Spatial analysis improves species distribution modelling during range expansion

Predicting the dispersal and invasion dynamics of ambrosia beetles through demographic reconstruction and process-explicit modeling

Evaluating potential routes of invasion of pathogens and vectors of sanitary importance is essential for planning and decision-making at multiple scales. An effective tool are process-explicit models that allow coupling environmental, demographic and dispersal information to evaluate population growth and range dynamics as a function of the abiotic conditions in a region. In this work we simulate multiple dispersal/invasion routes in Mexico that could be taken by ambrosia beetles and a specific symbiont, Harringtonia lauricola, responsible for a severe epiphytic of Lauraceae in North America. We used Xyleborus bispinatus Eichhoff 1868 as a study subject and estimated its demography in the laboratory in a temperature gradient (17, 20, 26, 29, 35 °C), which we then used to parameterize a process-based model to estimate its metapopulation dynamics. The maximum intrinsic growth rate of X. bispinatus is 0.13 with a thermal optimum of 26.2 °C. The models suggest important regions for the establishment and dispersal the states of Veracruz, Chiapas and Oaxaca (high host and secondary vectors diversity), the Isthmus of Tehuantepec (connectivity region), and Michoacán and Jalisco (important avocado plantations). The use of hybrid process-based models is a promising tool to refine the predictions applied to the study of biological invasions and species distributions.

Range Dynamics of Striped Field Mouse (Apodemus agrarius) in Northern Eurasia under Global Climate Change Based on Ensemble Species Distribution Models

The striped field mouse (Apodemus agrarius Pallas, 1771) is a widespread species in Northern Eurasia. It damages crops and carries zoonotic pathogens. Its current and future range expansion under climate change may negatively affect public health and the economy, warranting further research to understand the ecological and invasive characteristics of the species. In our study, we used seven algorithms (GLM, GAM, GBS, FDA, RF, ANN, and MaxEnt) to develop robust ensemble species distribution models (eSDMs) under current (1970-2000) and future climate conditions derived from global circulation models (GCMs) for 2021-2040, 2041-2060, 2061-2080, and 2081-2100. Simulation of climate change included high-, medium-, and low-sensitivity GCMs under four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). We analyzed the habitat suitability across GCMs and scenarios by constructing geographical ranges and calculating their centroids. The results showed that the range changes depended on both the sensitivity of GCMs and scenario. The main trends were range expansion to the northeast and partial loss of habitat in the steppe area. The striped field mouse may form a continuous range from Central Europe to East Asia, closing the range gap that has existed for 12 thousand years. We present 49 eSDMs for the current and future distribution of A. agrarius (for 2000-2100) with quantitative metrics (gain, loss, change) of the range dynamics under global climate change. The most important predictor variables determining eSDMs are mean annual temperature, mean diurnal range of temperatures, the highest temperature of the warmest month, annual precipitation, and precipitation in the coldest month. These findings could help limit the population of the striped field mouse and predict distribution of the species under global climate change.

The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling

Northern Eurasia is extensive and includes terrestrial and aquatic ecosystems that cover several natural zones and access to the seas of three oceans. As a result, it has been invaded by numerous invasive alien species (IAS) over large temporal and spatial scales. The purpose of this research was to assess invasion trends and construct species distribution models for the Russian TOP-100 most dangerous IAS. Environmentally suitable regions for IAS were established based on alien species attribute databases, datasets of 169,709 species occurrence records (SOR) and raster layers of environmental variables using species distribution modelling (MaxEnt). The objectives of this research were to (1) create databases of SOR for the TOP-100 IAS in Russia; 2) determine pathways, residence time, donor regions and trends of invasions; (3) determine the main types of spatial distributions of invasive species and their relation to residence time; and (4) distinguish regions with the highest richness of IAS that have a strong impact on the terrestrial and aquatic ecosystems of Russia. We found that although species invasions date back over 400 years, the number of naturalized IAS has increased non-linearly over the past 76 years. The TOP-100 list is mainly represented by unintentionally introduced species (62%) which are characterized by different introduction pathways. Species occurrence records revealed that 56 IAS are distributed locally, 26 are distributed regionally and 18 are widespread in Russia. Species with local, regional or widespread distributions were characterized by residence times of 55, 126 or 190 years, respectively. We found that IAS with local distribution can expand their range into suitable regions more extensively (expected increase by 32%) than widespread species (expected increase by only 7%). The procedure of identifying hot/cold spots locations based on SOR allowed us to identify the Russian regions with the highest richness of IAS. Our results and the integrated database that we created provide a framework for studying IAS over large temporal and spatial scales that can be used in the development of management plans for dangerous IAS.

A review of environmental and anthropogenic variables used to model jaguar occurrence

Jaguars (Panthera onca) are a landscape species of conservation importance and our understanding of environmental and anthropogenic drivers of jaguar occurrence is necessary to improve conservation strategies. We reviewed available literature to simply describe environmental and anthropogenic variables used and found to be significant in occurrence modeling. We reviewed 95 documents published from 1980 to 2021 that focused on jaguar occurrence and that used 39 variable types (21 anthropogenic, 18 environmental) among different techniques, scales, and approaches. In general, these variables included both anthropogenic (roads, land use, human activities, and population) and environmental (climate, vegetation, ecological interactions, topographic, water, and others) factors. Twelve variables were identified as affecting jaguar occurrence overall, eleven at local scale and seven at broad scales (regional and continental). Focusing more specifically on the variables that correlate with occurrence should help researchers to make better predictions in areas without quantitative jaguar data.

Impacts of climate change on aquatic insects in temperate alpine regions: Complementary modeling approaches applied to Swiss rivers

Freshwater biodiversity loss is a major concern, and global warming is already playing a significant role in species extinctions. Our main goal was to predict climate change impacts on aquatic insect species distribution and richness in Swiss running waters according to two climate change scenarios (RCP2.6 and RCP8.5), using different modeling approaches, that is, species distribution models (SDMs), stacked-SDMs (S-SDMs) and a macroecological model (MEM). We analyzed 10,808 reaches, used as spatial units for model predictions, for a total river network length of 20,610 km. Results were assessed at both the countrywide and the biogeographic regional scales. We used incidence data of 41 species of Ephemeroptera, Plecoptera and Trichoptera (EPT) from 259 sites distributed across Switzerland. We integrated a coupled model for hydrology and glacier retreat to simulate monthly time-step discharge from which we derived hydrological variables. These, along with thermal, land-cover, topographic and spatially explicit data, served as predictors for our ecological models. Predictions of occurrence probabilities and EPT richness were compared among the different regions, periods and scenarios. A Shiny web application was developed to interactively explore all the models' details, to ensure transparency and promote the sharing of information. MEM and S-SDMs approaches consistently showed that overall, climate change is likely to reduce EPT richness. Decrease could be around 10% in the least conservative scenario, depending on the region. Global warming was shown to represent a threat to species from high elevation, but in terms of species richness, running waters from lowlands and medium elevation seemed more vulnerable. Finally, our results suggested that the effects of anthropogenic activities could overweight natural factors in shaping the future of river biodiversity.

Global expansion of COVID-19 pandemic is driven by population size and airport connections

The pandemic state of COVID-19 caused by the SARS CoV-2 put the world in quarantine, led to hundreds of thousands of deaths and is causing an unprecedented economic crisis. However, COVID-19 is spreading in different rates at different countries. Here, we tested the effect of three classes of predictors, i.e., socioeconomic, climatic and transport, on the rate of daily increase of COVID-19 on its exponential phase. We found that population size and global connections, represented by countries’ importance in the global air transportation network, are the main explanations for the early growth rate of COVID-19 in different countries. Climate and socioeconomics had no significant effect in this big picture analysis. Our results indicate that the current claims that the growth rate of COVID-19 may be lower in warmer and humid countries should be taken very carefully, risking to disturb well-established and effective policy of social isolation that may help to avoid higher mortality rates due to the collapse of national health systems.

Changes in conservation value from grasslands to savannas to forests: How a temperate canopy cover gradient affects butterfly community composition

Temperate savannas and grasslands are globally threatened. In the Midwest United States of America (USA), for example, oak savannas persist today at a small percentage of recent historic coverage. Therefore, restoration of habitats of low and intermediate canopy cover is a landscape conservation priority that often emphasizes returning tree density to a savanna-like target value. Understanding how animal species react to such changes in vegetation structure is important for assessing the value of these restoration plans. We examined how butterfly community attributes in northwest Indiana USA, including community composition, richness, and abundance responded to a grassland-to-forest gradient of canopy cover. Butterfly community composition under intermediate canopy cover differed significantly from community composition in the most open or closed-canopy habitats. Composition of the plant community in flower was a significant predictor of three assessed attributes of the butterfly community—composition, richness, and abundance. Phenology, expressed as day-of-the-year, was also a strong predictor of these butterfly community attributes. Few butterfly species were habitat specialists as adults although canopy cover was a more important predictor of adult community composition than of richness or abundance of butterflies. Therefore, adult butterfly community differences along the canopy cover gradient were less about butterfly communities filled with habitat specialists for different canopy-defined habitats and more about gradual changes in community composition along this gradient. Overall, butterfly community richness was predicted to peak at about 34% canopy cover, butterfly abundance at about 53% canopy cover, community conservation value at about 59% canopy cover, and a combination of desirable conservation attributes–high diversity, high abundance, and high conservation value–was predicted to reach a peak of co-occurrence at about 67% canopy cover suggesting that habitats of intermediate canopy cover might be particularly effective for butterfly conservation in this region.

Habitat suitability, niche unfilling and the potential spread of Pterois miles in the Mediterranean Sea

The common lionfish Pterois miles has rapidly spread across the eastern Mediterranean Sea. We compiled occurrence data from both native and invaded range under the framework of Species Distribution Modelling (SDM). Through a construction of an environmental suitability model and estimation of spread rates we investigated the lionfish climate niche in both its native and invaded domains, this latter represented by the Mediterranean region. Model projections allowed to identify suitable areas for lionfish establishment in the Mediterranean. Spread analysis suggested that a further geographical expansion in this basin could be completed within the next years. Our results did not provide evidence for niche expansion but highlighted a high degree of niche unfilling thus prospecting a likely spread of Mediterranean lionfish invasion beyond the predictions of current SDMs. These findings provide novel inputs to forecast the future geographical evolution of the lionfish in the Mediterranean Sea and asses the related risk of invasion.

Global distribution modelling, invasion risk assessment and niche dynamics of Leucanthemum vulgare (Ox-eye Daisy) under climate change

In an era of climate change, biological invasions by alien species represent one of the main anthropogenic drivers of global environmental change. The present study, using an ensemble modelling approach, has mapped current and future global distribution of the invasive Leucanthemum vulgare (Ox-eye Daisy) and predicted the invasion hotspots under climate change. The current potential distribution of Ox-eye Daisy coincides well with the actual distribution records, thereby indicating robustness of our model. The model predicted a global increase in the suitable habitat for the potential invasion of this species under climate change. Oceania was shown to be the high-risk region to the potential invasion of this species under both current and future climate change scenarios. The results revealed niche conservatism for Australia and Northern America, but contrastingly a niche shift for Africa, Asia, Oceania and Southern America. The global distribution modelling and risk assessment of Ox-eye Daisy has immediate implications in mitigating its invasion impacts under climate change, as well as predicting the global invasion hotspots and developing region-specific invasion management strategies. Interestingly, the contrasting patterns of niche dynamics shown by this invasive plant species provide novel insights towards disentangling the different operative mechanisms underlying the process of biological invasions at the global scale.

Improving Species Distribution Modelling of freshwater invasive species for management applications

Freshwater ecosystems rank among the most endangered ecosystems in the world and are under increasing threat from aquatic invasive species (AIS). Understanding the range expansion of AIS is key for mitigating their impacts. Most approaches rely on Species Distribution Models (SDMs) to predict the expansion of AIS, using mainly environmental variables, yet ignore the role of human activities in favouring the introduction and range expansion of AIS. In this study, we use five SDM algorithms (independently and in ensemble) and two accuracy measures (TSS, AUC), combined with a null modelling approach, to assess the predictive performance of the models and to quantify which predictors (environmental and anthropogenic from the native and introduced regions) best explain the distribution of nine freshwater invasive species (including fish, arthropods, molluscs, amphibians and reptiles) in a large island (Great Britain), and which species characteristics affect model performance. Our results show that the distribution of invasive species is difficult to predict by SDMs, even in cases when TSS and AUC model accuracy values are high. Our study strongly advocates the use of null models for testing SDMs performance and the inclusion of information from the native area and a variety of both human-related and environmental predictors for a more accurate modelling of the range expansion of AIS. Otherwise, models that only include climatic variables, or rely only on standard accuracy measures or a single algorithm, might result in mismanagement of AIS.

Relationships between Riparian Forest Fragmentation and Biological Indicators of Streams

Anthropogenic activities, such as land use and land cover modifications in riparian areas, can alter the degree of fragmentation of riparian vegetation, lead to the degradation of stream habitats, and affect biological communities in the streams. The characteristics of the riparian forests can modify the condition of stream environments and the transporting mechanisms of materials, sediments, nutrients, and pollutants loaded from the watersheds. This study aimed to examine the relationships between forest fragmentation and three biological indicators of trophic diatom, benthic macroinvertebrate, and the fish assessment in the Nakdong River, Korea. Eighty-nine biological assessment sampling sites in the National Aquatic Ecological Monitoring Program of South Korea were identified. For each sampling site, riparian forest data within a 500 m radius were extracted from national LULC using GIS to compute fragmentation metrics using FRAGSTATS software. Four fragmentation metrics—number of forest patches, percentage of riparian forest cover (PLAND), largest riparian forest patch index (LPI), and riparian forest division index (DIVISION)—were correlated with the biological indicators. Also, due to severe spatial autocorrelation among observations, the fragmentation metrics and stream environmental variables were regressed to biological indicators using regression tree analysis. Our results indicate that the biological indicators were significantly associated with most forest fragmentation metrics. We found positive correlations of PLAND and LPI with biological indicators, whereas DIVISION was negatively correlated with biological indicators. Both correlation and regression tree analyses revealed that the biological conditions of streams were likely to be better if riparian forests are less fragmented. Particularly, stronger relationships were revealed between macroinvertebrates and fish with the fragmentation metrics of riparian forests than with benthic diatoms. However, these relationships varied with elevation, stream size, and slope conditions. The results of this study reinforced the importance of including riparian forests in the planning, restoration, and management of stream environments. These results also suggested that planners and managers may need to consider different strategies for different stream environments and topographic characteristics in managing riparian forests.

A hierarchical Bayesian Beta regression approach to study the effects of geographical genetic structure and spatial autocorrelation on species distribution range shifts

Global climate change (GCC) may be causing distribution range shifts in many organisms worldwide. Multiple efforts are currently focused on the development of models to better predict distribution range shifts due to GCC. We addressed this issue by including intraspecific genetic structure and spatial autocorrelation (SAC) of data in distribution range models. Both factors reflect the joint effect of ecoevolutionary processes on the geographical heterogeneity of populations. We used a collection of 301 georeferenced accessions of the annual plant Arabidopsis thaliana in its Iberian Peninsula range, where the species shows strong geographical genetic structure. We developed spatial and nonspatial hierarchical Bayesian models (HBMs) to depict current and future distribution ranges for the four genetic clusters detected. We also compared the performance of HBMs with Maxent (a presence-only model). Maxent and nonspatial HBMs presented some shortcomings, such as the loss of accessions with high genetic admixture in the case of Maxent and the presence of residual SAC for both. As spatial HBMs removed residual SAC, these models showed higher accuracy than nonspatial HBMs and handled the spatial effect on model outcomes. The ease of modelling and the consistency among model outputs for each genetic cluster was conditioned by the sparseness of the populations across the distribution range. Our HBMs enrich the toolbox of software available to evaluate GCC-induced distribution range shifts by considering both genetic heterogeneity and SAC, two inherent properties of any organism that should not be overlooked.

Beyond the model: expert knowledge improves predictions of species' fates under climate change

The need to proactively manage landscapes and species to aid their adaptation to climate change is widely acknowledged. Current approaches to prioritizing investment in species conservation generally rely on correlative models, which predict the likely fate of species under different climate change scenarios. Yet, while model statistics can be improved by refining modeling techniques, gaps remain in understanding the relationship between model performance and ecological reality. To investigate this, we compared standard correlative species distribution models to highly accurate, fine-scale, distribution models. We critically assessed the ecological realism of each species' model, using expert knowledge of the geography and habitat in the study area and the biology of the study species. Using interactive software and an iterative vetting with experts, we identified seven general principles that explain why the distribution modeling under- or overestimated habitat suitability, under both current and predicted future climates. Importantly, we found that, while temperature estimates can be dramatically improved through better climate downscaling, many models still inaccurately reflected moisture availability. Furthermore, the correlative models did not account for biotic factors, such as disease or competitor species, and were unable to account for the likely presence of micro refugia. Under-performing current models resulted in widely divergent future projections of species' distributions. Expert vetting identified regions that were likely to contain micro refugia, even where the fine-scale future projections of species distributions predicted population losses. Based on the results, we identify four priority conservation actions required for more effective climate change adaptation responses. This approach to improving the ecological realism of correlative models to understand climate change impacts on species can be applied broadly to improve the evidence base underpinning management responses.

Inter-individual consistency in habitat selection patterns and spatial range constraints of female little bustards during the non-breeding season

Background

Identifying the factors that affect ranging behavior of animals is a central issue to ecology and an essential tool for designing effective conservation policies. This knowledge provides the information needed to predict the consequences of land-use change on species habitat use, especially in areas subject to major habitat transformations, such as agricultural landscapes. We evaluate inter-individual variation relative to environmental predictors and spatial constraints in limiting ranging behavior of female little bustards (Tetrax tetrax) in the non-breeding season. Our analyses were based on 11 females tracked with GPS during 5 years in northeastern Spain. We conducted deviance partitioning analyses based on different sets of generalized linear mixed models constructed with environmental variables and spatial filters obtained by eigenvector mapping, while controlling for temporal and inter-individual variation.

Results

The occurrence probability of female little bustards in response to environmental variables and spatial filters within the non-breeding range exhibited inter-individual consistency. Pure spatial factors and joint spatial-habitat factors explained most of the variance in the models. Spatial predictors representing aggregation patterns at ~ 18 km and 3–5 km respectively had a high importance in female occurrence. However, pure habitat effects were also identified. Terrain slope, alfalfa, corn stubble and irrigated cereal stubble availability were the variables that most contributed to environmental models. Overall, models revealed a non-linear negative effect of slope and positive effects of intermediate values of alfalfa and corn stubble availability. High levels of cereal stubble in irrigated land and roads had also a positive effect on occurrence at the population level.

Conclusions

Our results provide evidence that female little bustard ranging behavior was spatially constrained beyond environmental variables during the non-breeding season. This pattern may result from different not mutually exclusive processes, such as cost–benefit balances of animal movement, configurational heterogeneity of environment or from high site fidelity and conspecific attraction. Measures aimed at keeping alfalfa availability and habitat heterogeneity in open landscapes and flat terrains, in safe places close to breeding grounds, could contribute to protect little bustard populations during the non-breeding season.

Electronic supplementary material

The online version of this article (10.1186/s12898-018-0205-9) contains supplementary material, which is available to authorized users.

Evaluating collinearity effects on species distribution models: An approach based on virtual species simulation

The increasing use of species distribution modeling (SDM) has raised new concerns regarding the inaccuracies, misunderstanding, and misuses of this important tool. One of those possible pitfalls - collinearity among environmental predictors - is assumed as an important source of model uncertainty, although it has not been subjected to a detailed evaluation in recent SDM studies. It is expected that collinearity will increase uncertainty in model parameters and decrease statistical power. Here we use a virtual species approach to compare models built using subsets of PCA-derived variables with models based on the original highly correlated climate variables. Moreover, we evaluated whether modelling algorithms and species data characteristics generate models with varying sensitivity to collinearity. As expected, collinearity among predictors decreases the efficiency and increases the uncertainty of species distribution models. Nevertheless, the intensity of the effect varied according to the algorithm properties: more complex procedures behaved better than simple envelope models. This may support the claim that complex models such as Maxent take advantage of existing collinearity in finding the best set of parameters. The interaction of the different factors with species characteristics (centroid and tolerance in environmental space) highlighted the importance of the so-called "idiosyncrasy in species responses" to model efficiency, but differences in prevalence may represent a better explanation. However, even models with low accuracy to predict suitability of individual cells may provide meaningful information on the estimation of range-size, a key species-trait for macroecological studies. We concluded that the use of PCA-derived variables is advised both to control the negative effects of collinearity and as a more objective solution for the problem of variable selection in studies dealing with large number of species with heterogeneous responses to environmental variables.

The effect of climate change on the distribution of a tropical zoanthid (Palythoa caribaeorum) and its ecological implications

Palythoa caribaeorum is a zoanthid often dominant in shallow rocky environments along the west coast of the Atlantic Ocean, from the tropics to the subtropics. This species has high environmental tolerance and is a good space competitor in reef environments. Considering current and future scenarios in the global climate regime, this study aimed to model and analyze the distribution of P. caribaeorum, generating maps of potential distribution for the present and the year 2100. The distribution was modeled using maximum entropy (Maxent) based on 327 occurrence sites retrieved from the literature. Calcite concentration, maximum chlorophyll-a concentration, salinity, pH, and temperature range yielded a model with the smallest Akaike information criterion (2649.8), and were used in the present and future distribution model. Data from the HadGEM2-ES climate model were used to generate the projections for the year 2100. The present distribution of P. caribaeorum shows that parts of the Brazilian coast, Caribbean Sea, and Florida are suitable regions for the species, as they are characterized by high salinity and pH and small temperature variation. An expansion of the species’ distribution was forecast northward under mild climate scenarios, while a decrease of suitable areas was forecast in the south. In the climate scenario with the most intense changes, P. caribaeorum would lose one-half of its suitable habitats, including the northernmost and southernmost areas of its distribution. The Caribbean Sea and northeastern Brazil, as well as other places under the influence of coastal upwellings, may serve as potential havens for this species.

Combining public participatory surveillance and occupancy modelling to predict the distributional response of Ixodes scapularis to climate change

Ixodes scapularis, a known vector of Borrelia burgdorferi sensu stricto (Bbss), is undergoing range expansion in many parts of Canada. The province of New Brunswick, which borders jurisdictions with established populations of I. scapularis, constitutes a range expansion zone for this species. To better understand the current and potential future distribution of this tick under climate change projections, this study applied occupancy modelling to distributional records of adult ticks that successfully overwintered, obtained through passive surveillance. This study indicates that I. scapularis occurs throughout the southern-most portion of the province, in close proximity to coastlines and major waterways. Milder winter conditions, as indicated by the number of degree days <0 °C, was determined to be a strong predictor of tick occurrence, as was, to a lesser degree, rising levels of annual precipitation, leading to a final model with a predictive accuracy of 0.845 (range: 0.828-0.893). Both RCP 4.5 and RCP 8.5 climate projections predict that a significant proportion of the province (roughly a quarter to a third) will be highly suitable for I. scapularis by the 2080s. Comparison with cases of canine infection show good spatial agreement with baseline model predictions, but the presence of canine Borrelia infections beyond the climate envelope, defined by the highest probabilities of tick occurrence, suggest the presence of Bbss-carrying ticks distributed by long-range dispersal events. This research demonstrates that predictive statistical modelling of multi-year surveillance information is an efficient way to identify areas where I. scapularis is most likely to occur, and can be used to guide subsequent active sampling efforts in order to better understand fine scale species distributional patterns.

Model application niche analysis: Assessing the transferability and generalizability of ecological models

The use of models by ecologists and environmental managers, to inform environmental management and decision-making, has grown exponentially in the past 50 years. Due to logistical, economical, and theoretical benefits, model users frequently transfer preexisting models to new sites where data are scarce. Modelers have made significant progress in understanding how to improve model generalizability during model development. However, models are always imperfect representations of systems and are constrained by the contextual frameworks used during their development. Thus, model users need better ways to evaluate the possibility of unintentional misapplication when transferring models to new sites. We propose a method of describing a model's application niche for use during the model selection process. Using this method, model users synthesize information from databases, past studies, and/or past model transfers to create model performance curves and heat maps. We demonstrated this method using an empirical model developed to predict the ecological condition of plant communities in riverine wetlands of the Appalachian Highland physiographic region, U.S.A. We assessed this model's transferability and generalizability across (1) riverine wetlands in the contiguous U.S.A., (2) wetland types in the Appalachian Highland physiographic region, and (3) wetland types in the contiguous U.S.A. With this methodology and a discussion of its critical steps, we set the stage for further inquiries into the development of consistent and transparent practices for model selection when transferring a model.

Modelling the Distribution of Forest-Dependent Species in Human-Dominated Landscapes: Patterns for the Pine Marten in Intensively Cultivated Lowlands

In recent years, the "forest-specialist" pine marten Martes martes has been reported to also occur also in largely fragmented, lowland landscapes of north-western Italy. The colonization of such an apparently unsuitable area provided the opportunity for investigating pine marten ecological requirements and predicting its potential south- and eastwards expansion. We collected available pine marten occurrence data in the flood plain of the River Po (N Italy) and relate them to 11 environmental variables by developing nine Species Distribution Models. To account for inter-model variability we used average ensemble predictions (EP). EP predicted a total of 482 suitable patches (8.31% of the total study area) for the pine marten. The main factors driving pine marten occurrence in the western River Po plain were the distance from watercourses and the distance from woods. EP suggested that the pine marten may further expand in the western lowland, whilst the negligible residual wood cover of large areas in the central and eastern plain makes the habitat unsuitable for the pine marten, except for some riparian corridors and the pine wood patches bordering the Adriatic coast. Based on our results, conservation strategies should seek to preserve remnant forest patches and enhance the functional connectivity provided by riparian corridors.

Spatial predictions at the community level: from current approaches to future frameworks

A fundamental goal of ecological research is to understand and model how processes generate patterns so that if conditions change, changes in the patterns can be predicted. Different approaches have been proposed for modelling species assemblage, but their use to predict spatial patterns of species richness and other community attributes over a range of spatial and temporal scales remains challenging. Different methods emphasize different processes of structuring communities and different goals. In this review, we focus on models that were developed for generating spatially explicit predictions of communities, with a particular focus on species richness, composition, relative abundance and related attributes. We first briefly describe the concepts and theories that span the different drivers of species assembly. A combination of abiotic processes and biotic mechanisms are thought to influence the community assembly process. In this review, we describe four categories of drivers: (i) historical and evolutionary, (ii) environmental, (iii) biotic, and (iv) stochastic. We discuss the different modelling approaches proposed or applied at the community level and examine them from different standpoints, i.e. the theoretical bases, the drivers included, the source data, and the expected outputs, with special emphasis on conservation needs under climate change. We also highlight the most promising novelties, possible shortcomings, and potential extensions of existing methods. Finally, we present new approaches to model and predict species assemblages by reviewing promising 'integrative frameworks' and views that seek to incorporate all drivers of community assembly into a unique modelling workflow. We discuss the strengths and weaknesses of these new solutions and how they may hasten progress in community-level modelling.

Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts

Conservation increasingly operates at the landscape scale. For this to be effective, we need landscape scale information on species distributions and the environmental factors that underpin them. Species records are becoming increasingly available via data centres and online portals, but they are often patchy and biased. We demonstrate how such data can yield useful habitat suitability models, using bat roost records as an example. We analysed the effects of environmental variables at eight spatial scales (500 m - 6 km) on roost selection by eight bat species (Pipistrellus pipistrellus, P. pygmaeus, Nyctalus noctula, Myotis mystacinus, M. brandtii, M. nattereri, M. daubentonii, and Plecotus auritus) using the presence-only modelling software MaxEnt. Modelling was carried out on a selection of 418 data centre roost records from the Lake District National Park, UK. Target group pseudoabsences were selected to reduce the impact of sampling bias. Multi-scale models, combining variables measured at their best performing spatial scales, were used to predict roosting habitat suitability, yielding models with useful predictive abilities. Small areas of deciduous woodland consistently increased roosting habitat suitability, but other habitat associations varied between species and scales. Pipistrellus were positively related to built environments at small scales, and depended on large-scale woodland availability. The other, more specialist, species were highly sensitive to human-altered landscapes, avoiding even small rural towns. The strength of many relationships at large scales suggests that bats are sensitive to habitat modifications far from the roost itself. The fine resolution, large extent maps will aid targeted decision-making by conservationists and planners. We have made available an ArcGIS toolbox that automates the production of multi-scale variables, to facilitate the application of our methods to other taxa and locations. Habitat suitability modelling has the potential to become a standard tool for supporting landscape-scale decision-making as relevant data and open source, user-friendly, and peer-reviewed software become widely available.

Predicting the potential distribution of the amphibian pathogen Batrachochytrium dendrobatidis in East and Southeast Asia

Batrachochytrium dendrobatidis (Bd) is the pathogen responsible for chytridiomycosis, a disease that is associated with a worldwide amphibian population decline. In this study, we predicted the potential distribution of Bd in East and Southeast Asia based on limited occurrence data. Our goal was to design an effective survey area where efforts to detect the pathogen can be focused. We generated ecological niche models using the maximum-entropy approach, with alleviation of multicollinearity and spatial autocorrelation. We applied eigenvector-based spatial filters as independent variables, in addition to environmental variables, to resolve spatial autocorrelation, and compared the model's accuracy and the degree of spatial autocorrelation with those of a model estimated using only environmental variables. We were able to identify areas of high suitability for Bd with accuracy. Among the environmental variables, factors related to temperature and precipitation were more effective in predicting the potential distribution of Bd than factors related to land use and cover type. Our study successfully predicted the potential distribution of Bd in East and Southeast Asia. This information should now be used to prioritize survey areas and generate a surveillance program to detect the pathogen.

Defining landscape resistance values in least-cost connectivity models for the invasive grey squirrel: a comparison of approaches using expert-opinion and habitat suitability modelling

Least-cost models are widely used to study the functional connectivity of habitat within a varied landscape matrix. A critical step in the process is identifying resistance values for each land cover based upon the facilitating or impeding impact on species movement. Ideally resistance values would be parameterised with empirical data, but due to a shortage of such information, expert-opinion is often used. However, the use of expert-opinion is seen as subjective, human-centric and unreliable. This study derived resistance values from grey squirrel habitat suitability models (HSM) in order to compare the utility and validity of this approach with more traditional, expert-led methods. Models were built and tested with MaxEnt, using squirrel presence records and a categorical land cover map for Cumbria, UK. Predictions on the likelihood of squirrel occurrence within each land cover type were inverted, providing resistance values which were used to parameterise a least-cost model. The resulting habitat networks were measured and compared to those derived from a least-cost model built with previously collated information from experts. The expert-derived and HSM-inferred least-cost networks differ in precision. The HSM-informed networks were smaller and more fragmented because of the higher resistance values attributed to most habitats. These results are discussed in relation to the applicability of both approaches for conservation and management objectives, providing guidance to researchers and practitioners attempting to apply and interpret a least-cost approach to mapping ecological networks.

Global invasion of Lantana camara: has the climatic niche been conserved across continents?

Lantana camara, a native plant from tropical America, is considered one of the most harmful invasive species worldwide. Several studies have identified potentially invasible areas under scenarios of global change, on the assumption that niche is conserved during the invasion process. Recent studies, however, suggest that many invasive plants do not conserve their niches. Using Principal Components Analyses (PCA), we tested the hypothesis of niche conservatism for L. camara by comparing its native niche in South America with its expressed niche in Africa, Australia and India. Using MaxEnt, the estimated niche for the native region was projected onto each invaded region to generate potential distributions there. Our results demonstrate that while L. camara occupied subsets of its original native niche in Africa and Australia, in India its niche shifted significantly. There, 34% of the occurrences were detected in warmer habitats nonexistent in its native range. The estimated niche for India was also projected onto Africa and Australia to identify other vulnerable areas predicted from the observed niche shift detected in India. As a result, new potentially invasible areas were identified in central Africa and southern Australia. Our findings do not support the hypothesis of niche conservatism for the invasion of L. camara. The mechanisms that allow this species to expand its niche need to be investigated in order to improve our capacity to predict long-term geographic changes in the face of global climatic changes.

Multimillion-year climatic effects on palm species diversity in Africa

Past climatic changes have caused extinction, speciation, and range dynamics, but assessing the influence of past multimillion-year climatic imprints on present-day biodiversity patterns remains challenging. We analyzed a new continental-scale data set to examine the importance of paleoclimatic effects on current gradients in African palm richness patterns. Using climate reconstructions from the late Miocene (-10 mya), the Pliocene (-3 mya), and the Last Glacial Maximum (0.021 mya), we found that African palm diversity patterns exhibit pronounced historical legacies related to long-term climate change. Notably, pre-Pleistocene paleoprecipitation variables differentially affected current diversity patterns of palms grouped by contrasting habitat requirements. Accounting for present-day environment, rain forest palms exhibit greater species richness in localities where Pliocene precipitation was relatively high, whereas open-habitat palms show higher species richness in areas of relatively low precipitation during the Miocene Epoch. Our results demonstrate that diversity-climate relationships among African palm species include multimillion-year lagged dynamics, i.e., with historical legacies persisting across much longer time periods than commonly recognized.

Influence of environment and climate on occurrence of the cixiid planthopper Hyalesthes obsoletus, the vector of the grapevine disease 'bois noir'

Species distribution models (SDMs), which are well established in many fields of biological research, are still uncommon in the agricultural risk analysis of pest insects. To exemplify the use of SDMs, we investigated the influence of environmental factors on the occurrence of Hyalesthes obsoletus Signoret (Hemiptera: Cixiidae). The planthopper is the only known vector of the grapevine yellows disease 'bois noir'. The study was conducted in 145 locations in the Baden region of southwest Germany. The planthopper was surveyed on host plant patches, consisting of stinging nettle and/or bindweeds. We used a stratified modelling framework where (1) species presence-absence data were related to an extensive environmental dataset using logistic regressions; and (2) different types of average models were developed based on an information theoretic method. The results show that the incidence of H. obsoletus is associated to above- as well as below-ground environmental factors, particularly to the amount of fine soil and average annual precipitation. This result was consistent across all average models. The relative importance of other environmental variables was dependent upon the average model under consideration and thus may vary according to their intended use, either the explanation of habitat requirements or the prediction and mapping of occurrence risks. The study showed that SDMs offer a quantification of species' habitat requirements and thus, could represent a valuable tool for pest management purposes. By providing examples of current issues of grapevine pests in viticulture, we discuss the use of SDMs in agricultural risk analysis and highlight their advantages and caveats.