Overcoming limitations of modelling rare species by using ensembles of small models

1. Species distribution models (SDMs) have become a standard tool in ecology and applied conservation biology. Modelling rare and threatened species is particularly important for conservation purposes. However, modelling rare species is difficult because the combination of few occurrences and many predictor variables easily leads to model overfitting. A new strategy using ensembles of small models was recently developed in an attempt to overcome this limitation of rare species modelling and has been tested successfully for only a single species so far. Here, we aim to test the approach more comprehensively on a large number of species including a transferability assessment. 2. For each species numerous small (here bivariate) models were calibrated, evaluated and averaged to an ensemble weighted by AUC scores. These 'ensembles of small models' (ESMs) were compared to standard Species Distribution Models (SDMs) using three commonly used modelling techniques (GLM, GBM, Maxent) and their ensemble prediction. We tested 107 rare and under-sampled plant species of conservation concern in Switzerland. 3. We show that ESMs performed significantly better than standard SDMs. The rarer the species, the more pronounced the effects were. ESMs were also superior to standard SDMs and their ensemble when they were independently evaluated using a transferability assessment. 4. By averaging simple small models to an ensemble, ESMs avoid overfitting without losing explanatory power through reducing the number of predictor variables. They further improve the reliability of species distribution models, especially for rare species, and thus help to overcome limitations of modelling rare species.

Assessing the use of very high resolution data to predict species distribution in a mountain environment

Nowadays, Species Distribution Models (SDMs) are a widely used tool. Using different statistical approaches these models reconstruct the realized niche of a species using presence data and a set of variables, often topoclimatic. There utilization range is quite large from understanding single species requirements, to the creation of nature reserve based on species hotspots, or modeling of climate change impact, etc... Most of the time these models are using variables at a resolution of 50km x 50km or 1 km x 1 km. However in some cases these models are used with resolutions below the kilometer scale and thus called high resolution models (100 m x 100 m or 25 m x 25 m). Quite recently a new kind of data has emerged enabling precision up to lm x lm and thus allowing very high resolution modeling. However these new variables are very costly and need an important amount of time to be processed. This is especially the case when these variables are used in complex calculation like models projections over large areas. Moreover the importance of very high resolution data in SDMs has not been assessed yet and is not well understood. Some basic knowledge on what drive species presence-absences is still missing. Indeed, it is not clear whether in mountain areas like the Alps coarse topoclimatic gradients are driving species distributions or if fine scale temperature or topography are more important or if their importance can be neglected when balance to competition or stochasticity. In this thesis I investigated the importance of very high resolution data (2-5m) in species distribution models using either very high resolution topographic, climatic or edaphic variables over a 2000m elevation gradient in the Western Swiss Alps. I also investigated more local responses of these variables for a subset of species living in this area at two precise elvation belts. During this thesis I showed that high resolution data necessitates very good datasets (species and variables for the models) to produce satisfactory results. Indeed, in mountain areas, temperature is the most important factor driving species distribution and needs to be modeled at very fine resolution instead of being interpolated over large surface to produce satisfactory results. Despite the instinctive idea that topographic should be very important at high resolution, results are mitigated. However looking at the importance of variables over a large gradient buffers the importance of the variables. Indeed topographic factors have been shown to be highly important at the subalpine level but their importance decrease at lower elevations. Wether at the mountane level edaphic and land use factors are more important high resolution topographic data is more imporatant at the subalpine level. Finally the biggest improvement in the models happens when edaphic variables are added. Indeed, adding soil variables is of high importance and variables like pH are overpassing the usual topographic variables in SDMs in term of importance in the models. To conclude high resolution is very important in modeling but necessitate very good datasets. Only increasing the resolution of the usual topoclimatic predictors is not sufficient and the use of edaphic predictors has been highlighted as fundamental to produce significantly better models. This is of primary importance, especially if these models are used to reconstruct communities or as basis for biodiversity assessments. -- Ces dernières années, l'utilisation des modèles de distribution d'espèces (SDMs) a continuellement augmenté. Ces modèles utilisent différents outils statistiques afin de reconstruire la niche réalisée d'une espèce à l'aide de variables, notamment climatiques ou topographiques, et de données de présence récoltées sur le terrain. Leur utilisation couvre de nombreux domaines allant de l'étude de l'écologie d'une espèce à la reconstruction de communautés ou à l'impact du réchauffement climatique. La plupart du temps, ces modèles utilisent des occur-rences issues des bases de données mondiales à une résolution plutôt large (1 km ou même 50 km). Certaines bases de données permettent cependant de travailler à haute résolution, par conséquent de descendre en dessous de l'échelle du kilomètre et de travailler avec des résolutions de 100 m x 100 m ou de 25 m x 25 m. Récemment, une nouvelle génération de données à très haute résolution est apparue et permet de travailler à l'échelle du mètre. Les variables qui peuvent être générées sur la base de ces nouvelles données sont cependant très coûteuses et nécessitent un temps conséquent quant à leur traitement. En effet, tout calcul statistique complexe, comme des projections de distribution d'espèces sur de larges surfaces, demande des calculateurs puissants et beaucoup de temps. De plus, les facteurs régissant la distribution des espèces à fine échelle sont encore mal connus et l'importance de variables à haute résolution comme la microtopographie ou la température dans les modèles n'est pas certaine. D'autres facteurs comme la compétition ou la stochasticité naturelle pourraient avoir une influence toute aussi forte. C'est dans ce contexte que se situe mon travail de thèse. J'ai cherché à comprendre l'importance de la haute résolution dans les modèles de distribution d'espèces, que ce soit pour la température, la microtopographie ou les variables édaphiques le long d'un important gradient d'altitude dans les Préalpes vaudoises. J'ai également cherché à comprendre l'impact local de certaines variables potentiellement négligées en raison d'effets confondants le long du gradient altitudinal. Durant cette thèse, j'ai pu monter que les variables à haute résolution, qu'elles soient liées à la température ou à la microtopographie, ne permettent qu'une amélioration substantielle des modèles. Afin de distinguer une amélioration conséquente, il est nécessaire de travailler avec des jeux de données plus importants, tant au niveau des espèces que des variables utilisées. Par exemple, les couches climatiques habituellement interpolées doivent être remplacées par des couches de température modélisées à haute résolution sur la base de données de terrain. Le fait de travailler le long d'un gradient de température de 2000m rend naturellement la température très importante au niveau des modèles. L'importance de la microtopographie est négligeable par rapport à la topographie à une résolution de 25m. Cependant, lorsque l'on regarde à une échelle plus locale, la haute résolution est une variable extrêmement importante dans le milieu subalpin. À l'étage montagnard par contre, les variables liées aux sols et à l'utilisation du sol sont très importantes. Finalement, les modèles de distribution d'espèces ont été particulièrement améliorés par l'addition de variables édaphiques, principalement le pH, dont l'importance supplante ou égale les variables topographique lors de leur ajout aux modèles de distribution d'espèces habituels.

Scientific journals in Brazil and Spain: Alternative publishing models

This paper describes high-quality journals in Brazil and Spain, with an emphasis on the distribution models used. It presents the general characteristics (age, type of publisher, and theme) and analyzes the distribution model by studying the type of format (print or digital), the type of access (open access or subscription), and the technology platform used. The 549 journals analyzed (249 in Brazil and 300 in Spain) are included in the 2011 Web of Science (WoS) and Scopus databases. Data on each journal were collected directly from their websites between March and October 2012. Brazil has a fully open access distribution model (97%) in which few journals require payment by authors thanks to cultural, financial, operational, and technological support provided by public agencies. In Spain, open access journals account for 55% of the total and have also received support from public agencies, although to a lesser extent. These results show that there are systems support of open access in scientific journals other than the"author pays" model advocated by the Finch report for the United Kingdom.

Effects of global climate changes on geographical distribution patterns of economically important plant species in cerrado

Different climate models, modeling methods and carbon emission scenarios were used in this paper to evaluate the effects of future climate changes on geographical distribution of species of economic and cultural importance across the Cerrado biome. As the results of several studies have shown, there are still many uncertainties associated with these projections, although bioclimatic models are still widely used and effective method to evaluate the consequences for biodiversity of these climate changes. In this article, it was found that 90% of these uncertainties are related to methods of modeling, although, regardless of the uncertainties, the results revealed that the studied species will reduce about 78% of their geographic distribution in Cerrado. For an effective work on the conservation of these species, many studies still need to be carried out, although it is already possible to observe that climate change will have a strong influence on the pattern of distribution of these species.

Modeling a spatially restricted distribution in the Neotropics: How the size of calibration area affects the performance of five presence-only methods

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Recent and future environmental suitability to dengue fever in Brazil using species distribution model

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Implementing new northernmost records to modelling the distribution of Hypsiboas caingua (Anura: Hylidae) in South America

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Species Distribution Modeling for Conservation Purposes

Species distribution models (SDMs) can be useful for different conservation purposes. We discuss the importance of fitting spatial scale and using current records and relevant predictors aiming conservation. We choose jaguar (Panthera onca) as a target species and Brazil and Atlantic Forest biome as study areas. We tested two different extents (continent and biome) and resolutions (similar to 4 Km and similar to 1 Km) in Maxent with 186 records and 11 predictors (bioclimatic, elevation, land-use and landscape structure). All models presented satisfactory AUC values (>0.70) and low omission errors (<23%). SDMs were scale-sensitive as the use of reduced extent implied in significant gains to model performance generating more constrained and real predictive distribution maps. Continental-scale models performed poorly in predicting potential current jaguar distribution, but they reached the historic distribution. Specificity increased significantly from coarse to finer-scale models due to the reduction of overprediction. The variability of environmental space (E-space) differed for most of climatic variables between continental and biome-scale and the representation of the E-space by predictors differed significantly (t = 2.42, g.I. = 9, P < 0.05). Refining spatial scale, incorporating landscape variables and improving the quality of biological data are essential for improving model prediction for conservation purposes.

Ensemble prediction distribution maps of macroalgae for current conditions and four climate change scenarios and high resultion bathymetry for Potter Cove, WAP, Antarctica

Species distribution models (SDM) predict species occurrence based on statistical relationships with environmental conditions. The R-package biomod2 which includes 10 different SDM techniques and 10 different evaluation methods was used in this study. Macroalgae are the main biomass producers in Potter Cove, King George Island (Isla 25 de Mayo), Antarctica, and they are sensitive to climate change factors such as suspended particulate matter (SPM). Macroalgae presence and absence data were used to test SDMs suitability and, simultaneously, to assess the environmental response of macroalgae as well as to model four scenarios of distribution shifts by varying SPM conditions due to climate change. According to the averaged evaluation scores of Relative Operating Characteristics (ROC) and True scale statistics (TSS) by models, those methods based on a multitude of decision trees such as Random Forest and Classification Tree Analysis, reached the highest predictive power followed by generalized boosted models (GBM) and maximum-entropy approaches (Maxent). The final ensemble model used 135 of 200 calculated models (TSS > 0.7) and identified hard substrate and SPM as the most influencing parameters followed by distance to glacier, total organic carbon (TOC), bathymetry and slope. The climate change scenarios show an invasive reaction of the macroalgae in case of less SPM and a retreat of the macroalgae in case of higher assumed SPM values.

Projecting Changes in the Distribution and Productivity of Living Marine Resources: A Critical Review of the Suite of Modeling Approaches used in the Large European Project VECTORS

We review and compare four broad categories of spatially-explicit modelling approaches currently used to understand and project changes in the distribution and productivity of living marine resources including: 1) statistical species distribution models, 2) physiology-based, biophysical models of single life stages or the whole life cycle of species, 3) food web models, and 4) end-to-end models. Single pressures are rare and, in the future, models must be able to examine multiple factors affecting living marine resources such as interactions between: i) climate-driven changes in temperature regimes and acidification, ii) reductions in water quality due to eutrophication, iii) the introduction of alien invasive species, and/or iv) (over-)exploitation by fisheries. Statistical (correlative) approaches can be used to detect historical patterns which may not be relevant in the future. Advancing predictive capacity of changes in distribution and productivity of living marine resources requires explicit modelling of biological and physical mechanisms. New formulations are needed which (depending on the question) will need to strive for more realism in ecophysiology and behaviour of individuals, life history strategies of species, as well as trophodynamic interactions occurring at different spatial scales. Coupling existing models (e.g. physical, biological, economic) is one avenue that has proven successful. However, fundamental advancements are needed to address key issues such as the adaptive capacity of species/groups and ecosystems. The continued development of end-to-end models (e.g., physics to fish to human sectors) will be critical if we hope to assess how multiple pressures may interact to cause changes in living marine resources including the ecological and economic costs and trade-offs of different spatial management strategies. Given the strengths and weaknesses of the various types of models reviewed here, confidence in projections of changes in the distribution and productivity of living marine resources will be increased by assessing model structural uncertainty through biological ensemble modelling.

Projecting Changes in the Distribution and Productivity of Living Marine Resources: A Critical Review of the Suite of Modeling Approaches used in the Large European Project VECTORS

We review and compare four broad categories of spatially-explicit modelling approaches currently used to understand and project changes in the distribution and productivity of living marine resources including: 1) statistical species distribution models, 2) physiology-based, biophysical models of single life stages or the whole life cycle of species, 3) food web models, and 4) end-to-end models. Single pressures are rare and, in the future, models must be able to examine multiple factors affecting living marine resources such as interactions between: i) climate-driven changes in temperature regimes and acidification, ii) reductions in water quality due to eutrophication, iii) the introduction of alien invasive species, and/or iv) (over-)exploitation by fisheries. Statistical (correlative) approaches can be used to detect historical patterns which may not be relevant in the future. Advancing predictive capacity of changes in distribution and productivity of living marine resources requires explicit modelling of biological and physical mechanisms. New formulations are needed which (depending on the question) will need to strive for more realism in ecophysiology and behaviour of individuals, life history strategies of species, as well as trophodynamic interactions occurring at different spatial scales. Coupling existing models (e.g. physical, biological, economic) is one avenue that has proven successful. However, fundamental advancements are needed to address key issues such as the adaptive capacity of species/groups and ecosystems. The continued development of end-to-end models (e.g., physics to fish to human sectors) will be critical if we hope to assess how multiple pressures may interact to cause changes in living marine resources including the ecological and economic costs and trade-offs of different spatial management strategies. Given the strengths and weaknesses of the various types of models reviewed here, confidence in projections of changes in the distribution and productivity of living marine resources will be increased by assessing model structural uncertainty through biological ensemble modelling.

Using niche modelling of indicator species to predict the distribution of xerophitic shrub dune communities in South-Western Portugal

Inland sand dune systems are amongst the most threatened habitat types of Europe. Affected by severe conditions, these habitats present distinct community compositions, which makes them excellent for studying possible interactions among their integrating species and the environment. We focus on understanding the distribution and cooccurrence of the species from dune plant assemblages as a key step for the adequate protection of these habitats. Using data from an extensive survey we identified the shrub species that could be considered indicators of the different xerophytic scrub dune communities in South West Portugal. Then, we modelled the responses of these species to the environmental conditions using Ecological Niche Factor Analysis. We present some preliminary results elucidating whether using species distribution models of indicator species at a regional scale is a valid approach to predict the distribution of the different types of communities inhabiting these endangered habitats.

Applying fuzzy logic to comparative distribution modelling: a case study with two sympatric amphibians.

We modelled the distributions of two toads (Bufo bufo and Epidalea calamita) in the Iberian Peninsula using the favourability function, which makes predictions directly comparable for different species and allows fuzzy logic operations to relate different models. The fuzzy intersection between individual models, representing favourability for the presence of both species simultaneously, was compared with another favourability model built on the presences shared by both species. The fuzzy union between individual models, representing favourability for the presence of any of the two species, was compared with another favourabilitymodel based on the presences of either or both of them. The fuzzy intersections between favourability for each species and the complementary of favourability for the other (corresponding to the logical operation “A and not B”) were compared with models of exclusive presence of one species versus the exclusive presence of the other. The results of modelling combined species data were highly similar to those of fuzzy logic operations between individual models, proving fuzzy logic and the favourability function valuable for comparative distribution modelling. We highlight several advantages of fuzzy logic over other forms of combining distribution models, including the possibility to combine multiple species models for management and conservation planning.

Favourable areas for expansion and reintroduction of Iberian lynx accounting for distribution trends and genetic diversity of the European rabbit

Although on a local scale Iberian lynx distribution is determined by the availability of prey rabbits, recent modelling analyses have uncovered broad-scale disagreements between these two species’ distribution trends. These analyses showed also that the lynx had become restricted to only a fraction of the rabbit’s genetic diversity, and that this could be jeopardising its survival in the face of environmental hazards and uncertainty. In the present paper, a follow-up was carried out through the building of lynx and rabbit distribution models based on the most recent Spanish mammal atlas. Environmental favourability values for lynx and rabbit were positively correlated within the lynx’s current distribution area, but they were negatively correlated within the total Spanish area where lynx used to occur in the 1980’s. Environmental favourability for rabbits was significantly higher where lynx maintains reproductive populations than where it recently disappeared, indicating that rabbit favourability plays an important role and can be a good predictor of lynx persistence. The lynx and rabbit models were extrapolated to predict favourable areas for both species in Spain as well as in Portugal, on the original scale of the distribution data (10x10 km) and on a 100 times finer spatial resolution (1x1 km). The lynx and rabbit models were also combined through fuzzy logic to forecast the potential for lynx occurrence incorporating information on favourable areas for its main prey. Several areas are proposed as favourable for lynx expansion or re-introduction,

Use of Coarse-resolution models of species' distributions to guide local conservation inferences

Distribution models are used increasingly for species conservation assessments over extensive areas, but the spatial resolution of the modeled data and, consequently, of the predictions generated directly from these models are usually too coarse for local conservation applications. Comprehensive distribution data at finer spatial resolution, however, require a level of sampling that is impractical for most species and regions. Models can be downscaled to predict distribution at finer resolutions, but this increases uncertainty because the predictive ability of models is not necessarily consistent beyond their original scale. We analyzed the performance of downscaled, previously published models of environmental favorability (a generalized linear modeling technique) for a restricted endemic insectivore, the Iberian desman (Galemys pyrenaicus), and a more widespread carnivore, the Eurasian otter ( Lutra lutra), in the Iberian Peninsula. The models, built from presence–absence data at 10 × 10 km resolution, were extrapolated to a resolution 100 times finer (1 × 1 km). We compared downscaled predictions of environmental quality for the two species with published data on local observations and on important conservation sites proposed by experts. Predictions were significantly related to observed presence or absence of species and to expert selection of sampling sites and important conservation sites. Our results suggest the potential usefulness of downscaled projections of environmental quality as a proxy for expensive and time-consuming field studies when the field studies are not feasible. This method may be valid for other similar species if coarse-resolution distribution data are available to define high-quality areas at a scale that is practical for the application of concrete conservation measures