% of studies compare distributional shifts across multiple directions, {\textasciitilde}1.6\% distinguish observed patterns from random expectations, and {\textasciitilde}19.66\% examine multicausal factors. Last, {\textasciitilde}75.5\% of studies report sufficient data and results to allow replication. We show that despite gradual improvements over time, there is scope for raising standards in data and methods within reports of climate-change induced shifts in species distribution. Accurate reporting is important because policy responses depend on them. Flawed assessments can fuel criticism and divert scarce resources for biodiversity to competing priorities.
Despite improvements, studies assessing climate change effects on species range shifts fall short on aspects of data and methods.
Despite improvements, studies assessing climate change effects on species range shifts fall short on aspects of data and methods.},
	language = {en},
	number = {15},
	urldate = {2021-06-28},
	journal = {Science Advances},
	author = {Taheri, Shirin and Naimi, Babak and Rahbek, Carsten and Araújo, Miguel B.},
	month = apr,
	year = {2021},
	pmid = {33827813},
	note = {Publisher: American Association for the Advancement of Science
Section: Review},
	keywords = {climate change, leading eadge, range shift, rear edge, reproducibility, review},
	pages = {eabe1110},
	annote = {Summary
They review the literature involving the analysis of multiple species responses to climate change in light of three criteria (i) pattern detection (ii) causality (iii) reproducibility. They advice for (i) testing the "significance" of the shift regarding null hypothesis (ii) studying multiple climatic variables, not just temperature, and (iii) enhance reproducibility.
Notes
- "insects, by far the most specious group in the world, are almost not covered by assessments" on range shifts},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/ZY9CLESP/Taheri et al. - 2021 - Improvements in reports of species redistribution .pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/C6UGCXYK/tab-pdf.html:text/html},
}

@article{hernandez2006,
	title = {The effect of sample size and species characteristics on performance of different species distribution modeling methods},
	volume = {29},
	issn = {1600-0587},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.0906-7590.2006.04700.x},
	doi = {10.1111/j.0906-7590.2006.04700.x},
	abstract = {Species distribution models should provide conservation practioners with estimates of the spatial distributions of species requiring attention. These species are often rare and have limited known occurrences, posing challenges for creating accurate species distribution models. We tested four modeling methods (Bioclim, Domain, GARP, and Maxent) across 18 species with different levels of ecological specialization using six different sample size treatments and three different evaluation measures. Our assessment revealed that Maxent was the most capable of the four modeling methods in producing useful results with sample sizes as small as 5, 10 and 25 occurrences. The other methods compensated reasonably well (Domain and GARP) to poorly (Bioclim) when presented with datasets of small sample sizes. We show that multiple evaluation measures are necessary to determine accuracy of models produced with presence-only data. Further, we found that accuracy of models is greater for species with small geographic ranges and limited environmental tolerance, ecological characteristics of many rare species. Our results indicate that reasonable models can be made for some rare species, a result that should encourage conservationists to add distribution modeling to their toolbox.},
	language = {en},
	number = {5},
	urldate = {2021-06-30},
	journal = {Ecography},
	author = {Hernandez, Pilar A. and Graham, Catherine H. and Master, Lawrence L. and Albert, Deborah L.},
	year = {2006},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.0906-7590.2006.04700.x},
	keywords = {ecological specialization, generalist species, marginality, model accuracy, sampling effort, specialist species, species distribution model},
	pages = {773--785},
	annote = {Summary
Based on ecological data, compare accuracy of four SDMs accross 18 species with different levels of ecological specialization (niche breadth) and marginality (optimum position compared to mean environmental value) using six sample size treatments.
Notes
- Found negative correlation between model accuracy and niche breadth -{\textgreater} more difficult to model generalists
- Found positive correlation between model accuracy and marginality -{\textgreater} "marginal" species easier to model},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/WN54WA3V/Hernandez et al. - 2006 - The effect of sample size and species characterist.pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/JV5YVW6P/j.0906-7590.2006.04700.html:text/html},
}

@article{elith2010,
	title = {The art of modelling range-shifting species},
	volume = {1},
	issn = {2041-210X},
	url = {https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/j.2041-210X.2010.00036.x},
	doi = {10.1111/j.2041-210X.2010.00036.x},
	abstract = {1. Species are shifting their ranges at an unprecedented rate through human transportation and environmental change. Correlative species distribution models (SDMs) are frequently applied for predicting potential future distributions of range-shifting species, despite these models’ assumptions that species are at equilibrium with the environments used to train (fit) the models, and that the training data are representative of conditions to which the models are predicted. Here we explore modelling approaches that aim to minimize extrapolation errors and assess predictions against prior biological knowledge. Our aim was to promote methods appropriate to range-shifting species. 2. We use an invasive species, the cane toad in Australia, as an example, predicting potential distributions under both current and climate change scenarios. We use four SDM methods, and trial weighting schemes and choice of background samples appropriate for species in a state of spread. We also test two methods for including information from a mechanistic model. Throughout, we explore graphical techniques for understanding model behaviour and reliability, including the extent of extrapolation. 3. Predictions varied with modelling method and data treatment, particularly with regard to the use and treatment of absence data. Models that performed similarly under current climatic conditions deviated widely when transferred to a novel climatic scenario. 4. The results highlight problems with using SDMs for extrapolation, and demonstrate the need for methods and tools to understand models and predictions. We have made progress in this direction and have implemented exploratory techniques as new options in the free modelling software, MaxEnt. Our results also show that deliberately controlling the fit of models and integrating information from mechanistic models can enhance the reliability of correlative predictions of species in non-equilibrium and novel settings. 5. Implications. The biodiversity of many regions in the world is experiencing novel threats created by species invasions and climate change. Predictions of future species distributions are required for management, but there are acknowledged problems with many current methods, and relatively few advances in techniques for understanding or overcoming these. The methods presented in this manuscript and made accessible in MaxEnt provide a forward step.},
	language = {en},
	number = {4},
	urldate = {2021-06-30},
	journal = {Methods in Ecology and Evolution},
	author = {Elith, Jane and Kearney, Michael and Phillips, Steven},
	year = {2010},
	note = {\_eprint: https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/j.2041-210X.2010.00036.x},
	keywords = {cane toad, changing correlations, climate change, extrapolation, future climate, invasive species, niche models, novel environments, species distribution model},
	pages = {330--342},
	annote = {Summary
Present exploratory methods to better understand data, model and predictions using SDM for extrapolation (prediction of occurrence in future climate). Case study on an invasive species (= range-shifting species), the cane toad in Australia.},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/3E6JM8CJ/Elith et al. - 2010 - The art of modelling range-shifting species.pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/VDGRL83W/j.2041-210X.2010.00036.html:text/html},
}

@article{thorson2016,
	title = {Model-based inference for estimating shifts in species distribution, area occupied and centre of gravity},
	volume = {7},
	copyright = {© 2016 The Authors. Methods in Ecology and Evolution © 2016 British Ecological Society},
	issn = {2041-210X},
	url = {https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.12567},
	doi = {10.1111/2041-210X.12567},
	abstract = {Changing climate is already impacting the spatial distribution of many taxa, including bees, plants, birds, butterflies and fishes. A common goal is to detect range shifts in response to climate change, including changes in the centre of the population's distribution (the centre of gravity, COG), population boundaries and area occupied. Conventional estimators, such as the abundance-weighted average (AWA) estimator for COG, confound range shifts with changes in the spatial distribution of available survey data and may be biased when the distribution of survey data shifts over time. AWA also does not estimate the standard error of COG in individual years and cannot incorporate data from multiple survey designs. To explicitly account for changes in the spatial distribution of survey effort, we propose an alternative species distribution function (SDF) estimator. The SDF approach involves calculating distribution metrics, including COG, population boundary and area occupied, directly from the predicted species distribution or density function. We illustrate the SDF approach using a spatiotemporal model that is available as an r package. Using simulated data, we confirm that the SDF substantially decreases bias in COG estimates relative to the AWA estimator. We then illustrate the method by analysing data from two data sets spanning 1977–2013 for 18 marine fishes along the U.S. West Coast. In our case study, the SDF estimator shows significant northward shifts for six of 18 species (with southward shifts for only 2), where two species (darkblotched and greenstriped rockfishes) have both a northward shift and a decreased area occupied. Pelagic species (e.g. Pacific hake and spiny dogfish) have more variable distribution than bottom-associated species. We also find substantial differences between AWA and SDF estimates of COG that are likely caused by shifts in sampling distribution (which affect the AWA but not the SDF estimator). We caution that common estimators for range shift can yield inappropriate inference whenever sampling designs have shifted over time. We conclude by suggesting further improvements in model-based approaches to analysing climate impacts, including methods addressing the impact of local and regional temperature changes on species distribution.},
	language = {en},
	number = {8},
	urldate = {2021-06-30},
	journal = {Methods in Ecology and Evolution},
	author = {Thorson, James T. and Pinsky, Malin L. and Ward, Eric J.},
	year = {2016},
	note = {\_eprint: https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/2041-210X.12567},
	keywords = {abundance, abundance-weighted average, age structure, California Current, centre of gravity, climate change, mean comparison method, optimum shift, range shift, simulation, spatiotemporal model, species distribution model, time series},
	pages = {990--1002},
	annote = {Summary
Compare, with both simulation and application study, abundance weighted average (AWA, the common method) with method based on any species distribution function (SDF, the 'new' method) in center of gravity (COG) estimation. Found better estimates with SDF, and caution that AWA could yield to biased results with varying sampling effort.
Notes
- COG {\textless}={\textgreater} optimum \& AWA {\textless}={\textgreater} mean comparison method
- Writing idea: Mean comparison method is to be related to abundance-weighted average which is known to be dependent of sampling effort [@Thorson\_model\_2016]
- "We also recommend further research regarding age- and size-structured species distribution models. [...] Fluctuations in distribution caused by variation in age structure might therefore be confused with climate-driven shifts in distribution, especially for short-lived species with strong cohorts (e.g. Pacific hake) or for long-lived species that rebuild their age structure following a reduction in fishing effort (e.g. summer flounder)."},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/FH8AKM83/Thorson et al. - 2016 - Model-based inference for estimating shifts in spe.pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/S5RQMNYI/2041-210X.html:text/html},
}

@article{urli2014,
	title = {Inferring shifts in tree species distribution using asymmetric distribution curves: a case study in the {Iberian} mountains},
	volume = {25},
	issn = {1654-1103},
	shorttitle = {Inferring shifts in tree species distribution using asymmetric distribution curves},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/jvs.12079},
	doi = {10.1111/jvs.12079},
	abstract = {Questions The objectives of this study were to examine altitudinal shifts in tree species distributions over one decade to quantify the potential for tree migration. Location Spain. Methods We analysed presence–absence data using two successive surveys of the Spanish Forest Inventory in five Fagaceae tree species (two temperate: Fagus sylvatica and Quercus petraea, one sub-Mediterranean: Q. faginea and two Mediterranean: Q. suber and Q. ilex) in two mountain ranges (the Pyrenees and the Iberian system). Half of the fitted altitudinal distributions were skewed and required use of an asymmetric model for unbiased estimates of optimum altitude and changes in the probability of presence along the altitudinal gradient. For each species and mountain range, shifts were considered to have occurred when the difference in optimum altitude was significant or when differences in probability of occurrence between the two surveys demonstrated the occurrence of colonization or extirpation events. Results Overall, depending on species and mountain range, shifts in optimum altitude ranged between −34 m and +181 m. The altitudinal distribution of the Mediterranean species at the core of their latitudinal distribution range presented no sign of change. For the temperate and sub-Mediterranean Fagaceae species, the patterns demonstrated the existence of distribution changes over a 10-yr period. The largest, although not statistically significant, upward shift in optimum altitude was observed for Q. petraea in the Iberian system. More interestingly, its distribution indicated colonization events at higher altitudes. For Q. faginea in the Pyrenees, the shift in optimum altitude was the second largest and statistically significant, and was associated with large extirpation events at the lower altitudes. No evidence of shifts was observed for F. sylvatica. Conclusion This work demonstrates that changes in altitudinal distribution could occur over a 10-yr time period for tree species located at the southern limit of their distribution, such as some temperate and sub-Mediterranean oaks, whereas no movement was detected for Mediterranean oaks in the core of their distribution area.},
	language = {en},
	number = {1},
	urldate = {2021-06-30},
	journal = {Journal of Vegetation Science},
	author = {Urli, Morgane and Delzon, Sylvain and Eyermann, Audrey and Couallier, Vincent and García-Valdés, Raúl and Zavala, Miguel Angel and Porté, Annabel J.},
	year = {2014},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/jvs.12079},
	keywords = {asymmetric response curves, elevational gradient, Fagus, Forest, Model, Optimum, optimum shift, Quercus, species distribution model},
	pages = {147--159},
	annote = {Summary
Study shifts in altitudinal optimums of 5 trees using 3 models (GLM, HOF IV and HOF V). Shifts were computed as differences between optimums of two periods, optimums were derivated from model’s parameters, and so NO uncertainity associated with shift (significativity was assessed regarding confidence intervals of opti coverage). They encourage to use HOF V which allows for skewed distribution.
Notes
- delta method to estimate optimum confidence interval (Coualler et al., 2011)- avoid bootstrap proposed by Maggini et al. (2011) because « with presence-absence data such method overestimate variability (Davison \& Hinkley, 1999) »- species shifts seem to depend on geographic context- species shifts varied among species- They found 50 \% of skewed species distribution- They worked on probability of presence (calculated by altitudinal interval) and not on presence-absence directly},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/X5AUU9MF/Urli et al. - 2014 - Inferring shifts in tree species distribution usin.pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/LEWR2GPW/jvs.html:text/html},
}

@article{freeman2018,
	title = {Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community},
	volume = {115},
	number = {47},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Freeman, Benjamin G. and Scholer, Micah N. and Ruiz-Gutierrez, Viviana and Fitzpatrick, John W.},
	month = nov,
	year = {2018},
	keywords = {climate change, extirpation, mean comparison method, optimum shift, range shift, species loss, T-test, tropical mountain},
	pages = {11982--11987},
	annote = {Summary
Study range and optimum shifts, using t-test, and risk of extirpation in a tropcial bird community. Show declined of some mountaintop species and disappearance of other ones.},
	file = {Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community | PNAS:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/HP5FCMG4/11982.html:text/html;Freeman et al. - 2018 - Climate change causes upslope shifts and mountaint.pdf:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/P4UCA6EJ/Freeman et al. - 2018 - Climate change causes upslope shifts and mountaint.pdf:application/pdf},
}

@article{rumpf2018,
	title = {Range dynamics of mountain plants decrease with elevation},
	volume = {115},
	number = {8},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Rumpf, Sabine B. and Hülber, Karl and Klonner, Günther and Moser, Dietmar and Schütz, Martin and Wessely, Johannes and Willner, Wolfgang and Zimmermann, Niklaus E. and Dullinger, Stefan},
	month = feb,
	year = {2018},
	keywords = {climate change, elevational gradient, optimum shift, range shift, sampling effort, species range},
	pages = {1848--1853},
	annote = {Summary
Compare position of optima, leading edges, rear edges and abundance of 183 mountain plant species between two periods. Found variability in responses, depending on the historical optimum position and some ecological traits.
Notes
- sampling sites are distributed ‘normally’- position of optimums, edges and ranges assessed using kernel density on occurrences (more a descriptive process than a statistical analysis) → no incertitude associated with species specific estimates- species that can occur beyond sampling edges are removed from the analysis},
	file = {Range dynamics of mountain plants decrease with elevation | PNAS:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/IAZVGR8Z/1848.html:text/html;Rumpf et al. - 2018 - Range dynamics of mountain plants decrease with el.pdf:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/S74EYJCP/Rumpf et al. - 2018 - Range dynamics of mountain plants decrease with el.pdf:application/pdf},
}

@article{lembrechts2017,
	title = {Mountain roads shift native and non-native plant species' ranges},
	volume = {40},
	issn = {1600-0587},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ecog.02200},
	doi = {10.1111/ecog.02200},
	abstract = {Roads are known to act as corridors for dispersal of plant species. With their variable microclimate, role as corridors for species movement and reoccurring disturbance events, they show several characteristics that might influence range dynamics of both native and non-native species. Previous research on plant species ranges in mountains however seldom included the effects of roads. To study how ranges of native and non-native species differ between roads and adjacent vegetation, we used a global dataset of plant species composition along mountain roads. We compared average elevation and range width of species, and used generalized linear mixed models (GLMMs) to compile their range optimum and amplitude. We then explored differences between roadside and adjacent plots based on a species’ origin (native vs non-native) and nitrogen and temperature affinity. Most non-native species had on average higher elevational ranges and broader amplitudes in roadsides. Higher optima for non-native species were associated with high nitrogen and temperature affinity. While lowland native species showed patterns comparable to those in non-native species, highland native species had significantly lower elevational ranges in roadsides compared to the adjacent vegetation. We conclude that roadsides indeed change the elevational ranges of a variety of species. These changes are not limited to the expansion of non-native species along mountain roads, but also include both upward and downward changes in ranges of native species. Roadsides may thus facilitate upward range shifts, for instance related to climate change, and they could serve as corridors to facilitate migration of alpine species between adjacent high-elevation areas. We recommend including the effects of mountain roads in species distribution models to fine-tune the predictions of range changes in a warming climate.},
	language = {en},
	number = {3},
	urldate = {2021-06-30},
	journal = {Ecography},
	author = {Lembrechts, Jonas J. and Alexander, Jake M. and Cavieres, Lohengrin A. and Haider, Sylvia and Lenoir, Jonathan and Kueffer, Christoph and McDougall, Keith and Naylor, Bridgett J. and Nuñez, Martín A. and Pauchard, Aníbal and Rew, Lisa J. and Nijs, Ivan and Milbau, Ann},
	year = {2017},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ecog.02200},
	keywords = {ecological niche, elevational gradient, GLMM, optimum shift, species range},
	pages = {353--364},
	annote = {Summary
One part of the paper is about optimum positions and shifts which were calculated using both GLMMs and mean comparions. Also interested in range width estimated with GLMMs},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/JKJIY8F4/Lembrechts et al. - 2017 - Mountain roads shift native and non-native plant s.pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/MX596W5M/ecog.html:text/html},
}

@article{tayleur2015,
	title = {Swedish birds are tracking temperature but not rainfall: evidence from a decade of abundance changes},
	volume = {24},
	issn = {1466-8238},
	shorttitle = {Swedish birds are tracking temperature but not rainfall},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/geb.12308},
	doi = {10.1111/geb.12308},
	abstract = {Aim To quantify avian distribution shifts and the extent of niche tracking in response to changing temperature and precipitation patterns. Location Sweden. Methods We used abundance monitoring data to quantify changes in bird species distributions between two time periods, 2000–02 and 2010–12. First we examined shifts at the level of whole distributions using population centroids in temperature, rainfall, altitude, latitude and longitude. We then characterized shifts in temperature and latitude at different parts of species ranges using species response curves (SRC). We accounted for yearly turnover in abundance and sampling effort, and compared the observed shifts with those expected under perfect niche tracking. Results Most species demonstrated changes in their distributions over the last decade but not all were in response to weather. The degree to which species tracked their climatic niches and the dynamics driving these shifts varied considerably. Only 20\% of species shifted in the direction expected given the temperature changes, while few showed a strong response to rainfall. Most shifts did not fully compensate for changes in temperature. Range changes were most evident at the leading edges, but there was some evidence for retractions of trailing edges. Amongst species that tracked temperature, those with southerly distributions were less successful at tracking changes than those in the north. Main conclusions Swedish birds demonstrated highly dynamic distributions, with many rapid directional shifts occurring over the last decade. However, only a few species kept pace with observed climatic change. Species that did not track their climatic niche may be tolerant to ongoing climatic change or constrained by strong habitat requirements. We demonstrate that measuring range shifts along both environmental and geographic gradients can help disentangle drivers of distribution changes.},
	language = {en},
	number = {7},
	urldate = {2021-06-30},
	journal = {Global Ecology and Biogeography},
	author = {Tayleur, Catherine and Caplat, Paul and Massimino, Dario and Johnston, Alison and Jonzén, Niclas and Smith, Henrik G. and Lindström, Åke},
	year = {2015},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/geb.12308},
	keywords = {abundance, Climate change, climate tracking, ecological niche, GAM, optimum shift, range margins, species distribution model, species distributions},
	pages = {859--872},
	annote = {Summary
Quantify avian distribution shifts associated with changing temperature and precipitation patterns. Use two methods : population centroids in the environmental niche composed by 5 gradients and GAMs as in Maggini et al. (2011)},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/G3G8R3MN/Tayleur et al. - 2015 - Swedish birds are tracking temperature but not rai.pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/J2HYZ6CL/geb.html:text/html},
}

@article{maggini2011,
	title = {Are {Swiss} birds tracking climate change?: {Detecting} elevational shifts using response curve shapes},
	volume = {222},
	issn = {0304-3800},
	shorttitle = {Are {Swiss} birds tracking climate change?},
	url = {https://www.sciencedirect.com/science/article/pii/S0304380010004813},
	doi = {10.1016/j.ecolmodel.2010.09.010},
	abstract = {Climate change is affecting biodiversity worldwide inducing species to either “move, adapt or die”. In this paper we propose a conceptual framework for analysing range shifts, namely a catalogue of the possible patterns of change in the distribution of a species along elevational or other environmental gradients and an improved quantitative methodology to identify and objectively describe these patterns. Patterns are defined in terms of changes occurring at the leading, trailing or both edges of the distribution: (a) leading edge expansion, (b) trailing edge retraction, (c) range expansion, (d) optimum shift, (e) expansion, (f) retraction, and (g) shift. The methodology is based on the modelling of species distributions along a gradient using generalized additive models (GAMs). Separate models are calibrated for two distinct periods of assessment and response curves are compared over five reference points. Changes occurred at these points are formalized into a code that ultimately designates the corresponding change pattern. We tested the proposed methodology using data from the Swiss national common breeding bird survey. The elevational distributions of 95 bird species were modelled for the periods 1999–2002 and 2004–2007 and significant upward shifts (all patterns confounded) were identified for 35\% of the species. Over the same period, an increase in mean temperature was registered for Switzerland. In consideration of the short period covered by the case study, assessed change patterns are considered to correspond to intermediate patterns in an ongoing shifting process. However, similar patterns can be determined by habitat barriers, land use/land cover changes, competition with concurrent or invasive species or different warming rates at different elevations.},
	language = {en},
	number = {1},
	urldate = {2021-06-30},
	journal = {Ecological Modelling},
	author = {Maggini, Ramona and Lehmann, Anthony and Kéry, Marc and Schmid, Hans and Beniston, Martin and Jenni, Lukas and Zbinden, Niklaus},
	month = jan,
	year = {2011},
	keywords = {bootstrap, Breeding birds, Climate change, Elevational shift, environmental gradient, GAM, Leading/trailing edge, optimum shift, range shift, Switzerland},
	pages = {21--32},
	annote = {Summary
Propose a new method based on two GAMs, each applied on one period, and bootstrapping to test for significance in range and optimum shifts between two surveys.
Notes
- bootstrapping questionable and two models used},
	file = {ScienceDirect Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/6MXBEQP3/Maggini et al. - 2011 - Are Swiss birds tracking climate change Detectin.pdf:application/pdf;ScienceDirect Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/CDDH6LQI/S0304380010004813.html:text/html},
}

@article{jamil2014,
	title = {A {Unimodal} {Species} {Response} {Model} {Relating} {Traits} to {Environment} with {Application} to {Phytoplankton} {Communities}},
	volume = {9},
	issn = {1932-6203},
	url = {https://dx.plos.org/10.1371/journal.pone.0097583},
	doi = {10.1371/journal.pone.0097583},
	abstract = {In this paper we attempt to explain observed niche differences among species (i.e. differences in their distribution along environmental gradients) by differences in trait values (e.g. volume) in phytoplankton communities. For this, we propose the trait-modulated Gaussian logistic model in which the niche parameters (optimum, tolerance and maximum) are made linearly dependent on species traits. The model is fitted to data in the Bayesian framework using OpenBUGS (Bayesian inference Using Gibbs Sampling) to identify according to which environmental variables there is niche differentiation among species and traits. We illustrate the method with phytoplankton community data of 203 lakes located within four climate zones and associated measurements on 11 environmental variables and six morphological species traits of 60 species. Temperature and chlorophyll-a (with opposite signs) described well the niche structure of all species. Results showed that about 25\% of the variance in the niche centres with respect to chlorophyll-a were accounted for by traits, whereas niche width and maximum could not be predicted by traits. Volume, mucilage, flagella and siliceous exoskeleton are found to be the most important traits to explain the niche centres. Species were clustered in two groups with different niches structures, group 1 high temperature-low chlorophyll-a species and group 2 low temperature-high chlorophyll-a species. Compared to group 2, species in group 1 had larger volume but lower surface area, had more often flagella but neither mucilage nor siliceous exoskeleton. These results might help in understanding the effect of environmental changes on phytoplankton community. The proposed method, therefore, can also apply to other aquatic or terrestrial communities for which individual traits and environmental conditioning factors are available.},
	language = {en},
	number = {5},
	urldate = {2021-06-30},
	journal = {PLoS ONE},
	author = {Jamil, Tahira and Kruk, Carla and {ter Braak}, Cajo J. F.},
	editor = {Hector, Andrew},
	month = may,
	year = {2014},
	keywords = {bayesian, ecological niche, environmental gradient, GLMM, optimum modeling, species distribution model, species response curves, species traits},
	pages = {e97583},
	annote = {Summary
They propose the trait-modulated Gaussian logistic model in which the niche parameters (optimum, tolerance and maximum) are made linearly dependant on species traits through a case of study about plankton communities varying with 11 environmental covariates. The model is computed through OpenBUGS and the code is available.},
	file = {Jamil et al. - 2014 - A Unimodal Species Response Model Relating Traits .pdf:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/BAY4885T/Jamil et al. - 2014 - A Unimodal Species Response Model Relating Traits .pdf:application/pdf},
}

@article{coudun2005,
	title = {Ecological behaviour of herbaceous forest species along a {pH} gradient: a comparison between oceanic and semicontinental regions in northern {France}},
	volume = {14},
	issn = {1466-8238},
	shorttitle = {Ecological behaviour of herbaceous forest species along a {pH} gradient},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1466-822X.2005.00144.x},
	doi = {10.1111/j.1466-822X.2005.00144.x},
	abstract = {Aim On the basis of 2402 phytoecological relevés, with complete species lists, and real pH measurements resulting from chemical analyses of the top layer of forest soils, this paper compares quantitatively the ecological response of 46 herbaceous forest species along a pH gradient in two regions of northern France. Location The two regions investigated are oceanic north-western France (NW) and semicontinental north-eastern France (NE). Methods For each of the 46 species with more than 50 occurrences in both NW and NE regions, an ecological response curve was computed with simple logistic regression models, and two synthetic numerical values were derived: ecological optimum (OPT) and ecological amplitude (AMP). A comparison of the ecological behaviour of species present in both regions was performed in terms of shift in optimum and/or amplitude. Results All 46 species did reveal a reaction to pH. Our main observation was the relative stability of the behaviour of most species with reference to pH conditions, which is consistent with results from some previous studies. Slight differences regarding the two synthetic parameters (OPT and AMP) were however observed between the NW and NE regions, probably due to a form of competitive release in neutral environments in the NW. Main conclusions Among the main possibilities that could explain a geographical shift in the ecological behaviour of herbaceous species, competitive effects are, we suggest, the most plausible explanation. In the light of former studies, it seems that further analyses of geographical shifts in the ecological behaviour of forest plant species over large areas are needed, such as for example over the European continent. Nomenclature Tutin et al. (2001).},
	language = {en},
	number = {3},
	urldate = {2021-06-30},
	journal = {Global Ecology and Biogeography},
	author = {Coudun, Christophe and Gégout, Jean-Claude},
	year = {2005},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1466-822X.2005.00144.x},
	keywords = {Competitive release, ecological amplitude, ecological niche, ecological optimum, ecological response curves, EcoPlant database, Ellenberg's indicator values, forest herbs, France, GLM, logistic regression, optimum shift, species range},
	pages = {263--270},
	annote = {Summary
Compare ecological optimum and amplitude in tree distribution along a pH gradient. Search for geographical shifts in the ecological behaviour of forest plant species.
Notes
- interest in shift but not in climate change context- used two GLMs, one for each location to compare, derived optimum and look at optimum1 vs opti2},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/VVVWF2CA/Coudun et Gégout - 2005 - Ecological behaviour of herbaceous forest species .pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/L5J963N3/j.1466-822X.2005.00144.html:text/html},
}

@misc{kellner2021,
	title = {{jagsUI}: {A} {Wrapper} {Around} 'rjags' to {Streamline} '{JAGS}' Analyses},
	copyright = {GPL-3},
	shorttitle = {{jagsUI}},
	url = {https://CRAN.R-project.org/package=jagsUI},
	abstract = {A set of wrappers around 'rjags' functions to run Bayesian analyses in 'JAGS' (specifically, via 'libjags'). A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the 'R2WinBUGS'/'R2OpenBUGS' packages to allow easy switching between MCMC samplers.},
	urldate = {2021-06-30},
	author = {Kellner, Ken and Meredith, Mike},
	month = jun,
	year = {2021}
}

@book{gelman2013,
	address = {New York},
	edition = {Third edition},
	title = {Bayesian {Data} {Analysis}},
	language = {en},
	publisher = {Chapman and Hall},
	author = {Gelman, Andrew and Carlin, John B and Stern, Hal S and Dunson, David B and Vehtari, Aki and Rubin, Donald B},
	year = {2013},
	file = {Gelman et al. - Bayesian Data Analysis Third edition (with errors .pdf:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/2FBKKSVA/Gelman et al. - Bayesian Data Analysis Third edition (with errors .pdf:application/pdf},
}

@article{bates2015,
	title = {Distinguishing geographical range shifts from artefacts of detectability and sampling effort},
	volume = {21},
	copyright = {© 2014 John Wiley \& Sons Ltd},
	issn = {1472-4642},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ddi.12263},
	doi = {10.1111/ddi.12263},
	abstract = {Aim The redistribution of species with climate change is well documented. Even so, the relative contribution of species detectability to the variation in measured range shift rates among species is poorly understood. How can true range shifts be discerned from sampling artefacts? Location Australia. Methods We simulate range shifts for species which differ in their abundance for comparison to patterns derived from empirical range shift data from two regional-scale (100s km) empirical studies. We demonstrate the use of spatial occupancy data in a distance-to-edge (DTE) model to assess changes in geographical range edges of fish species within a temperate reef fish community. Results Simulations identified how sampling design can produce relatively larger error in range shift estimates in less abundant species, patterns that correspond with those observed in real data. Application of the DTE model allowed us to estimate the location of the true range edge with high accuracy in common species. In addition, upper confidence bounds for range edge estimates identified species with range edges that have likely shifted in location. Conclusions Simulation and modelling approaches used to quantify the level of confidence that can be placed in observed range shifts are particularly valuable for studies of marine species, where observations are typically few and patchy. Given the observed variability in range shift estimates, the inclusion of confidence bounds on estimates of geographical range edges will advance our capacity to disentangle true distributional change from artefacts of sampling design.},
	language = {en},
	number = {1},
	urldate = {2019-06-12},
	journal = {Diversity and Distributions},
	author = {Bates, Amanda E. and Bird, Tomas J. and Stuart‐Smith, Rick D. and Wernberg, Thomas and Sunday, Jennifer M. and Barrett, Neville S. and Edgar, Graham J. and Frusher, Stewart and Hobday, Alistair J. and Pecl, Gretta T. and Smale, Dan A. and McCarthy, Michael},
	year = {2015},
	keywords = {Climate warming, extreme value statistics, range edge estimation, sampling methodology},
	pages = {13--22},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/E6XQNZZC/Bates et al. - 2015 - Distinguishing geographical range shifts from arte.pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/T22DVBYE/ddi.html:text/html},
}

@article{ovaskainen2011,
	title = {Making more out of sparse data: hierarchical modeling of species communities},
	volume = {92},
	copyright = {© 2011 by the Ecological Society of America},
	issn = {1939-9170},
	shorttitle = {Making more out of sparse data},
	url = {https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/10-1251.1},
	doi = {10.1890/10-1251.1},
	abstract = {Community ecologists and conservation biologists often work with data that are too sparse for achieving reliable inference with species-specific approaches. Here we explore the idea of combining species-specific models into a single hierarchical model. The community component of the model seeks for shared patterns in how the species respond to environmental covariates. We illustrate the modeling framework in the context of logistic regression and presence–absence data, but a similar hierarchical structure could also be used in many other types of applications. We first use simulated data to illustrate that the community component can improve parameterization of species-specific models especially for rare species, for which the data would be too sparse to be informative alone. We then apply the community model to real data on 500 diatom species to show that it has much greater predictive power than a collection of independent species-specific models. We use the modeling approach to show that roughly one-third of distance decay in community similarity can be explained by two variables characterizing water quality, rare species typically preferring nutrient-poor waters with high pH, and common species showing a more general pattern of resource use.},
	language = {en},
	number = {2},
	urldate = {2019-05-09},
	journal = {Ecology},
	author = {Ovaskainen, Otso and Soininen, Janne},
	year = {2011},
	keywords = {Bayesian inference, diatoms, hierarchical modeling, predictive power, species community, species distribution model, statistical modeling},
	pages = {289--295},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/A2S8N6TL/Ovaskainen et Soininen - 2011 - Making more out of sparse data hierarchical model.pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/UI585M2Y/10-1251.html:text/html},
}

@article{dorazio2005,
	title = {Estimating {Size} and {Composition} of {Biological} {Communities} by {Modeling} the {Occurrence} of {Species}},
	volume = {100},
	issn = {0162-1459, 1537-274X},
	url = {http://www.tandfonline.com/doi/abs/10.1198/016214505000000015},
	doi = {10.1198/016214505000000015},
	abstract = {We develop a model that uses repeated observations of a biological community to estimate the number and composition of species in the community. Estimators of community-level attributes are constructed from model-based estimators of occurrence of individual species that incorporate imperfect detection of individuals. Data from the North American Breeding Bird Survey are analyzed to illustrate the variety of ecologically-important quantities that are easily constructed and estimated using our model-based estimators of species occurrence. In particular, we compute site-speciﬁc estimates of species richness that honor classical notions of species-area relationships. We suggest extensions of our model to estimate maps of occurrence of individual species and to compute inferences related to the temporal and spatial dynamics of biological communities.},
	language = {en},
	number = {470},
	urldate = {2019-04-29},
	journal = {Journal of the American Statistical Association},
	author = {Dorazio, Robert M and Royle, J. Andrew},
	month = jun,
	year = {2005},
	pages = {389--398},
	file = {Dorazio et Royle - 2005 - Estimating Size and Composition of Biological Comm.pdf:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/5WWFK5UR/Dorazio et Royle - 2005 - Estimating Size and Composition of Biological Comm.pdf:application/pdf},
}

@Manual{r2018,
	organization = {R Foundation for   Statistical Computing},
	address = {Vienna, Austria},
	title = {R: A language and environment for statistical computing},
	url = {https://www.R-project.org/},
	author = {{R Core Team}},
	year = {2018},
}

@article{michaelis2017,
	title = {Biased niches – {Species} response curves and niche attributes from {Huisman}-{Olff}-{Fresco} models change with differing species prevalence and frequency},
	volume = {12},
	issn = {1932-6203},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0183152},
	doi = {10.1371/journal.pone.0183152},
	abstract = {The study aimed to examine the effects of different numbers of presences and frequencies (proportions) of occurrences of species in a plot data set of forest vegetation on the species response curves and their niche attributes, based on Huisman-Olff-Fresco models (HOF). We modeled responses of 72 to 105 herbaceous forest species along a pH gradient under 14 different random sampling scenarios by varying the number of presences and absences used for model fitting. Mean niche attributes were calculated from 100 repetitive runs for each scenario and species. Re-prediction success of HOF models among the repetitive runs was highest when the total number of plots was high and the frequency of occurrences was low. With low plot numbers and high frequencies, less complicated model types (no response or monotonically increasing/decreasing responses) predominate. Measures of species niche boundaries (limits \& borders) and niche width were strongly influenced by changes in sampling characteristics. With an increasing number of presences and an increasing frequency, limits and borders shifted to more extreme values, leading to wider niches. In contrast, species optima showed almost no change between the scenarios. Thus, the detected ecological response of a species often depends on the size of the data set and the relation between presences and absences of a species. In general, high data quantities are required for reliable response curve modeling with HOF models, which prevents the assessment of the responses of many rare species. To avoid undesired bias by differing sampling characteristics when comparing niches between different species or between data sets, the data basis used for model fitting should be adjusted according to the niche attribute in question, for example by keeping the frequency of the species constant.},
	language = {en},
	number = {8},
	urldate = {2021-09-21},
	journal = {PLOS ONE},
	author = {Michaelis, Jana and Diekmann, Martin R.},
	month = aug,
	year = {2017},
	note = {Publisher: Public Library of Science},
	keywords = {Species diversity, species distribution model, Ecological niches, niche width, species response curves, Animal behavior, Curve fitting, Forests, Paleoecology, Scientists, Soil pH, species tolerance, HOF models, model complexity},
	pages = {e0183152},
	annote = {Summary
Shows that numbers of presences and frequencies of occurrences of species affect the species response curves and their niche attributes based on Huisman-Olff-Fresco models (HOF).Shows that flexible models (such as HOF models) can attribute different shapes of species response curves because of the structure of the data (nb of presences and frequencies) and not because of the ecological response. Maybe models that follow ecological theories should be preferred to flexible models.
Notes
- two kinds of species tolerances are defined : fixed tolerance = width of the curve when the occupancy probability reach 0.05 / relative tolerance = central borders following (Heegaard, 2002) → values of x when the probability reach pmax*exp(-0.5)},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/85Z3WU22/Michaelis et Diekmann - 2017 - Biased niches – Species response curves and niche .pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/LAWATJQ3/article.html:text/html},
}

@article{plummer2003,
	title = {{JAGS}: {A} program for analysis of {Bayesian} graphical models using {Gibbs} sampling},
	abstract = {JAGS is a program for Bayesian Graphical modelling which aims for compatibility with Classic BUGS. The program could eventually be developed as an R package. This article explains the motivations for this program, brieﬂy describes the architecture and then discusses some ideas for a vectorized form of the BUGS language.},
	language = {en},
	journal = {Working Papers},
	author = {Plummer, Martyn},
	year = {2003},
	pages = {8},
	file = {Plummer - 2003 - JAGS A program for analysis of Bayesian graphical.pdf:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/XN5BQA2Z/Plummer - 2003 - JAGS A program for analysis of Bayesian graphical.pdf:application/pdf;Plummer - 2003 - JAGS A program for analysis of Bayesian graphical.pdf:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/B87K3DY8/Plummer - 2003 - JAGS A program for analysis of Bayesian graphical.pdf:application/pdf},
}

@article{vitasse2021,
	author = {Vitasse, Yann and Ursenbacher, Sylvain and Klein, Geoffrey and Bohnenstengel, Thierry and Chittaro, Yannick and Delestrade, Anne and Monnerat, Christian and Rebetez, Martine and Rixen, Christian and Strebel, Nicolas and Schmidt, Benedikt R. and Wipf, Sonja and Wohlgemuth, Thomas and Yoccoz, Nigel Gilles and Lenoir, Jonathan},
title = {Phenological and elevational shifts of plants, animals and fungi under climate change in the European Alps},
journal = {Biological Reviews},
volume = {96},
number = {5},
pages = {1816-1835},
keywords = {biodiversity redistribution, climate change velocity, disequilibrium dynamics, global warming, migration, mountain ecosystems, phenological mismatches, upslope shift},
doi = {https://doi.org/10.1111/brv.12727},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/brv.12727},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/brv.12727},
abstract = {ABSTRACT Mountain areas are biodiversity hotspots and provide a multitude of ecosystem services of irreplaceable socio-economic value. In the European Alps, air temperature has increased at a rate of about 0.36°C decade−1 since 1970, leading to glacier retreat and significant snowpack reduction. Due to these rapid environmental changes, this mountainous region is undergoing marked changes in spring phenology and elevational distribution of animals, plants and fungi. Long-term monitoring in the European Alps offers an excellent natural laboratory to synthetize climate-related changes in spring phenology and elevational distribution for a large array of taxonomic groups. This review assesses the climatic changes that have occurred across the European Alps during recent decades, spring phenological changes and upslope shifts of plants, animals and fungi from evidence in published papers and previously unpublished data. Our review provides evidence that spring phenology has been shifting earlier during the past four decades and distribution ranges show an upwards trend for most of the taxonomic groups for which there are sufficient data. The first observed activity of reptiles and terrestrial insects (e.g. butterflies) in spring has shifted significantly earlier, at an average rate of −5.7 and −6.0 days decade−1, respectively. By contrast, the first observed spring activity of semi-aquatic insects (e.g. dragonflies and damselflies) and amphibians, as well as the singing activity or laying dates of resident birds, show smaller non-significant trends ranging from −1.0 to +1.3 days decade−1. Leaf-out and flowering of woody and herbaceous plants showed intermediate trends with mean values of −2.4 and −2.8 days decade−1, respectively. Regarding species distribution, plants, animals and fungi (N = 2133 species) shifted the elevation of maximum abundance (optimum elevation) upslope at a similar pace (on average between +18 and +25 m decade−1) but with substantial differences among taxa. For example, the optimum elevation shifted upward by +36.2 m decade−1 for terrestrial insects and +32.7 m decade−1 for woody plants, whereas it was estimated to range between −1.0 and +11 m decade−1 for semi-aquatic insects, ferns, birds and wood-decaying fungi. The upper range limit (leading edge) of most species also shifted upslope with a rate clearly higher for animals (from +47 to +91 m decade−1) than for plants (from +17 to +40 m decade−1), except for semi-aquatic insects (−4.7 m decade−1). Although regional land-use changes could partly explain some trends, the consistent upward shift found in almost all taxa all over the Alps is likely reflecting the strong warming and the receding of snow cover that has taken place across the European Alps over recent decades. However, with the possible exception of terrestrial insects, the upward shift of organisms seems currently too slow to track the pace of isotherm shifts induced by climate warming, estimated at about +62 to +71 m decade−1 since 1970. In the light of these results, species interactions are likely to change over multiple trophic levels through phenological and spatial mismatches. This nascent research field deserves greater attention to allow us to anticipate structural and functional changes better at the ecosystem level.},
year = {2021}
}

@article{beissinger2021,
	author = {Beissinger, Steven R. and Riddell, Eric A.},
title = {Why Are Species’ Traits Weak Predictors of Range Shifts?},
journal = {Annual Review of Ecology, Evolution, and Systematics},
volume = {52},
number = {1},
pages = {47-66},
year = {2021},
doi = {10.1146/annurev-ecolsys-012021-092849},

URL = {
        https://doi.org/10.1146/annurev-ecolsys-012021-092849

},
eprint = {
        https://doi.org/10.1146/annurev-ecolsys-012021-092849

}
,
    abstract = { We examine the evidence linking species’ traits to contemporary range shifts and find they are poor predictors of range shifts that have occurred over decades to a century. We then discuss reasons for the poor performance of traits for describing interspecific variation in range shifts from two perspectives: (a) factors associated with species’ traits that degrade range-shift signals stemming from the measures used for species’ traits, traits that are typically not analyzed, and the influence of phylogeny on range-shift potential and (b) issues in quantifying range shifts and relating them to species’ traits due to imperfect detection of species, differences in the responses of altitudinal and latitudinal ranges, and emphasis on testing linear relationships between traits and range shifts instead of nonlinear responses. Improving trait-based approaches requires a recognition that traits within individuals interact in unexpected ways and that different combinations of traits may be functionally equivalent. }
}

@article{citores2020,
	title = {Modelling species presence–absence in the ecological niche theory framework using shape-constrained generalized additive models},
	volume = {418},
	issn = {0304-3800},
	url = {https://www.sciencedirect.com/science/article/pii/S030438001930434X},
	doi = {10.1016/j.ecolmodel.2019.108926},
	abstract = {According to ecological niche theory, species response curves are unimodal with respect to environmental gradients. A variety of statistical methods have been developed for species distribution modelling. A general problem with most of these habitat modelling approaches is that the estimated response curves can display biologically implausible shapes which do not respect ecological niche theory. This work proposes using shape-constrained generalized additive models (SC-GAMs) to build species distribution models under the ecological niche theory framework, imposing concavity constraints in the linear predictor scale. Based on a simulation study and a real data application, we compared performance with respect to other regression models without shape-constraints (such as standard GLMs and GAMs with varying degrees of freedom) and also to models based on so-called “Plateau” climate-envelopes. The imposition of concavity for response curves resulted in a good balance between the goodness of fit (GOF) and agreement with ecological niche theory. The approach has been applied to fit distribution models for three fish species given several environmental variables.},
	language = {en},
	urldate = {2021-09-28},
	journal = {Ecological Modelling},
	author = {Citores, L. and Ibaibarriaga, L. and Lee, D. -J. and Brewer, M. J. and Santos, M. and Chust, G.},
	month = feb,
	year = {2020},
	keywords = {ecological niche, Ecological niche theory, GAM, GAMs, GLM, GLMs, Plateau method, Shape-constrained GAMs, species distribution model, Species distribution models, species response curves, unimodal response},
	pages = {108926},
	annote = {Summary
Propose a method based on GAM but with constraint that forces an unimodal shape to model species distribution along environmental gradient, following niche theory which assumes unimodal responses. Test the method with simulation, compare it to ‘basic’ GAM, GLM and plateau method.},
	file = {Citores et al. - 2020 - Modelling species presence–absence in the ecologic.pdf:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/F98CZKS5/Citores et al. - 2020 - Modelling species presence–absence in the ecologic.pdf:application/pdf;ScienceDirect Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/MRIYARNZ/S030438001930434X.html:text/html},
}

@article{bell2016,
	title = {On the dangers of model complexity without ecological justification in species distribution modeling},
	volume = {330},
	issn = {0304-3800},
	url = {https://www.sciencedirect.com/science/article/pii/S0304380016300783},
	doi = {10.1016/j.ecolmodel.2016.03.012},
	abstract = {Although biogeographic patterns are the product of complex ecological processes, the increasing complexity of correlative species distribution models (SDMs) is not always motivated by ecological theory, but by model fit. The validity of model projections, such as shifts in a species’ climatic niche, becomes questionable particularly during extrapolations, such as for future no-analog climate conditions. To examine the effects of model complexity on SDM predictive performance, we fit statistical models of varying complexity to simulated species occurrence data arising from data-generating processes that assume differing degrees of distributional symmetry in environmental space, interaction effects, and coverage in climate space. Mismatches between data-generating processes and statistical models (i.e., different functional forms) led to poor predictive performance when extrapolating to new climate-space and greater variation in extrapolated predictions for overly complex models. In contrast, performance issues were not apparent when using independent evaluation data from the training region. These results draw into question the use of highly flexible models for prediction without ecological justification.},
	language = {en},
	urldate = {2021-09-28},
	journal = {Ecological Modelling},
	author = {Bell, David M. and Schlaepfer, Daniel R.},
	month = jun,
	year = {2016},
	keywords = {Extrapolation, Model fitting, Prediction, Species distribution modeling, Transferability},
	pages = {50--59},
	file = {ScienceDirect Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/W8RPSEWY/S0304380016300783.html:text/html},
}

@article{merow2014,
	title = {What do we gain from simplicity versus complexity in species distribution models?},
	volume = {37},
	issn = {1600-0587},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/ecog.00845},
	doi = {10.1111/ecog.00845},
	abstract = {Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence–environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence–environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building ‘under fit’ models, having insufficient flexibility to describe observed occurrence–environment relationships, we risk misunderstanding the factors shaping species distributions. By building ‘over fit’ models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.},
	language = {en},
	number = {12},
	urldate = {2021-09-30},
	journal = {Ecography},
	author = {Merow, Cory and Smith, Mathew J. and Edwards Jr, Thomas C. and Guisan, Antoine and McMahon, Sean M. and Normand, Signe and Thuiller, Wilfried and Wüest, Rafael O. and Zimmermann, Niklaus E. and Elith, Jane},
	year = {2014},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/ecog.00845},
	pages = {1267--1281},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/WMWDCSWB/Merow et al. - 2014 - What do we gain from simplicity versus complexity .pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/45XG8GYR/ecog.html:text/html},
}

@book{mcelreath2016,
	address = {Boca Raton},
	title = {Statistical {Rethinking}: {A} {Bayesian} {Course} with {Examples} in {R} and {Stan}},
	isbn = {978-1-315-37249-5},
	shorttitle = {Statistical {Rethinking}},
	abstract = {Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.

The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.

By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.
Web ResourceThe book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.},
	publisher = {Chapman and Hall/CRC},
	author = {McElreath, Richard},
	month = dec,
	year = {2016},
	doi = {10.1201/9781315372495},
}

@article{oksanen2002,
	title = {Continuum theory revisited: what shape are species responses along ecological gradients?},
	volume = {157},
	issn = {0304-3800},
	shorttitle = {Continuum theory revisited},
	url = {https://www.sciencedirect.com/science/article/pii/S0304380002001904},
	doi = {10.1016/S0304-3800(02)00190-4},
	abstract = {The shape of species’ responses along ecological gradients has important implications for both continuum theory and community analysis. Most current theories and analytical models in community ecology assume that responses are unimodal and symmetric. However, interactions between species and extreme environmental stress may cause skewed or non-unimodal responses. To date, statistical tools for evaluating response shapes have been either inappropriate, inefficient or biased. Using a data set on vascular plant distributions along an elevation gradient, we show that Huisman–Olff–Fresco (HOF) models are an effective method for this purpose, allowing models of various forms (skewed, symmetric, plateau, monotonic) to be tested for adequacy. HOF modeling was compared with alternative methods for response fitting, including Gaussian responses as Generalized linear model (GLMs), Generalized Additive Models (GAM) and Beta Functions with fixed or estimated endpoints. In our data set, skewed and plateau responses are less common than symmetric ones. Less than half of the species have skewed or plateau responses that can not be adequately modeled by Gaussian models. We show that Beta function models with fixed endpoints are biased, confounding skewness and the location of the mode and should not be used to test response shapes. Beta models with estimated endpoints are fairly consistent with other models. GAM's cannot provide clear tests of skewness or kurtosis of response curves, though we show that GAM's, in general, confirmed the shapes chosen by HOF modeling. We provide free software for fitting HOF models and encourage further applications to community data collected along different types of ecological gradients.},
	language = {en},
	number = {2},
	urldate = {2021-10-21},
	journal = {Ecological Modelling},
	author = {Oksanen, Jari and Minchin, Peter R},
	month = nov,
	year = {2002},
	keywords = {Beta response function, Gaussian response function, Gradient analysis},
	pages = {119--129},
	file = {Oksanen et Minchin - 2002 - Continuum theory revisited what shape are species.pdf:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/JI6L6W7T/Oksanen et Minchin - 2002 - Continuum theory revisited what shape are species.pdf:application/pdf;ScienceDirect Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/ARZGHC8N/S0304380002001904.html:text/html},
}

@article{parker2016,
	title = {Transparency in {Ecology} and {Evolution}: {Real} {Problems}, {Real} {Solutions}},
	volume = {31},
	issn = {0169-5347},
	shorttitle = {Transparency in {Ecology} and {Evolution}},
	url = {https://www.sciencedirect.com/science/article/pii/S0169534716300957},
	doi = {10.1016/j.tree.2016.07.002},
	abstract = {To make progress scientists need to know what other researchers have found and how they found it. However, transparency is often insufficient across much of ecology and evolution. Researchers often fail to report results and methods in detail sufficient to permit interpretation and meta-analysis, and many results go entirely unreported. Further, these unreported results are often a biased subset. Thus the conclusions we can draw from the published literature are themselves often biased and sometimes might be entirely incorrect. Fortunately there is a movement across empirical disciplines, and now within ecology and evolution, to shape editorial policies to better promote transparency. This can be done by either requiring more disclosure by scientists or by developing incentives to encourage disclosure.},
	language = {en},
	number = {9},
	urldate = {2021-10-21},
	journal = {Trends in Ecology \& Evolution},
	author = {Parker, Timothy H. and Forstmeier, Wolfgang and Koricheva, Julia and Fidler, Fiona and Hadfield, Jarrod D. and Chee, Yung En and Kelly, Clint D. and Gurevitch, Jessica and Nakagawa, Shinichi},
	month = sep,
	year = {2016},
	keywords = {-hacking, confirmation bias, inflated effect size, preregistration, replication, selective reporting},
	pages = {711--719},
	file = {ScienceDirect Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/8X73QKHM/S0169534716300957.html:text/html;Version soumise:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/QAQ8C7F2/Parker et al. - 2016 - Transparency in Ecology and Evolution Real Proble.pdf:application/pdf},
}

@article{felde2012,
	title = {Upward shift in elevational plant species ranges in {Sikkilsdalen}, central {Norway}},
	volume = {35},
	issn = {1600-0587},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1600-0587.2011.07057.x},
	doi = {10.1111/j.1600-0587.2011.07057.x},
	abstract = {Phytosociological studies can be an important tool to detect temporal vegetation changes in response to global climate change. In this study, we present the results of a resurvey of a plot-based phytosociological study from Sikkilsdalen, central Norway, originally executed between 1922 and 1932. By using a detailed phytosociological study we are able to investigate several aspects of elevational shifts in species ranges. Here we tested for upward and downward shifts in observed upper and lower distribution limits of species, as well as changes in species optima along an elevational gradient, and related the observed range shifts to species traits that could explain the observed trends. More species shifted upwards than downwards, independently of whether we were investigating shifts in species’ upper or lower distribution ranges or in species optima. However, shifts in species upper range margins changed independently of their lower range margins. Linking different species traits to the magnitude of shifts we found that species with a higher preference for prolonged snow cover shifted upwards more in their upper elevational limits and in their optima than species that prefer a shorter snow cover, whereas no species traits were correlated with the magnitude of changes in lower limits. The observed change in species ranges concord both with studies on other mountains in the region and with studies from other alpine areas. Furthermore, our study indicates that different factors are influencing species ranges at the upper and lower range limits. Increased precipitation rates and increased temperatures are considered the most important factors for the observed changes, probably mainly through altering the pattern in snow cover dynamics in the area.},
	language = {en},
	number = {10},
	urldate = {2022-04-25},
	journal = {Ecography},
	author = {Felde, Vivian Astrup and Kapfer, Jutta and Grytnes, John-Arvid},
	year = {2012},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1600-0587.2011.07057.x},
	keywords = {edge species removed, GLM, optimum shift, post-hoc tests, species traits},
	pages = {922--932},
	file = {Full Text PDF:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/6QFBFCNU/Felde et al. - 2012 - Upward shift in elevational plant species ranges i.pdf:application/pdf;Snapshot:/home/bmourgui/snap/zotero-snap/common/Zotero/storage/7QRG37UD/j.1600-0587.2011.07057.html:text/html},
}

@article{jansen2013,
  title={How to model species responses along ecological gradients--H uisman--O lff--F resco Models Revisited},
  author={Jansen, Florian and Oksanen, Jari},
  journal={Journal of Vegetation Science},
  volume={24},
  number={6},
  pages={1108--1117},
  year={2013},
  publisher={Wiley Online Library}
}

@article{huisman1993,
  title={A hierarchical set of models for species response analysis},
  author={Huisman, J and Olff, Hl and Fresco, LFM},
  journal={Journal of Vegetation Science},
  volume={4},
  number={1},
  pages={37--46},
  year={1993},
  publisher={Wiley Online Library}
}

@article{wilson2005,
  title={Changes to the elevational limits and extent of species ranges associated with climate change},
  author={Wilson, Robert J and Guti{\'e}rrez, David and Guti{\'e}rrez, Javier and Mart{\'i}nez, David and Agudo, Rosa and Monserrat, V{\'\i}ctor J},
  journal={Ecology letters},
  volume={8},
  number={11},
  pages={1138--1146},
  year={2005},
  publisher={Wiley Online Library}
}

@article{zipkin2009,
	title = {Impacts of forest fragmentation on species richness: a hierarchical approach to community modelling},
	volume = {46},
	copyright = {© 2009 The Authors. Journal compilation © 2009 British Ecological Society},
	issn = {1365-2664},
	shorttitle = {Impacts of forest fragmentation on species richness},
	url = {https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-2664.2009.01664.x},
	doi = {10.1111/j.1365-2664.2009.01664.x},
	abstract = {1. Species richness is often used as a tool for prioritizing conservation action. One method for predicting richness and other summaries of community structure is to develop species-specific models of occurrence probability based on habitat or landscape characteristics. However, this approach can be challenging for rare or elusive species for which survey data are often sparse. 2. Recent developments have allowed for improved inference about community structure based on species-specific models of occurrence probability, integrated within a hierarchical modelling framework. This framework offers advantages to inference about species richness over typical approaches by accounting for both species-level effects and the aggregated effects of landscape composition on a community as a whole, thus leading to increased precision in estimates of species richness by improving occupancy estimates for all species, including those that were observed infrequently. 3. We developed a hierarchical model to assess the community response of breeding birds in the Hudson River Valley, New York, to habitat fragmentation and analysed the model using a Bayesian approach. 4. The model was designed to estimate species-specific occurrence and the effects of fragment area and edge (as measured through the perimeter and the perimeter/area ratio, P/A), while accounting for imperfect detection of species. 5. We used the fitted model to make predictions of species richness within forest fragments of variable morphology. The model revealed that species richness of the observed bird community was maximized in small forest fragments with a high P/A. However, the number of forest interior species, a subset of the community with high conservation value, was maximized in large fragments with low P/A. 6. Synthesis and applications. Our results demonstrate the importance of understanding the responses of both individual, and groups of species, to environmental heterogeneity while illustrating the utility of hierarchical models for inference about species richness for conservation. This framework can be used to investigate the impacts of land-use change and fragmentation on species or assemblage richness, and to further understand trade-offs in species-specific occupancy probabilities associated with landscape variability.},
	language = {en},
	number = {4},
	urldate = {2019-05-09},
	journal = {Journal of Applied Ecology},
	author = {Zipkin, Elise F. and DeWan, Amielle and Royle, J. Andrew},
	year = {2009},
	keywords = {imperfect detection, biodiversity, species richness, fragmentation, hierarchical modelling, occupancy modelling},
	pages = {815--822},
}

@article{conn2018,
author = {Conn, Paul B. and Johnson, Devin S. and Williams, Perry J. and Melin, Sharon R. and Hooten, Mevin B.},
title = {A guide to Bayesian model checking for ecologists},
journal = {Ecological Monographs},
volume = {88},
number = {4},
pages = {526-542},
keywords = {Bayesian model checking, Bayesian P value, goodness-of-fit, hierarchical model, model diagnostics, posterior checks},
doi = {https://doi.org/10.1002/ecm.1314},
url = {https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/ecm.1314},
eprint = {https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecm.1314},
abstract = {Abstract Checking that models adequately represent data is an essential component of applied statistical inference. Ecologists increasingly use hierarchical Bayesian statistical models in their research. The appeal of this modeling paradigm is undeniable, as researchers can build and fit models that embody complex ecological processes while simultaneously accounting for observation error. However, ecologists tend to be less focused on checking model assumptions and assessing potential lack of fit when applying Bayesian methods than when applying more traditional modes of inference such as maximum likelihood. There are also multiple ways of assessing the fit of Bayesian models, each of which has strengths and weaknesses. For instance, Bayesian P values are relatively easy to compute, but are well known to be conservative, producing P values biased toward 0.5. Alternatively, lesser known approaches to model checking, such as prior predictive checks, cross-validation probability integral transforms, and pivot discrepancy measures may produce more accurate characterizations of goodness-of-fit but are not as well known to ecologists. In addition, a suite of visual and targeted diagnostics can be used to examine violations of different model assumptions and lack of fit at different levels of the modeling hierarchy, and to check for residual temporal or spatial autocorrelation. In this review, we synthesize existing literature to guide ecologists through the many available options for Bayesian model checking. We illustrate methods and procedures with several ecological case studies including (1) analysis of simulated spatiotemporal count data, (2) N-mixture models for estimating abundance of sea otters from an aircraft, and (3) hidden Markov modeling to describe attendance patterns of California sea lion mothers on a rookery. We find that commonly used procedures based on posterior predictive P values detect extreme model inadequacy, but often do not detect more subtle cases of lack of fit. Tests based on cross-validation and pivot discrepancy measures (including the “sampled predictive P value”) appear to be better suited to model checking and to have better overall statistical performance. We conclude that model checking is necessary to ensure that scientific inference is well founded. As an essential component of scientific discovery, it should accompany most Bayesian analyses presented in the literature.},
year = {2018}
}

@article{lenoir2008,
  title={A significant upward shift in plant species optimum elevation during the 20th century},
  author={Lenoir, Jonathan and G{\'e}gout, Jean-Claude and Marquet, Pablo A and de Ruffray, Patrice and Brisse, Hhttps},
  journal={Science},
  volume={320},
  number={5884},
  pages={1768--1771},
  year={2008},
  publisher={American Association for the Advancement of Science}
}

@article{pecl2017,
  title={Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being},
  author={Pecl, Gretta T and Ara{\'u}jo, Miguel B and Bell, Johann D and Blanchard, Julia and Bonebrake, Timothy C and Chen, I-Ching and Clark, Timothy D and Colwell, Robert K and Danielsen, Finn and Eveng{\aa}rd, Birgitta and others},
  journal={Science},
  volume={355},
  number={6332},
  pages={eaai9214},
  year={2017},
  publisher={American Association for the Advancement of Science}
}

@article{lemoine2019,
author = {Lemoine, Nathan P.},
title = {Moving beyond noninformative priors: why and how to choose weakly informative priors in Bayesian analyses},
journal = {Oikos},
volume = {128},
number = {7},
pages = {912-928},
keywords = {Bayesian statistics, frequentist statistics, Markov chain Monte Carlo, vague priors},
doi = {https://doi.org/10.1111/oik.05985},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/oik.05985},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/oik.05985},
abstract = {Throughout the last two decades, Bayesian statistical methods have proliferated throughout ecology and evolution. Numerous previous references established both philosophical and computational guidelines for implementing Bayesian methods. However, protocols for incorporating prior information, the defining characteristic of Bayesian philosophy, are nearly nonexistent in the ecological literature. Here, I hope to encourage the use of weakly informative priors in ecology and evolution by providing a ‘consumer's guide’ to weakly informative priors. The first section outlines three reasons why ecologists should abandon noninformative priors: 1) common flat priors are not always noninformative, 2) noninformative priors provide the same result as simpler frequentist methods, and 3) noninformative priors suffer from the same high type I and type M error rates as frequentist methods. The second section provides a guide for implementing informative priors, wherein I detail convenient ‘reference’ prior distributions for common statistical models (i.e. regression, ANOVA, hierarchical models). I then use simulations to visually demonstrate how informative priors influence posterior parameter estimates. With the guidelines provided here, I hope to encourage the use of weakly informative priors for Bayesian analyses in ecology. Ecologists can and should debate the appropriate form of prior information, but should consider weakly informative priors as the new ‘default’ prior for any Bayesian model.},
year = {2019}
}

@article{rydgren2003,
 ISSN = {11009233, 16541103},
 URL = {http://www.jstor.org/stable/3236984},
 abstract = {Vegetation science has relied on untested paradigms relating to the shape of species response curves along environmental gradients. To advance in this field, we used the HOF approach to model response curves for 112 plant species along six environmental gradients and three ecoclines (as represented by DCA ordination axes) in SE Norwegian swamp forests. Response curve properties were summarized in three binary response variables: (1) model unimodal or monotonous (determinate) vs. indeterminate; (2) for determinate models, unimodal vs. monotonous and (3) for unimodal models, skewed vs. symmetric. We used logistic regression to test the influence, singly and jointly, of seven predictor variables on each of three response variables. Predictor variables included gradient type (environmental or ecocline) and length (compositional turnover); species category (vascular plant, moss, Sphagnum or hepatic), species frequency and richness, tolerance (the fraction of the gradient along which the species occurs) and position of species along each gradient. The probability for fitting a determinate model increased as the main occurrence of species approached gradient extremes and with increasing species tolerance and frequency and gradient length. Appearance of unimodal models was favoured by low species tolerance and disfavoured by closeness of species to gradient extremes. Appearance of skewed models was weakly related to predictors but was slightly favoured by species optima near gradient extremes. Contrary to the results of previous studies, species category, gradient type and variation in species richness along gradients did not contribute independently to model prediction. The overall best predictors of response curve shape were position along the gradient (relative to extremes) and tolerance; the latter also expressing gradient length in units of compositional turnover. This helps predicting species responses to gradients from gradient specific species properties. The low proportion of skewed response curves and the large variation of species response curves along all gradients indicate that skewed response curves is a smaller problem for the performance of ordination methods than often claimed. We find no evidence that DCA ordination increases the unimodality, or symmetry, of species response curves more than expected from the higher compositional turnover along ordination axes. Thus ordination axes may be appropriate proxies for ecoclines, applicable for use in species response modelling.},
 author = {Knut Rydgren and Rune Halvorsen Økland and Tonje Økland},
 journal = {Journal of Vegetation Science},
 number = {6},
 pages = {869--880},
 publisher = {Wiley},
 title = {Species Response Curves along Environmental Gradients. A Case Study from SE Norwegian Swamp Forests},
 urldate = {2022-08-02},
 volume = {14},
 year = {2003}
}

@article{heegaard2002,
title = {The outer border and central border for species–environmental relationships estimated by non-parametric generalised additive models},
journal = {Ecological Modelling},
volume = {157},
number = {2},
pages = {131-139},
year = {2002},
issn = {0304-3800},
doi = {https://doi.org/10.1016/S0304-3800(02)00191-6},
url = {https://www.sciencedirect.com/science/article/pii/S0304380002001916},
author = {Einar Heegaard},
keywords = {Species–environmental relationship, GAM, GLM, Limit, Main distribution, Range, Tolerance},
abstract = {In modelling species–environmental relationships, non-parametric generalised additive modelling (GAM) regression has become a popular approach, as it has been shown that the generalised linear model (GLM) approach includes a bias that may be biologically unrealistic. However, the more versatile GAM approach has the disadvantage that a simple numerical description of the estimated response and estimation of the range (2×tolerance) and tolerance (t=σ, sigma) are not possible. To try to overcome this, I introduce a procedure that estimates the outer border and central border numerically for non-parametric functions. The outer and central borders are given by the distance from the optimum that is needed for the response curve to drop a certain amount, i.e. these parameters represent the rate of decrease in the response in both directions from the optimum independent of each other. Thus, the species with a narrow niche will show a faster drop (shorter distance from the optimum) than the species with a broader niche. The outer and the central borders are defined as the predictor values which take the response value equal to a specified fraction of the maximum response: max(E(y∣x))×exp(−2) for the outer border and max(E(y∣x))×exp(−1/2) for the expected central border. These parameters are directly related to the estimated response values rather than, as both range and tolerance, related to a polynomial function defining the response curve. For logistic regressions there are increasing number of positive observations outside the classic range as the maximum response increases. In this, the expected borders differ from the range, as the interpretation of the former is independent of the maximum response. By reporting the lower and upper outer border and the central border combined with the optimum for the species, a simple unbiased numerical description of the species–environment response is obtained, which can be used in comparative studies.}
}

@book{serfling1980,
  title={Approximation theorems of mathematical statistics},
  author={Serfling, Robert J},
  year={1980},
  publisher={John Wiley \& Sons},
address={New York}
}

@book{billingsley1986,
  title={Probability and measure},
  author={Billingsley, Patrick},
  year={1986},
  publisher={John Wiley \& Sons},
address={New York}
}

repository: bmourgui/ehmos

repo_name: bmourgui/ehmos