Publications

Heuclin, B et al (2022) M. Bayesian sparse group selection with indexed regressors within groups: the group fused horseshoe prior. JABES, under revision.

Pollock, L.J., et al (2022) Food webs and climate shape vertebrate distributions in Europe. Frontiers in Ecology and Evolution, minor revision.

Chauvier, Y., et al (2022) Resolution in species distribution models shapes spatial patterns of plant multifaceted diversity. Ecography, in press

Gibaud, J., Bry, X., Trottier, C., Mortier, F., Réjou-Méchain, M. (2022) Response mixture models based on supervised components: clustering floristic taxa.

  • Abstract:
    “In this paper, we propose to cluster responses in order to identify groups predicted by specific explanatory components. A response matrix is assumed to depend on a set of explanatory variables, and a set of additional covariates. Explanatory variables are supposed many and redundant, which implies some dimension reduction and regularization. By contrast, additional covariates contain few selected variables which are forced into the regression model, as they demand no regularization. The response matrix is assumed partitioned into several unknown groups of responses. We suppose that the responses in each group are predictable from an appropriate number of specific orthogonal supervised components of explanatory variables. The classification is based on a mixture model of the responses. To estimate the model, we propose a criterion extending that of Supervised Component-based Generalized Linear Regression, a Partial Least Squares-type method, and develop an algorithm combining component-based model and Expectation Maximization estimation. This new methodology is tested on simulated data and then applied to a floristic ecology dataset.”

Chauvier, Y., Zimmermann N. E., Poggiato G., Bystrova D., Brun P., & Thuiller W. (2021). Novel methods to correct for observer and sampling bias in presence-only species distribution models. Global Ecology and Biogeography, 30, 2312–2325.

  • Abstract:
    “Aim: While species distribution models (SDMs) are standard tools to predict species dis-tributions, they can suffer from observation and sampling biases, particularly presence-only SDMs, which often rely on species observations from non-standardized sampling efforts. To address this issue, sampling background points with a target-group strategy is commonly used, although more robust strategies and refinements could be implemented. Here, we exploited a dataset of plant species from the European Alps to propose and dem-onstrate efficient ways to correct for observer and sampling bias in presence-only models.Innovation: Recent methods correct for observer bias by including covariates related to accessibility in model calibrations (classic bias covariate correction, Classic-BCC). However, depending on how species are sampled, accessibility covariates may not sufficiently capture observer bias. Here, we introduced BCCs more directly related to sampling effort, as well as a novel corrective method based on stratified resampling of the observational dataset before model calibration (environmental bias correction, EBC). We compared, individually and jointly, the effect of EBC and different BCC strategies, when modelling the distributions of 1,900 plant species. We evaluated model performance with spatial block split-sampling and independent test data, and assessed the accuracy of plant diversity predictions across the European Alps.Main conclusions: Implementing EBC with BCC showed best results for every evalu-ation method. Particularly, adding the observation density of a target group as a bias covariate (Target-BCC) gave the most realistic modelled species distributions, with a clear positive correlation (r≃ .5) found between predicted and expert-based species richness. Although EBC must be carefully implemented in a species-specific manner, such limitations may be addressed via automated diagnostics included in a provided R function. Implementing EBC and bias covariate correction together may allow future studies to address efficiently observer bias in presence-only models, and overcome the standard need of an independent test dataset for model evaluation.”

Chauvier, Y., Thuiller, W., Brun, P., Lavergne, S., Descombes, P., Karger, D. N., Renaud, J., and Zimmermann, N. E.. 2021. Influence of climate, soil, and land cover on plant species distribution in the European Alps. Ecological Monographs 91( 2):e01433.

  • Abstract:
    “Although the importance of edaphic factors and habitat structure for plantgrowth and survival is known, both are often neglected in favor of climatic drivers when inves-tigating the spatial patterns of plant species and diversity. Yet, especially in mountain ecosys-tems with complex topography, missing edaphic and habitat components may be detrimentalfor a sound understanding of biodiversity distribution. Here, we compare the relative impor-tance of climate, soil and land cover variables when predicting the distributions of 2,616 vascu-lar plant species in the European Alps, representing approximately two-thirds of all Europeanflora. Using presence-only data, we built point-process models (PPMs) to relate species obser-vations to different combinations of covariates. We evaluated the PPMs through block cross-validations and assessed the independent contributions of climate, soil, and land cover covari-ates to predict plant species distributions using an innovative predictive partitioning approach.We found climate to be the most influential driver of spatial patterns in plant species with a rel-ative influence of~58.5% across all species, with decreasing importance from low to high eleva-tions. Soil (~20.1%) and land cover (~21.4%), overall, were less influential than climate, butincreased in importance along the elevation gradient. Furthermore, land cover showed stronglocal effects in lowlands, while the contribution of soil stabilized at mid-elevations. Thedecreasing influence of climate with elevation is explained by increasing endemism, and thefact that climate becomes more homogeneous as habitat diversity declines at higher altitudes.In contrast, soil predictors were found to follow the opposite trend. Additionally, at low eleva-tions, human-mediated land cover effects appear to reduce the importance of climate predic-tors. We conclude that soil and land cover are, like climate, principal drivers of plant speciesdistribution in the European Alps. While disentangling their effects remains a challenge, futurestudies can benefit markedly by including soil and land cover effects when predicting speciesdistributions.”

Bystrova, D., Poggiato, G., Bektaş, B., Arbel, J., Clark, J. S., Guglielmi, A., Thuiller, W. (2021). Clustering Species With Residual Covariance Matrix in Joint Species Distribution Models. 9.

  • Abstract:
    “Modeling species distributions over space and time is one of the major research topics in both ecology and conservation biology. Joint Species Distribution models (JSDMs) have recently been introduced as a tool to better model community data, by inferring a residual covariance matrix between species, after accounting for species' response to the environment. However, these models are computationally demanding, even when latent factors, a common tool for dimension reduction, are used. To address this issue, Taylor-Rodriguez et al. (2017) proposed to use a Dirichlet process, a Bayesian nonparametric prior, to further reduce model dimension by clustering species in the residual covariance matrix. Here, we built on this approach to include a prior knowledge on the potential number of clusters, and instead used a Pitman–Yor process to address some critical limitations of the Dirichlet process. We therefore propose a framework that includes prior knowledge in the residual covariance matrix, providing a tool to analyze clusters of species that share the same residual associations with respect to other species. We applied our methodology to a case study of plant communities in a protected area of the French Alps (the Bauges Regional Park), and demonstrated that our extensions improve dimension reduction and reveal additional information from the residual covariance matrix, notably showing how the estimated clusters are compatible with plant traits, endorsing their importance in shaping communities.”

Réjou-Méchain, M., Mortier, F., Bastin, JF., Cornu, G., Barbier, N., Bayol, N., Bénédet, F., Bry, X., Dauby, G., Deblauwe, V., Doucet, J.-L.,Doumenge, C., Fayolle, A., Garcia, C., Kibambe Lubamba, J.-P., Loumeto, J.-J., Ngomanda, A.,Ploton, P., Sonké, B., Trottier, C., Vimal, R., Yongo, O., Pélissier, R., Gourlet-Fleury, S. (2021). Unveiling African rainforest composition and vulnerability to global change. Nature 593, 90–94.

  • Abstract:
    “Africa is forecasted to experience large and rapid climate change1 and population growth2 during the twenty-first century, which threatens the world’s second largest rainforest. Protecting and sustainably managing these African forests requires an increased understanding of their compositional heterogeneity, the environmental drivers of forest composition and their vulnerability to ongoing changes. Here, using a very large dataset of 6 million trees in more than 180,000 field plots, we jointly model the distribution in abundance of the most dominant tree taxa in central Africa, and produce continuous maps of the floristic and functional composition of central African forests. Our results show that the uncertainty in taxon-specific distributions averages out at the community level, and reveal highly deterministic assemblages. We uncover contrasting floristic and functional compositions across climates, soil types and anthropogenic gradients, with functional convergence among types of forest that are floristically dissimilar. Combining these spatial predictions with scenarios of climatic and anthropogenic global change suggests a high vulnerability of the northern and southern forest margins, the Atlantic forests and most forests in the Democratic Republic of the Congo, where both climate and anthropogenic threats are expected to increase sharply by 2085. These results constitute key quantitative benchmarks for scientists and policymakers to shape transnational conservation and management strategies that aim to provide a sustainable future for central African forests.”

Picard, N., Mortier, F., Ploton, P., Liang J., Derroire, G., Bastin, J.-F., Ayyappan, N., Bénédet, F., Boyemba Bosela, F., Clark, C. J., Crowther, T. W., Engone Obiang, N. L., Forni, É., Harris, D., Ngomanda, A., Poulsen, J. R., Sonké, B., Couteron, P., Gourlet-Fleury, S. (2021). Using Model Analysis to Unveil Hidden Patterns in Tropical Forest Structures. Frontiers in Ecology and Evolution. 9.

  • Abstract:
    “When ordinating plots of tropical rain forests using stand-level structural attributes such as biomass, basal area and the number of trees in different size classes, two patterns often emerge: a gradient from poorly to highly stocked plots and high positive correlations between biomass, basal area and the number of large trees. These patterns are inherited from the demographics (growth, mortality and recruitment) and size allometry of trees and tend to obscure other patterns, such as site differences among plots, that would be more informative for inferring ecological processes. Using data from 133 rain forest plots at nine sites for which site differences are known, we aimed to filter out these patterns in forest structural attributes to unveil a hidden pattern. Using a null model framework, we generated the anticipated pattern inherited from individual allometric patterns. We then evaluated deviations between the data (observations) and predictions of the null model. Ordination of the deviations revealed site differences that were not evident in the ordination of observations. These sites differences could be related to different histories of large-scale forest disturbance. By filtering out patterns inherited from individuals, our model analysis provides more information on ecological processes.”

Poggiato, G., Münkemüller, T., Bystrova, D., Arbel, J., Clark, J. S., Thuiller, W. (2021) On the Interpretations of Joint Modeling in Community Ecology. Trends in Ecology & Evolution.

  • Abstract:
    “Explaining and modeling species communities is more than ever a central goal of ecology. Recently, joint species distribution models (JSDMs), which extend species distribution models (SDMs) by considering correlations among species, have been proposed to improve species community analyses and rare species predictions while potentially inferring species interactions. Here, we illustrate the mathematical links between SDMs and JSDMs and their ecological implications and demonstrate that JSDMs, just like SDMs, cannot separate environmental effects from biotic interactions. We provide a guide to the conditions under which JSDMs are (or are not) preferable to SDMs for species community modeling. More generally, we call for a better uptake and clarification of novel statistical developments in the field of biodiversity modeling.”

Peyhardi, J., Fernique, P., Durand,J.-B. (2021) Splitting models for multivariate count data. Journal of Multivariate Analysis. 181.

  • Abstract:
    “We investigate the class of splitting distributions as the composition of a singular multivariate distribution and a univariate distribution. It will be shown that most common parametric count distributions (multinomial, negative multinomial, multivariate hypergeometric, multivariate negative hypergeometric, …) can be written as splitting distributions with separate parameters for both components, thus facilitating their interpretation, inference, the study of their probabilistic characteristics and their extensions to regression models. We highlight many probabilistic properties deriving from the compound aspect of splitting distributions and their underlying algebraic properties. Parameter inference and model selection are thus reduced to two separate problems, preserving time and space complexity of the base models. Based on this principle, we introduce several new distributions. In the case of multinomial splitting distributions, conditional independence and asymptotic normality properties for estimators are obtained. Mixtures of splitting regression models are used on a mango tree dataset in order to analyze the patchiness.”

Martinez-Almoyna, C., Piton, G., Abdulhak, S., Boulangeat, L., Choler, P., Delahay, T., Dentant, C., Foulquier, A., Poulenard, J., Noble, V., Renaud, J., Rome, M., Saillard, A., The ORCHAMP Consortium,Thuiller, W., & Münkemüller, T. (2020) Climate, soil resources and microbial activity shape the distributions of mountain plants based on their functional traits. Ecography. 43, 10, 1550-1559.

  • Abstract:
    “While soil ecosystems undergo important modifications due to global change, the effect of soil properties on plant distributions is still poorly understood. Plant growth is not only controlled by soil physico‐chemistry but also by microbial activities through the decomposition of organic matter and the recycling of nutrients essential for plants. A growing body of evidence also suggests that plant functional traits modulate species’ response to environmental gradients. However, no study has yet contrasted the importance of soil physico‐chemistry, microbial activities and climate on plant species distributions, while accounting for how plant functional traits can influence species‐specific responses. Using hierarchical effects in a multi‐species distribution model, we investigate how four functional traits related to resource acquisition (plant height, leaf carbon to nitrogen ratio, leaf dry matter content and specific leaf area) modulate the response of 44 plant species to climatic variables, soil physico‐chemical properties and microbial decomposition activity (i.e. exoenzymatic activities) in the French Alps. Our hierarchical trait‐based model allowed to predict well 41 species according to the TSS statistic. In addition to climate, the combination of soil C/N, as a measure of organic matter quality, and exoenzymatic activity, as a measure of microbial decomposition activity, strongly improved predictions of plant distributions. Plant traits played an important role. In particular, species with conservative traits performed better under limiting nutrient conditions but were outcompeted by exploitative plants in more favorable environments. We demonstrate tight associations between microbial decomposition activity, plant functional traits associated to different resource acquisition strategies and plant distributions. This highlights the importance of plant–soil linkages for mountain plant distributions. These results are crucial for biodiversity modelling in a world where both climatic and soil systems are undergoing profound and rapid transformations.”

Zurell, D., Franklin, J., Bouchet, P.J., Dormann, C.F., Elith, J., Fandos Guzman, G., Feng, X., Guillera-Arroita, G., Guisan, A., König, C., Lahoz-Monfort, J.J., Leitão, P.J., Park, D.S., Peterson, A.T., Rapacciuolo, G., Schmatz, D., Schröder, B., Serra-Diaz, J.M., Thuiller, W., Yates, K.L., Zimmermann, N.E. and Merow, C. (2020) A standard protocol for reporting species distribution models. Ecography, 43, 1–17

  • Abstract:
    “Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready‐to‐use software packages and increasing availability of digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservation and management, and for quantifying impacts from global change. However, models must be fit for purpose, with all important aspects of their development and applications properly considered. Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use. To address these issues, we propose a standard protocol for reporting SDMs, with an emphasis on describing how a study's objective is achieved through a series of modeling decisions. We call this the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol, as its components reflect the main steps involved in building SDMs and other empirically‐based biodiversity models. The ODMAP protocol serves two main purposes. First, it provides a checklist for authors, detailing key steps for model building and analyses, and thus represents a quick guide and generic workflow for modern SDMs. Second, it introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta‐analyses. We detail all elements of ODMAP, and explain how it can be used for different model objectives and applications, and how it complements efforts to store associated metadata and define modelling standards. We illustrate its utility by revisiting nine previously published case studies, and provide an interactive web‐based application to facilitate its use. We plan to advance ODMAP by encouraging its further refinement and adoption by the scientific community.”

Münkemüller, T, Gallien, L, Pollock, LJ, Barros, C, Carboni, M, Chalmandrier, L, Mazel, F, Mokany, K, Roquet, C, Smyčka, J, Talluto, M, Thuiller, W. (2020). Dos and don'ts when inferring assembly rules from diversity patterns. Global Ecol Biogeogr. 2020; 29: 1212– 1229.

  • Abstract:
    “
    Aim
    More than ever, ecologists seek to understand how species are distributed and have assembled into communities using the “filtering framework”. This framework is based on the hypothesis that local assemblages result from a series of abiotic and biotic filters applied to regional species pools and that these filters leave predictable signals in observed diversity patterns. In theory, statistical comparisons of expected and observed patterns enable data‐driven tests of assembly processes. However, so far this framework has fallen short in delivering generalizable conclusions, challenging whether (and how) diversity patterns can be used to characterize and understand underlying assembly processes better.
    Methods
    By synthesizing the previously raised critiques and suggested solutions in a comprehensive way, we identify 10 pitfalls that can lead to flawed interpretations of α‐diversity patterns, summarize solutions developed to circumvent these pitfalls and provide general guidelines.
    Results
    We find that most issues arise from an overly simplistic view of potential processes that influence diversity patterns, which is often motivated by practical constraints on study design, focal scale and methodology. We outline solutions for each pitfall, such as methods spanning over spatial, environmental or phylogenetic scales, and suggest guidelines for best scientific practices in community ecology. Among key future challenges are the integration of mechanistic modelling and multi‐trophic interactions.
    Main conclusions
    Our conclusion is that the filtering framework still holds promise, but only if researchers successfully navigate major pitfalls, foster the integration of mechanistic modelling and multi‐trophic interactions and directly account for uncertainty in their conclusions.
    ”

Laroche, F., Violle, C., Taudière, A., and Munoz, F.. 2020. Analyzing snapshot diversity patterns with the Neutral Theory can show functional groups’ effects on community assembly. Ecology 101(4):e02977.

  • Abstract:
    “
    A central question of community ecology is to understand how the interplay between processes of the Neutral Theory (e.g., immigration and ecological drift) and niche-based processes (e.g., environmental filtering, intra- and interspecific density dependence) shape species diversity in competitive communities. The articulation between these two categories of mechanisms can be studied through the lens of the intermediate organizational level of “functional groups” (FGs), defined as clusters of species with similar traits. Indeed, FGs stress ecological differences among species and are thus likely to unravel non-neutral interactions within communities. Here we presented a novel approach to explore how FGs affect species coexistence by comparing species and functional diversity patterns. Our framework considers the Neutral Theory as a mechanistic null hypothesis. It assesses how much the functional diversity deviates from species diversity in communities, and compares this deviation, called the “average functional deviation,” to a neutral baseline. We showed that the average functional deviation can indicate reduced negative density dependence or environmental filtering among FGs. We validated our framework using simulations illustrating the two situations. We further analyzed tropical tree communities in Western Ghats, India. Our analysis of the average functional deviation revealed environmental filtering between deciduous and evergreen FGs along a broad rainfall gradient. By contrast, we did not find clear evidence for reduced density dependence among FGs. We predict that applying our approach to new case studies where environmental gradients are milder and FGs are more clearly associated to resource partitioning should reveal the missing pattern of reduced density dependence among FGs.
    ”