Predicting the impacts of climate change on the distribution and conservation of endemic forest land snails of Madeira Island

Climate change is emerging as one of the major threats to natural communities of the world’s ecosystems; and biodiversity hotspots, such as Madeira Island, might face a challenging future in the conservation of endangered land snails’ species. With this thesis, progresses have been made in order to properly understand the impact of climate on these vulnerable taxa; and species distribution models coupled with GIS and climate change scenarios have become crucial to understand the relations between species distribution and environmental conditions, identifying threats and determining biodiversity vulnerability. With the use of MaxEnt, important changes in the species suitable areas were obtained. Laurel forest species, highly dependent on precipitation and relative humidity, may face major losses on their future suitable areas, leading to the possible extinction of several endangered species, such as Leiostyla heterodon. Despite the complexity of the biological systems, the intrinsic uncertainty of species distribution models and the lack of information about land snails’ functional traits, this analysis contributed to a pioneer study on the impacts of climate change on endemic species of Madeira Island. The future inclusion of predictions of the effect of climate change on species distribution as part of IUCN assessments could contribute to species prioritizing, promoting specific management actions and maximizing conservation investment.

Climate and land-use changes effects on the distribution of a regional endemism: Melanophryniscus sanmartini (Amphibia, Bufonidae)

ABSTRACT Amphibians are the most threatened vertebrate group according to the IUCN. Land-use and land cover change (LULCC) and climate change (CC) are two of the main factors related to declining amphibian populations. Given the vulnerability of threatened and rare species, the study of their response to these impacts is a conservation priority. The aim of this work was to analyze the combined impact of LULCC and CC on the regionally endemic species Melanophryniscus sanmartini Klappenbach, 1968. This species is currently categorized as near threatened by the IUCN, and previous studies suggest negative effects of projected changes in climate. Using maximum entropy methods we modeled the effects of CC on the current and mid-century distribution of M. sanmartini under two IPCC scenarios - A2 (severe) and B2 (moderate). The effects of LULCC were studied by superimposing the potential distribution with current land use, while future distribution models were evaluated under the scenario of maximum expansion of soybean and afforestation in Uruguay. The results suggest that M. sanmartini is distributed in eastern Uruguay and the south of Brazil, mainly related to hilly and grasslands systems. Currently more than 10% of this species' distribution is superimposed by agricultural crops and exotic forest plantations. Contrasting with a recent modelling study our models suggest an expansion of the distribution of M. sanmartini by mid-century under both climate scenarios. However, despite the rise in climatically suitable areas for the species in the future, LULCC projections indicate that the proportion of modified habitats will occupy up to 25% of the distribution of M. sanmartini. Future change in climate conditions could represent an opportunity for M. sanmartini, but management measures are needed to mitigate the effects of habitat modification in order to ensure its survival and allow the eventual expansion of its distribution.

Climate-based empirical models show biased predictions of butterfly communities along environmental gradients

A better understanding of the factors that mould ecological community structure is required to accurately predict community composition and to anticipate threats to ecosystems due to global changes. We tested how well stacked climate-based species distribution models (S-SDMs) could predict butterfly communities in a mountain region. It has been suggested that climate is the main force driving butterfly distribution and community structure in mountain environments, and that, as a consequence, climate-based S-SDMs should yield unbiased predictions. In contrast to this expectation, at lower altitudes, climate-based S-SDMs overpredicted butterfly species richness at sites with low plant species richness and underpredicted species richness at sites with high plant species richness. According to two indices of composition accuracy, the Sorensen index and a matching coefficient considering both absences and presences, S-SDMs were more accurate in plant-rich grasslands. Butterflies display strong and often specialised trophic interactions with plants. At lower altitudes, where land use is more intense, considering climate alone without accounting for land use influences on grassland plant richness leads to erroneous predictions of butterfly presences and absences. In contrast, at higher altitudes, where climate is the main force filtering communities, there were fewer differences between observed and predicted butterfly richness. At high altitudes, even if stochastic processes decrease the accuracy of predictions of presence, climate-based S-SDMs are able to better filter out butterfly species that are unable to cope with severe climatic conditions, providing more accurate predictions of absences. Our results suggest that predictions should account for plants in disturbed habitats at lower altitudes but that stochastic processes and heterogeneity at high altitudes may limit prediction success of climate-based S-SDMs.

Horizontal, but not vertical, biotic interactions affect fine-scale plant distribution patterns in a low-energy system

Studies of species range determinants have traditionally focused on abiotic variables (typically climatic conditions), and therefore the recent explicit consideration of biotic interactions represents an important advance in the field. While these studies clearly support the role of biotic interactions in shaping species distributions, most examine only the influence of a single species and/or a single interaction, failing to account for species being subject to multiple concurrent interactions. By fitting species distribution models (SDMs), we examine the influence of multiple vertical (i.e., grazing, trampling, and manuring by mammalian herbivores) and horizontal (i.e., competition and facilitation; estimated from the cover of dominant plant species) interspecific interactions on the occurrence and cover of 41 alpine tundra plant species. Adding plant-plant interactions to baseline SDMs (using five field-quantified abiotic variables) significantly improved models' predictive power for independent data, while herbivore-related variables had only a weak influence. Overall, abiotic variables had the strongest individual contributions to the distribution of alpine tundra plants, with the importance of horizontal interaction variables exceeding that of vertical interaction variables. These results were consistent across three modeling techniques, for both species occurrence and cover, demonstrating the pattern to be robust. Thus, the explicit consideration of multiple biotic interactions reveals that plant-plant interactions exert control over the fine-scale distribution of vascular species that is comparable to abiotic drivers and considerably stronger than herbivores in this low-energy system.

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling.

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km(2) to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.

Bioclimatic constraints to Andean cat distribution: a modelling application for rare species

AimTo identify the bioclimatic niche of the endangered Andean cat (Leopardus jacobita), one of the rarest and least known felids in the world, by developing a species distribution model.LocationSouth America, High Andes and Patagonian steppe. Peru, Bolivia, Chile, Argentina.MethodsWe used 108 Andean cat records to build the models, and 27 to test them, applying the Maxent algorithm to sets of uncorrelated bioclimatic variables from global databases, including elevation. We based our biogeographical interpretations on the examination of the predicted geographic range, the modelled response curves and latitudinal variations in climatic variables associated with the locality data.ResultsSimple bioclimatic models for Andean cats were highly predictive with only 3-4 explanatory variables. The climatic niche of the species was defined by extreme diurnal variations in temperature, cold minimum and moderate maximum temperatures, and aridity, characteristic not only of the Andean highlands but also of the Patagonian steppe. Argentina had the highest representation of suitable climates, and Chile the lowest. The most favourable conditions were centrally located and spanned across international boundaries. Discontinuities in suitable climatic conditions coincided with three biogeographical barriers associated with climatic or topographic transitions.Main conclusionsSimple bioclimatic models can produce useful predictions of suitable climatic conditions for rare species, including major biogeographical constraints. In our study case, these constraints are also known to affect the distribution of other Andean species and the genetic structure of Andean cat populations. We recommend surveys of areas with suitable climates and no Andean cat records, including the corridor connecting two core populations. The inclusion of landscape variables at finer scales, crucially the distribution of Andean cat prey, would contribute to refine our predictions for conservation applications.

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.

A probabilistic approach to niche-based community models for spatial forecasts of assemblage properties and their uncertainties

Aim Conservation strategies are in need of predictions that capture spatial community composition and structure. Currently, the methods used to generate these predictions generally focus on deterministic processes and omit important stochastic processes and other unexplained variation in model outputs. Here we test a novel approach of community models that accounts for this variation and determine how well it reproduces observed properties of alpine butterfly communities. Location The western Swiss Alps. Methods We propose a new approach to process probabilistic predictions derived from stacked species distribution models (S-SDMs) in order to predict and assess the uncertainty in the predictions of community properties. We test the utility of our novel approach against a traditional threshold-based approach. We used mountain butterfly communities spanning a large elevation gradient as a case study and evaluated the ability of our approach to model species richness and phylogenetic diversity of communities. Results S-SDMs reproduced the observed decrease in phylogenetic diversity and species richness with elevation, syndromes of environmental filtering. The prediction accuracy of community properties vary along environmental gradient: variability in predictions of species richness was higher at low elevation, while it was lower for phylogenetic diversity. Our approach allowed mapping the variability in species richness and phylogenetic diversity projections. Main conclusion Using our probabilistic approach to process species distribution models outputs to reconstruct communities furnishes an improved picture of the range of possible assemblage realisations under similar environmental conditions given stochastic processes and help inform manager of the uncertainty in the modelling results

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.

Using niche-based models to improve the sampling of rare species

Because data on rare species usually are sparse, it is important to have efficient ways to sample additional data. Traditional sampling approaches are of limited value for rare species because a very large proportion of randomly chosen sampling sites are unlikely to shelter the species. For these species, spatial predictions from niche-based distribution models can be used to stratify the sampling and increase sampling efficiency. New data sampled are then used to improve the initial model. Applying this approach repeatedly is an adaptive process that may allow increasing the number of new occurrences found. We illustrate the approach with a case study of a rare and endangered plant species in Switzerland and a simulation experiment. Our field survey confirmed that the method helps in the discovery of new populations of the target species in remote areas where the predicted habitat suitability is high. In our simulations the model-based approach provided a significant improvement (by a factor of 1.8 to 4 times, depending on the measure) over simple random sampling. In terms of cost this approach may save up to 70% of the time spent in the field.

Providing more informative projections of climate change impact on plant distribution in a mountain environment

Summary Due to their conic shape and the reduction of area with increasing elevation, mountain ecosystems were early identified as potentially very sensitive to global warming. Moreover, mountain systems may experience unprecedented rates of warming during the next century, two or three times higher than that records of the 20th century. In this context, species distribution models (SDM) have become important tools for rapid assessment of the impact of accelerated land use and climate change on the distribution plant species. In my study, I developed and tested new predictor variables for species distribution models (SDM), specific to current and future geographic projections of plant species in a mountain system, using the Western Swiss Alps as model region. Since meso- and micro-topography are relevant to explain geographic patterns of plant species in mountain environments, I assessed the effect of scale on predictor variables and geographic projections of SDM. I also developed a methodological framework of space-for-time evaluation to test the robustness of SDM when projected in a future changing climate. Finally, I used a cellular automaton to run dynamic simulations of plant migration under climate change in a mountain landscape, including realistic distance of seed dispersal. Results of future projections for the 21st century were also discussed in perspective of vegetation changes monitored during the 20th century. Overall, I showed in this study that, based on the most severe A1 climate change scenario and realistic dispersal simulations of plant dispersal, species extinctions in the Western Swiss Alps could affect nearly one third (28.5%) of the 284 species modeled by 2100. With the less severe 61 scenario, only 4.6% of species are predicted to become extinct. However, even with B1, 54% (153 species) may still loose more than 80% of their initial surface. Results of monitoring of past vegetation changes suggested that plant species can react quickly to the warmer conditions as far as competition is low However, in subalpine grasslands, competition of already present species is probably important and limit establishment of newly arrived species. Results from future simulations also showed that heavy extinctions of alpine plants may start already in 2040, but the latest in 2080. My study also highlighted the importance of fine scale and regional. assessments of climate change impact on mountain vegetation, using more direct predictor variables. Indeed, predictions at the continental scale may fail to predict local refugees or local extinctions, as well as loss of connectivity between local populations. On the other hand, migrations of low-elevation species to higher altitude may be difficult to predict at the local scale. Résumé La forme conique des montagnes ainsi que la diminution de surface dans les hautes altitudes sont reconnues pour exposer plus sensiblement les écosystèmes de montagne au réchauffement global. En outre, les systèmes de montagne seront sans doute soumis durant le 21ème siècle à un réchauffement deux à trois fois plus rapide que celui mesuré durant le 20ème siècle. Dans ce contexte, les modèles prédictifs de distribution géographique de la végétation se sont imposés comme des outils puissants pour de rapides évaluations de l'impact des changements climatiques et de la transformation du paysage par l'homme sur la végétation. Dans mon étude, j'ai développé de nouvelles variables prédictives pour les modèles de distribution, spécifiques à la projection géographique présente et future des plantes dans un système de montagne, en utilisant les Préalpes vaudoises comme zone d'échantillonnage. La méso- et la microtopographie étant particulièrement adaptées pour expliquer les patrons de distribution géographique des plantes dans un environnement montagneux, j'ai testé les effets d'échelle sur les variables prédictives et sur les projections des modèles de distribution. J'ai aussi développé un cadre méthodologique pour tester la robustesse potentielle des modèles lors de projections pour le futur. Finalement, j'ai utilisé un automate cellulaire pour simuler de manière dynamique la migration future des plantes dans le paysage et dans quatre scénarios de changement climatique pour le 21ème siècle. J'ai intégré dans ces simulations des mécanismes et des distances plus réalistes de dispersion de graines. J'ai pu montrer, avec les simulations les plus réalistes, que près du tiers des 284 espèces considérées (28.5%) pourraient être menacées d'extinction en 2100 dans le cas du plus sévère scénario de changement climatique A1. Pour le moins sévère des scénarios B1, seulement 4.6% des espèces sont menacées d'extinctions, mais 54% (153 espèces) risquent de perdre plus 80% de leur habitat initial. Les résultats de monitoring des changements de végétation dans le passé montrent que les plantes peuvent réagir rapidement au réchauffement climatique si la compétition est faible. Dans les prairies subalpines, les espèces déjà présentes limitent certainement l'arrivée de nouvelles espèces par effet de compétition. Les résultats de simulation pour le futur prédisent le début d'extinctions massives dans les Préalpes à partir de 2040, au plus tard en 2080. Mon travail démontre aussi l'importance d'études régionales à échelle fine pour évaluer l'impact des changements climatiques sur la végétation, en intégrant des variables plus directes. En effet, les études à échelle continentale ne tiennent pas compte des micro-refuges, des extinctions locales ni des pertes de connectivité entre populations locales. Malgré cela, la migration des plantes de basses altitudes reste difficile à prédire à l'échelle locale sans modélisation plus globale.

Use of niche models in invasive species risk assessments.

Risk maps summarizing landscape suitability of novel areas for invading species can be valuable tools for preventing species' invasions or controlling their spread, but methods employed for development of such maps remain variable and unstandardized. We discuss several considerations in development of such models, including types of distributional information that should be used, the nature of explanatory variables that should be incorporated, and caveats regarding model testing and evaluation. We highlight that, in the case of invasive species, such distributional predictions should aim to derive the best hypothesis of the potential distribution of the species by using (1) all distributional information available, including information from both the native range and other invaded regions; (2) predictors linked as directly as is feasible to the physiological requirements of the species; and (3) modelling procedures that carefully avoid overfitting to the training data. Finally, model testing and evaluation should focus on well-predicted presences, and less on efficient prediction of absences; a k-fold regional cross-validation test is discussed.

A multi-temporal approach to model endangered species distribution in Europe: the case of the Eurasian otter in Italy

We analysed the relationship between changes in land cover patterns and the Eurasian otter occurrence over the course of about 20 years (1985-2006) using multi-temporal Species Distribution Models (SDMs). The study area includes five river catchments covering most of the otter's Italian range. Land cover and topographic data were used as proxies of the ecological requirements of the otter within a 300-m buffer around river courses. We used species presence, pseudo-absence data, and environmental predictors to build past (1985) and current (2006) SDMs by applying an ensemble procedure through the BIOMOD modelling package. The performance of each model was evaluated by measuring the area under the curve (AUC) of the receiver-operating characteristic (ROC). Multi-temporal analyses of species distribution and land cover maps were performed by comparing the maps produced for 1985 and 2006. The ensemble procedure provided a good overall modelling accuracy, revealing that elevation and slope affected the otter's distribution in the past; in contrast, land cover predictors, such as cultivations and forests, were more important in the present period. During the transition period, 20.5% of the area became suitable, with 76% of the new otter presence data being located in these newly available areas. The multi-temporal analysis suggested that the quality of otter habitat improved in the last 20 years owing to the expansion of forests and to the reduction of cultivated fields in riparian belts. The evidence presented here stresses the great potential of riverine habitat restoration and environmental management for the future expansion of the otter in Italy

Measuring the relative effect of factors affecting species distribution model predictions

1. Species distribution models are increasingly used to address conservation questions, so their predictive capacity requires careful evaluation. Previous studies have shown how individual factors used in model construction can affect prediction. Although some factors probably have negligible effects compared to others, their relative effects are largely unknown. 2. We introduce a general "virtual ecologist" framework to study the relative importance of factors involved in the construction of species distribution models. 3. We illustrate the framework by examining the relative importance of five key factors-a missing covariate, spatial autocorrelation due to a dispersal process in presences/absences, sample size, sampling design and modeling technique-in a real study framework based on plants in a mountain landscape at regional scale, and show that, for the parameter values considered here, most of the variation in prediction accuracy is due to sample size and modeling technique. Contrary to repeatedly reported concerns, spatial autocorrelation has only comparatively small effects. 4. This study shows the importance of using a nested statistical framework to evaluate the relative effects of factors that may affect species distribution models.

Biotic interactions boost spatial models of species richness

Biotic interactions are known to affect the composition of species assemblages via several mechanisms, such as competition and facilitation. However, most spatial models of species richness do not explicitly consider inter-specific interactions. Here, we test whether incorporating biotic interactions into high-resolution models alters predictions of species richness as hypothesised. We included key biotic variables (cover of three dominant arctic-alpine plant species) into two methodologically divergent species richness modelling frameworks - stacked species distribution models (SSDM) and macroecological models (MEM) - for three ecologically and evolutionary distinct taxonomic groups (vascular plants, bryophytes and lichens). Predictions from models including biotic interactions were compared to the predictions of models based on climatic and abiotic data only. Including plant-plant interactions consistently and significantly lowered bias in species richness predictions and increased predictive power for independent evaluation data when compared to the conventional climatic and abiotic data based models. Improvements in predictions were constant irrespective of the modelling framework or taxonomic group used. The global biodiversity crisis necessitates accurate predictions of how changes in biotic and abiotic conditions will potentially affect species richness patterns. Here, we demonstrate that models of the spatial distribution of species richness can be improved by incorporating biotic interactions, and thus that these key predictor factors must be accounted for in biodiversity forecasts