Combining food web and species distribution models for improved community projections

The ability to model biodiversity patterns is of prime importance in this era of severe environmental crisis. Species assemblage along environmental gradient is subject to the interplay of biotic interactions in complement to abiotic environmental filtering. Accounting for complex biotic interactions for a wide array of species remains so far challenging. Here, we propose to use food web models that can infer the potential interaction links between species as a constraint in species distribution models. Using a plant-herbivore (butterfly) interaction dataset, we demonstrate that this combined approach is able to improve both species distribution and community forecasts. Most importantly, this combined approach is very useful in rendering models of more generalist species that have multiple potential interaction links, where gap in the literature may be recurrent. Our combined approach points a promising direction forward to model the spatial variation of entire species interaction networks. Our work has implications for studies of range shifting species and invasive species biology where it may be unknown how a given biota might interact with a potential invader or in future climate.

What do we gain from simplicity versus complexity in species distribution models?

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.

Sensitivity of predictive species distribution models to change in grain size

Predictive species distribution modelling (SDM) has become an essential tool in biodiversity conservation and management. The choice of grain size (resolution) of environmental layers used in modelling is one important factor that may affect predictions. We applied 10 distinct modelling techniques to presence-only data for 50 species in five different regions, to test whether: (1) a 10-fold coarsening of resolution affects predictive performance of SDMs, and (2) any observed effects are dependent on the type of region, modelling technique, or species considered. Results show that a 10 times change in grain size does not severely affect predictions from species distribution models. The overall trend is towards degradation of model performance, but improvement can also be observed. Changing grain size does not equally affect models across regions, techniques, and species types. The strongest effect is on regions and species types, with tree species in the data sets (regions) with highest locational accuracy being most affected. Changing grain size had little influence on the ranking of techniques: boosted regression trees remain best at both resolutions. The number of occurrences used for model training had an important effect, with larger sample sizes resulting in better models, which tended to be more sensitive to grain. Effect of grain change was only noticeable for models reaching sufficient performance and/or with initial data that have an intrinsic error smaller than the coarser grain size.

The MIGCLIM R package - seamless integration of dispersal constraints into projections of species distribution models

The MIGCLIM R package is a function library for the open source R software that enables the implementation of species-specific dispersal constraints into projections of species distribution models under environmental change and/or landscape fragmentation scenarios. The model is based on a cellular automaton and the basic modeling unit is a cell that is inhabited or not. Model parameters include dispersal distance and kernel, long distance dispersal, barriers to dispersal, propagule production potential and habitat invasibility. The MIGCLIM R package has been designed to be highly flexible in the parameter values it accepts, and to offer good compatibility with existing species distribution modeling software. Possible applications include the projection of future species distributions under environmental change conditions and modeling the spread of invasive species.

Species distribution and antifungal susceptibility profile of Candida spp. bloodstream isolates from Latin American hospitals

From March 1999 to March 2000, we conducted a prospective multicenter study of candidemia involving five tertiary care hospitals from four countries in Latin America. Yeast isolates were identified by classical methods and the antifungal susceptibility profile was determined according to the National Committee for Clinical Laboratory Standards microbroth assay method. During a 12 month-period we were able to collect a total of 103 bloodstream isolates of Candida spp. C. albicans was the most frequently isolated species accounting for 42% of all isolates. Non-albicans Candida species strains accounted for 58% of all episodes of candidemia and were mostly represented by C. tropicalis (24.2%) and C. parapsilosis (21.3%). It is noteworthy that we were able to identify two cases of C. lusitaniae from different institutions. In our casuistic, non-albicans Candida species isolates related to candidemic episodes were susceptible to fluconazole. Continuously surveillance programs are needed in order to identify possible changes in the species distribution and antifungal susceptibility patterns of yeasts that may occurs after increasing the use of azoles in Latin American hospitals.

Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data

Background Multiple logistic regression is precluded from many practical applications in ecology that aim to predict the geographic distributions of species because it requires absence data, which are rarely available or are unreliable. In order to use multiple logistic regression, many studies have simulated "pseudo-absences" through a number of strategies, but it is unknown how the choice of strategy influences models and their geographic predictions of species. In this paper we evaluate the effect of several prevailing pseudo-absence strategies on the predictions of the geographic distribution of a virtual species whose "true" distribution and relationship to three environmental predictors was predefined. We evaluated the effect of using a) real absences b) pseudo-absences selected randomly from the background and c) two-step approaches: pseudo-absences selected from low suitability areas predicted by either Ecological Niche Factor Analysis: (ENFA) or BIOCLIM. We compared how the choice of pseudo-absence strategy affected model fit, predictive power, and information-theoretic model selection results. Results Models built with true absences had the best predictive power, best discriminatory power, and the "true" model (the one that contained the correct predictors) was supported by the data according to AIC, as expected. Models based on random pseudo-absences had among the lowest fit, but yielded the second highest AUC value (0.97), and the "true" model was also supported by the data. Models based on two-step approaches had intermediate fit, the lowest predictive power, and the "true" model was not supported by the data. Conclusion If ecologists wish to build parsimonious GLM models that will allow them to make robust predictions, a reasonable approach is to use a large number of randomly selected pseudo-absences, and perform model selection based on an information theoretic approach. However, the resulting models can be expected to have limited fit.

SESAM - a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages

Two different approaches currently prevail for predicting spatial patterns of species assemblages. The first approach (macroecological modelling, MEM) focuses directly on realised properties of species assemblages, whereas the second approach (stacked species distribution modelling, S-SDM) starts with constituent species to approximate assemblage properties. Here, we propose to unify the two approaches in a single 'spatially-explicit species assemblage modelling' (SESAM) framework. This framework uses relevant species source pool designations, macroecological factors, and ecological assembly rules to constrain predictions of the richness and composition of species assemblages obtained by stacking predictions of individual species distributions. We believe that such a framework could prove useful in many theoretical and applied disciplines of ecology and evolution, both for improving our basic understanding of species assembly across spatio-temporal scales and for anticipating expected consequences of local, regional or global environmental changes. In this paper, we propose such a framework and call for further developments and testing across a broad range of community types in a variety of environments.

Mosquito fauna on the Cape Verde Islands (West Africa): an update on species distribution and a new finding

To evaluate the risk of transmission of vector-borne diseases, regular updates of the geographic distribution of insect vectors are required. In the archipelago of Cape Verde, nine mosquito species have been reported. Of these, four are major vectors of diseases that have been present in the archipelago: yellow fever, lymphatic filariasis, malaria and, currently, an outbreak of dengue. In order to assess variation in mosquito biodiversity, we have carried out an update on the distribution of the mosquito species in Cape Verde, based on an enquiry of 26 unpublished technical reports (1983-2006) and on the results of an entomological survey carried out in 2007. Overall, there seems to be a general trend for an expansion of biological diversity in the islands. Mosquito species richness was negatively correlated with the distance of the islands from the mainland but not with the size of the islands. Human- and/or sporadic climatic-mediated events of dispersal may have contributed to a homogenization of species richness regardless of island size but other ecological factors may also have affected the mosquito biogeography in the archipelago. An additional species, Culex perexiguus, was collected for the first time in the archipelago during the 2007 survey.

Predicting species distribution: offering more than simple habitat models

n the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.

Unfolding nature "in silico" : overcoming practical and metodological limitations of species distribution models

Abstract : The existence of a causal relationship between the spatial distribution of living organisms and their environment, in particular climate, has been long recognized and is the central principle of biogeography. In turn, this recognition has led scientists to the idea of using the climatic, topographic, edaphic and biotic characteristics of the environment to predict its potential suitability for a given species or biological community. In this thesis, my objective is to contribute to the development of methodological improvements in the field of species distribution modeling. More precisely, the objectives are to propose solutions to overcome limitations of species distribution models when applied to conservation biology issues, or when .used as an assessment tool of the potential impacts of global change. The first objective of my thesis is to contribute to evidence the potential of species distribution models for conservation-related applications. I present a methodology to generate pseudo-absences in order to overcome the frequent lack of reliable absence data. I also demonstrate, both theoretically (simulation-based) and practically (field-based), how species distribution models can be successfully used to model and sample rare species. Overall, the results of this first part of the thesis demonstrate the strong potential of species distribution models as a tool for practical applications in conservation biology. The second objective this thesis is to contribute to improve .projections of potential climate change impacts on species distributions, and in particular for mountain flora. I develop and a dynamic model, MIGCLIM, that allows the implementation of dispersal limitations into classic species distribution models and present an application of this model to two virtual species. Given that accounting for dispersal limitations requires information on seed dispersal, distances, a general methodology to classify species into broad dispersal types is also developed. Finally, the M~GCLIM model is applied to a large number of species in a study area of the western Swiss Alps. Overall, the results indicate that while dispersal limitations can have an important impact on the outcome of future projections of species distributions under climate change scenarios, estimating species threat levels (e.g. species extinction rates) for a mountainous areas of limited size (i.e. regional scale) can also be successfully achieved when considering dispersal as unlimited (i.e. ignoring dispersal limitations, which is easier from a practical point of view). Finally, I present the largest fine scale assessment of potential climate change impacts on mountain vegetation that has been carried-out to date. This assessment involves vegetation from 12 study areas distributed across all major western and central European mountain ranges. The results highlight that some mountain ranges (the Pyrenees and the Austrian Alps) are expected to be more affected by climate change than others (Norway and the Scottish Highlands). The results I obtain in this study also indicate that the threat levels projected by fine scale models are less severe than those derived from coarse scale models. This result suggests that some species could persist in small refugias that are not detected by coarse scale models. Résumé : L'existence d'une relation causale entre la répartition des espèces animales et végétales et leur environnement, en particulier le climat, a été mis en évidence depuis longtemps et est un des principes centraux en biogéographie. Ce lien a naturellement conduit à l'idée d'utiliser les caractéristiques climatiques, topographiques, édaphiques et biotiques de l'environnement afin d'en prédire la qualité pour une espèce ou une communauté. Dans ce travail de thèse, mon objectif est de contribuer au développement d'améliorations méthodologiques dans le domaine de la modélisation de la distribution d'espèces dans le paysage. Plus précisément, les objectifs sont de proposer des solutions afin de surmonter certaines limitations des modèles de distribution d'espèces dans des applications pratiques de biologie de la conservation ou dans leur utilisation pour évaluer l'impact potentiel des changements climatiques sur l'environnement. Le premier objectif majeur de mon travail est de contribuer à démontrer le potentiel des modèles de distribution d'espèces pour des applications pratiques en biologie de la conservation. Je propose une méthode pour générer des pseudo-absences qui permet de surmonter le problème récurent du manque de données d'absences fiables. Je démontre aussi, de manière théorique (par simulation) et pratique (par échantillonnage de terrain), comment les modèles de distribution d'espèces peuvent être utilisés pour modéliser et améliorer l'échantillonnage des espèces rares. Ces résultats démontrent le potentiel des modèles de distribution d'espèces comme outils pour des applications de biologie de la conservation. Le deuxième objectif majeur de ce travail est de contribuer à améliorer les projections d'impacts potentiels des changements climatiques sur la flore, en particulier dans les zones de montagnes. Je développe un modèle dynamique de distribution appelé MigClim qui permet de tenir compte des limitations de dispersion dans les projections futures de distribution potentielle d'espèces, et teste son application sur deux espèces virtuelles. Vu que le fait de prendre en compte les limitations dues à la dispersion demande des données supplémentaires importantes (p.ex. la distance de dispersion des graines), ce travail propose aussi une méthode de classification simplifiée des espèces végétales dans de grands "types de disperseurs", ce qui permet ainsi de d'obtenir de bonnes approximations de distances de dispersions pour un grand nombre d'espèces. Finalement, j'applique aussi le modèle MIGCLIM à un grand nombre d'espèces de plantes dans une zone d'études des pré-Alpes vaudoises. Les résultats montrent que les limitations de dispersion peuvent avoir un impact considérable sur la distribution potentielle d'espèces prédites sous des scénarios de changements climatiques. Cependant, quand les modèles sont utilisés pour évaluer les taux d'extinction d'espèces dans des zones de montages de taille limitée (évaluation régionale), il est aussi possible d'obtenir de bonnes approximations en considérant la dispersion des espèces comme illimitée, ce qui est nettement plus simple d'un point dé vue pratique. Pour terminer je présente la plus grande évaluation à fine échelle d'impact potentiel des changements climatiques sur la flore des montagnes conduite à ce jour. Cette évaluation englobe 12 zones d'études réparties sur toutes les chaines de montages principales d'Europe occidentale et centrale. Les résultats montrent que certaines chaines de montagnes (les Pyrénées et les Alpes Autrichiennes) sont projetées comme plus sensibles aux changements climatiques que d'autres (les Alpes Scandinaves et les Highlands d'Ecosse). Les résultats obtenus montrent aussi que les modèles à échelle fine projettent des impacts de changement climatiques (p. ex. taux d'extinction d'espèces) moins sévères que les modèles à échelle large. Cela laisse supposer que les modèles a échelle fine sont capables de modéliser des micro-niches climatiques non-détectées par les modèles à échelle large.

Improving the prediction of plant species distribution and community composition by adding edaphic to topo-climatic variables

Questions Soil properties have been widely shown to influence plant growth and distribution. However, the degree to which edaphic variables can improve models based on topo-climatic variables is still unclear. In this study, we tested the roles of seven edaphic variables, namely (1) pH; (2) the content of nitrogen and of (3) phosphorus; (4) silt; (5) sand; (6) clay and (7) carbon-to-nitrogen ratio, as predictors of species distribution models in an edaphically heterogeneous landscape. We also tested how the respective influence of these variables in the models is linked to different ecological and functional species characteristics. Location The Western Alps, Switzerland. Methods With four different modelling techniques, we built models for 115 plant species using topo-climatic variables alone and then topo-climatic variables plus each of the seven edaphic variables, one at a time. We evaluated the contribution of each edaphic variable by assessing the change in predictive power of the model. In a second step, we evaluated the importance of the two edaphic variables that yielded the largest increase in predictive power in one final set of models for each species. Third, we explored the change in predictive power and the importance of variables across plant functional groups. Finally, we assessed the influence of the edaphic predictors on the prediction of community composition by stacking the models for all species and comparing the predicted communities with the observed community. Results Among the set of edaphic variables studied, pH and nitrogen content showed the highest contributions to improvement of the predictive power of the models, as well as the predictions of community composition. When considering all topo-climatic and edaphic variables together, pH was the second most important variable after degree-days. The changes in model results caused by edaphic predictors were dependent on species characteristics. The predictions for the species that have a low specific leaf area, and acidophilic preferences, tolerating low soil pH and high humus content, showed the largest improvement by the addition of pH and nitrogen in the model. Conclusions pH was an important predictor variable for explaining species distribution and community composition of the mountain plants considered in our study. pH allowed more precise predictions for acidophilic species. This variable should not be neglected in the construction of species distribution models in areas with contrasting edaphic conditions.

Candida species distribution and antifungal susceptibility testing according to European Committee on Antimicrobial Susceptibility Testing and new vs. old Clinical and Laboratory Standards Institute clinical breakpoints: a 6-year prospective candidaemia survey from the fungal infection network of Switzerland.

We analyzed the species distribution of Candida blood isolates (CBIs), prospectively collected between 2004 and 2009 within FUNGINOS, and compared their antifungal susceptibility according to clinical breakpoints defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) in 2013, and the Clinical and Laboratory Standards Institute (CLSI) in 2008 (old CLSI breakpoints) and 2012 (new CLSI breakpoints). CBIs were tested for susceptiblity to fluconazole, voriconazole and caspofungin by microtitre broth dilution (Sensititre(®) YeastOne? test panel). Of 1090 CBIs, 675 (61.9%) were C. albicans, 191 (17.5%) C. glabrata, 64 (5.9%) C. tropicalis, 59 (5.4%) C. parapsilosis, 33 (3%) C. dubliniensis, 22 (2%) C. krusei and 46 (4.2%) rare Candida species. Independently of the breakpoints applied, C. albicans was almost uniformly (>98%) susceptible to all three antifungal agents. In contrast, the proportions of fluconazole- and voriconazole-susceptible C. tropicalis and F-susceptible C. parapsilosis were lower according to EUCAST/new CLSI breakpoints than to the old CLSI breakpoints. For caspofungin, non-susceptibility occurred mainly in C. krusei (63.3%) and C. glabrata (9.4%). Nine isolates (five C. tropicalis, three C. albicans and one C. parapsilosis) were cross-resistant to azoles according to EUCAST breakpoints, compared with three isolates (two C. albicans and one C. tropicalis) according to new and two (2 C. albicans) according to old CLSI breakpoints. Four species (C. albicans, C. glabrata, C. tropicalis and C. parapsilosis) represented >90% of all CBIs. In vitro resistance to fluconazole, voriconazole and caspofungin was rare among C. albicans, but an increase of non-susceptibile isolates was observed among C. tropicalis/C. parapsilosis for the azoles and C. glabrata/C. krusei for caspofungin according to EUCAST and new CLSI breakpoints compared with old CLSI breakpoints.

Five (or so) challenges for species distribution modelling

Species distribution modelling is central to both fundamental and applied research in biogeography. Despite widespread use of models, there are still important conceptual ambiguities as well as biotic and algorithmic uncertainties that need to be investigated in order to increase confidence in model results. We identify and discuss five areas of enquiry that are of high importance for species distribution modelling: (1) clarification of the niche concept; (2) improved designs for sampling data for building models; (3) improved parameterization; (4) improved model selection and predictor contribution; and (5) improved model evaluation. The challenges discussed in this essay do not preclude the need for developments of other areas of research in this field. However, they are critical for allowing the science of species distribution modelling to move forward.

Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy.

Remote sensing using airborne imaging spectroscopy (AIS) is known to retrieve fundamental optical properties of ecosystems. However, the value of these properties for predicting plant species distribution remains unclear. Here, we assess whether such data can add value to topographic variables for predicting plant distributions in French and Swiss alpine grasslands. We fitted statistical models with high spectral and spatial resolution reflectance data and tested four optical indices sensitive to leaf chlorophyll content, leaf water content and leaf area index. We found moderate added-value of AIS data for predicting alpine plant species distribution. Contrary to expectations, differences between species distribution models (SDMs) were not linked to their local abundance or phylogenetic/functional similarity. Moreover, spectral signatures of species were found to be partly site-specific. We discuss current limits of AIS-based SDMs, highlighting issues of scale and informational content of AIS data.

Accuracy of plant assemblage predictions from species distribution models varies along environmental gradients

Aim: Climatic niche modelling of species and community distributions implicitly assumes strong and constant climatic determinism across geographic space. This assumption had however never been tested so far. We tested it by assessing how stacked-species distribution models (S-SDMs) perform for predicting plant species assemblages along elevation. Location: Western Swiss Alps. Methods: Using robust presence-absence data, we first assessed the ability of topo-climatic S-SDMs to predict plant assemblages in a study area encompassing a 2800 m wide elevation gradient. We then assessed the relationships among several evaluation metrics and trait-based tests of community assembly rules. Results: The standard errors of individual SDMs decreased significantly towards higher elevations. Overall, the S-SDM overpredicted far more than they underpredicted richness and could not reproduce the humpback curve along elevation. Overprediction was greater at low and mid-range elevations in absolute values but greater at high elevations when standardised by the actual richness. Looking at species composition, the evaluation metrics accounting for both the presence and absence of species (overall prediction success and kappa) or focusing on correctly predicted absences (specificity) increased with increasing elevation, while the metrics focusing on correctly predicted presences (Jaccard index and sensitivity) decreased. The best overall evaluation - as driven by specificity - occurred at high elevation where species assemblages were shown to be under significant environmental filtering of small plants. In contrast, the decreased overall accuracy in the lowlands was associated with functional patterns representing any type of assembly rule (environmental filtering, limiting similarity or null assembly). Main Conclusions: Our study reveals interesting patterns of change in S-SDM errors with changes in assembly rules along elevation. Yet, significant levels of assemblage prediction errors occurred throughout the gradient, calling for further improvement of SDMs, e.g., by adding key environmental filters that act at fine scales and developing approaches to account for variations in the influence of predictors along environmental gradients.