How much should one sample to accurately predict the distribution of species assemblages? A virtual community approach

Correlative species distribution models (SDMs) are widely used to predict species distributions and assemblages, with many fundamental and applied uses. Different factors were shown to affect SDM prediction accuracy. However, real data cannot give unambiguous answers on these issues, and for this reason, artificial data have been increasingly used in recent years. Here, we move one step further by assessing how different factors can affect the prediction accuracy of virtual assemblages obtained by stacking individual SDM predictions (stacked SDMs, S-SDM). We modeled 100 virtual species in a real study area, testing five different factors: sample size (200-800-3200), sampling method (nested, non-nested), sampling prevalence (25%, 50%, 75% and species true prevalence), modelling technique (GAM, GLM, BRT and RF) and thresholding method (ROC, MaxTSS, and MaxKappa). We showed that the accuracy of S-SDM predictions is mostly affected by modelling technique followed by sample size. Models fitted by GAM/GLM had a higher accuracy and lower variance than BRT/RF. Model accuracy increased with sample size and a sampling strategy reflecting the true prevalence of the species was most successful. However, even with sample sizes as high as >3000 sites, residual uncertainty remained in the predictions, potentially reflecting a bias introduced by creating and/or resampling the virtual species. Therefore, when evaluating the accuracy of predictions from S-SDMs fitted with real field data, one can hardly expect reaching perfect accuracy, and reasonably high values of similarity or predictive success can already be seen as valuable predictions. We recommend the use of a ‘plot-like’ sampling method (best approximation of the species’ true prevalence) and not simply increasing the number of presences-absences of species. As presented here, virtual simulations might be used more systematically in future studies to inform about the best accuracy level that one could expect given the characteristics of the data and the methods used to fit and stack SDMs

How to evaluate community predictions without thresholding?

Stacked species distribution models (S-SDM) provide a tool to make spatial predictions about communities by first modelling individual species and then stacking the modelled predictions to form assemblages. The evaluation of the predictive performance is usually based on a comparison of the observed and predicted community properties (e.g. species richness, composition). However, the most available and widely used evaluation metrics require the thresholding of single species' predicted probabilities of occurrence to obtain binary outcomes (i.e. presence/absence). This binarization can introduce unnecessary bias and error. Herein, we present and demonstrate the use of several groups of new or rarely used evaluation approaches and metrics for both species richness and community composition that do not require thresholding but instead directly compare the predicted probabilities of occurrences of species to the presence/absence observations in the assemblages. Community AUC, which is based on traditional AUC, measures the ability of a model to differentiate between species presences or absences at a given site according to their predicted probabilities of occurrence. Summing the probabilities gives the expected species richness and allows the estimation of the probability that the observed species richness is not different from the expected species richness based on the species' probabilities of occurrence. The traditional Sorensen and Jaccard similarity indices (which are based on presences/absences) were adapted to maxSorensen and maxJaccard and to probSorensen and probJaccard (which use probabilities directly). A further approach (improvement over null models) compares the predictions based on S-SDMs with the expectations from the null models to estimate the improvement in both species richness and composition predictions. Additionally, all metrics can be described against the environmental conditions of sites (e.g. elevation) to highlight the abilities of models to detect the variation in the strength of the community assembly processes in different environments. These metrics offer an unbiased view of the performance of community predictions compared to metrics that requiring thresholding. As such, they allow more straightforward comparisons of model performance among studies (i.e. they are not influenced by any subjective thresholding decisions).Peer reviewe

Ecological indicator values reveal missing predictors of species distributions.

The questions of how much abiotic environment contributes to explain species distributions, and which abiotic factors are the most influential, are key when projecting species realized niches in space and time. Here, we show that answers to these questions can be obtained by using species' ecological indicator values (EIVs). By calculating community averages of plant EIVs (397 plant species and 3988 vegetation plots), we found that substituting mapped environmental predictors with site EIVs led to a doubling of explained variation (22.5% to 44%). EIVs representing light and soil showed the highest model improvement, while EIVs representing temperature did not explain additional variance, suggesting that current temperature maps are already fairly accurate. Therefore, although temperature is frequently reported as having a dominant effect on species distributions over other factors, our results suggest that this might primarily result from limitations in our capacity to map other key environmental factors, such as light and soil properties, over large areas

Uncertainty, errors and virtual ecology: using artificial data to improve species distribution models

With the growing pressures exerted by anthropogenic activities (e.g. land-use changes, habitat fragmentation, greenhouse gas emissions) and environmental changes (e.g. climate change, biological invasions), biodiversity is being threatened worldwide. It is therefore important to sufficiently understand which factors influence the distribution and composition of species assemblages, develop tools allowing us to accurately predict them under current and future environmental conditions. Species distribution models (SDMs) are especially useful to tackle these challenges since they allow the modelling of the distribution of species and their assemblages at different spatial and temporal scales. This is done by simply relating species observations with environmental conditions where they occur. However, different factors (e.g. sample size, modelling technique) and errors/bias (i.e. false presences/absences) were shown to affect the prediction accuracy of single species and assemblage SDMs (i.e. S-SDMs). SDMs can also provide biased projections when predicting to regions or time periods with environmental conditions outside the range of data used for model calibration (i.e. model transferability) or when that data doesn’t capture the full conditions occupied by the species (i.e. truncated datasets). While the majority of SDMs use real species data, it is important to assess their accuracy by having complete control of the data and factors influencing species distributions, hence the use of virtual or simulated species. In the first chapter of my thesis, I used virtual species data to test SDM/S-SDMs and determine the degree to which different types and levels of errors in species data (i.e. false presences or absences) affect the predictions of individual species models, and how this is reflected in metrics that are frequently used to evaluate the prediction accuracy of SDMs. I found that interpretation of models’ performance depended on the data and metrics used to evaluate them, with model performance being more affected by false positives. In the second chapter, I assessed how different factors (sample size, sampling method, sampling prevalence, modelling technique and thresholding method) affect the prediction accuracy of S-SDMs. I found that prediction accuracy is mostly affected by modelling technique followed by sample size and that a ‘plot-like’ sampling method is recommended when sampling species data (i.e. best approximation of the species’ true prevalence). In my third chapter I tested the potential causes that increasingly truncated datasets have on the predictive accuracy of species assemblages and if the variables used to calibrate the models also influence that accuracy, finding that the degree of truncation has more influence on species with wide realized niches. Finally, on my last main chapter, I tested and compared how accurate different modelling strategies are at predicting species assemblages under current and future climatic conditions, assessing their transferability. I found that when using presence/pseudo-absence data, all the strategies failed to predict accurate species assemblages, being better when presence-absence data is used (under current environmental conditions). -- La biodiversité est actuellement mondialement menacée par l’augmentation de la pression due aux activités anthropiques (p. ex. changement dans l’utilisation du territoire, fragmentation des habitats, émission de gaz à effet de serre) et aux changements environnementaux (p. ex. changements climatiques, invasions biologiques). Il est donc capital de comprendre les facteurs influençant la distribution et la composition des assemblages d’espèces ainsi que de développer des outils pour les prédire précisément autant dans des conditions environnementales actuelles que future. Les modèles prédictifs de distribution (MPDs) sont des outils particulièrement utiles pour appréhender ce genre de challenges, car ils permettent de modéliser la distribution des espèces ainsi que leurs assemblages à différentes échelles spatiales et temporelles. Cela peut se faire en reliant des observations d’espèces avec les conditions environnementales dans lesquelles elles se trouvent. Cependant, il a été montré que différent facteurs (p. ex. taille d’échantillonnage, techniques de modélisation) et erreur/biais (c.-à-d. fausses présences/absences) peuvent affecter la qualité des prédictions obtenues lors de la modélisation prédictive de la distribution de simples espèces (MPD) et d’assemblages (S-SDMs). Les MPDs peuvent aussi créer des projections biaisées lorsqu’ils prédisent dans des régions ou des périodes de temps qui possèdent des conditions environnementales en dehors de la gamme de données utilisées lors de la calibration du modèle (c.-à-d. transférabilité du modèle) ou quand les données ne représentent pas l’entier des conditions occupées par l’espèce (c.-à-d. jeu de données tronqué). Bien que la majorité des MPDs utilisent des données d’espèces réelles, il est important de pouvoir évaluer leurs précisions en ayant le contrôle complet des données ainsi que des facteurs pouvant influencer la distribution des espèces. Seul l’utilisation d’espèces virtuelles ou simulées permet d’obtenir ce contrôle total. Dans le premier chapitre de ma thèse, j’ai utilisé des données d’espèces virtuelles afin de déterminer, à l’aide de MPDs/S-SDMs, dans quelle mesure différents types et niveaux d’erreurs dans les données d’espèces (c.-à-d. fausses présences ou absences) pouvaient affecter les prédictions obtenues. J’ai aussi cherché à comprendre comment cela se reflète sur les métriques habituellement utilisées pour évaluer la qualité des prédictions de ces MPDs. J’ai découvert que l’interprétation des performances des modèles dépends des données et des métriques utilisées pour les évaluer. Cette performance est particulièrement affectée par les faux positifs. Dans le second chapitre, j’ai évalué comment différents facteurs (taille d’échantillonnage, méthode d’échantillonnage, prévalence d’échantillonnage, technique de modélisation et méthode de définition des seuils) affectent la qualité des prédictions obtenues à l’aide de S- SDMs. J’ai trouvé que la qualité des prédictions est principalement affectée par les techniques de modélisation, suivie par la taille de l’échantillonnage. Une méthode d’échantillonnage dite « plot-like » est recommandée lors de la récolte de données (c.-à-d. qu’elle donne la meilleure approximation de la réelle prévalence de l’espèce). Dans mon troisième chapitre, j’ai testé quels pouvaient être les potentiels effets de l’utilisation de jeux de données de plus en plus tronqués sur la qualité des prédictions des assemblages d’espèces ainsi que l’influence des variables utilisées lors de la calibration. Il s’avère que le degré de troncature a plus d’effet sur les espèces ayant une large niche réalisée. Finalement, dans mon dernier chapitre, j’ai testé différentes stratégies de modélisation puis j’ai comparé leur aptitude à prédire des assemblages d’espèces dans des conditions présentes et futures pour évaluer leur transférabilité. J’ai découvert que lors de l’utilisation de données de présences/pseudo-absences, toutes les stratégies échouaient à prédire de manière précise les assemblages. L’utilisation de données de présence/absences a permis, quant à elle, d’obtenir de meilleurs résultats, principalement dans des conditions environnementales présentes