The role of land cover in bioclimatic models depends on spatial resolution

Aim We explored the importance of climate and land cover in bird species distribution models on multiple spatial scales. In particular, we tested whether the integration of land cover data improves the performance of pure bioclimatic models. Location Finland, northern Europe. Methods The data of the bird atlas survey carried out in 1986–89 using a 10 × 10 km uniform grid system in Finland were employed in the analyses. Land cover and climatic variables were compiled using the same grid system. The dependent and explanatory variables were resampled to 20‐km, 40‐km and 80‐km resolutions. Generalized additive models (GAM) were constructed for each of the 88 land bird species studied in order to estimate the probability of occurrence as a function of (1) climate and (2) climate and land cover variables. Model accuracy was measured by a cross‐validation approach using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Results In general, the accuracies of the 88 bird–climate models were good at all studied resolutions. However, the inclusion of land cover increased the performance of 79 and 78 of the 88 bioclimatic models at 10‐km and 20‐km resolutions, respectively. There was no significant improvement at the 40‐km resolution. In contrast to the finer resolutions, the inclusion of land cover variables decreased the modelling accuracy at 80km resolution. Main conclusions Our results suggest that the determinants of bird species distributions are hierarchically structured: climatic variables are large‐scale determinants, followed by land cover at finer resolutions. The majority of the land bird species in Finland are rather clearly correlated with climate, and bioclimate envelope models can provide useful tools for identifying the relationships between these species and the environment at resolutions ranging from 10 km to 80 km. However, the notable contribution of land cover to the accuracy of bioclimatic models at 10–20‐km resolutions indicates that the integration of climate and land cover information can improve our understanding and model predictions of biogeographical patterns under global change.     

Fine scale waterbody data improve prediction of waterbird occurrence despite coarse species data

While modelling habitat suitability and species distribution, ecologists must deal with issues related to the spatial resolution of species occurrence and environmental data. Indeed, given that the spatial resolution of species and environmental datasets range from centimeters to hundreds of kilometers, it underlines the importance of choosing the optimal combination of resolutions to achieve the highest possible modelling prediction accuracy. We evaluated how the spatial resolution of land cover/waterbody datasets (meters to 1 km) affect waterbird habitat suitability models based on atlas data (grid cell of 12 × 11 km). We hypothesized that the area, perimeter and number of waterbodies computed from high resolution datasets would explain distributions of waterbirds better because coarse resolution datasets omit small waterbodies affecting species occurrence. Specifically, we investigated which spatial resolution of waterbodies better explain the distribution of seven waterbirds nesting on ponds/lakes with areas ranging from 0.1 ha to hundreds of hectares. Our results show that the area and perimeter of waterbodies derived from high resolution datasets (raster data with 30 m resolution, vector data corresponding with map scale 1:10 000) explain the distribution of the waterbirds better than those calculated using less accurate datasets despite the coarse grain of the species data. Taking into account the spatial extent (global vs regional) of the datasets, we found the Global Inland Waterbody Dataset to be the most suitable for modelling distribution of waterbirds. In general, we recommend using land cover data of a resolution sufficient to capture the smallest patches of the habitat suitable for a given species’ presence for both fine and coarse grain habitat suitability and distribution modelling.     

Predicting the distribution of four species of raptors (Aves: Accipitridae) in southern Spain: statistical models work better than existing maps

Aim To test the effectiveness of statistical models based on explanatory environmental variables vs. existing distribution information (maps and breeding atlas), for predicting the distribution of four species of raptors (family Accipitridae): common buzzard Buteo buteo (Linnaeus, 1758), short‐toed eagle Circaetus gallicus (Gmelin, 1788), booted eagle Hieraaetus pennatus (Gmelin, 1788) and black kite Milvus migrans (Boddaert, 1783). Location Andalusia, southern Spain. Methods Generalized linear models of 10 × 10 km squares surveyed for the presence/absence of the species by road census. Statistical models use as predictors variables derived from topography, vegetation and land‐use, and the geographical coordinates (to take account of possible spatial trends). Predictions from the models are compared with current distribution maps from the national breeding atlas and leading reference works. Results The maps derived from statistical models for all four species were more predictive than the previously published range maps and the recent national breeding atlas. The best models incorporated both topographic and vegetation and land‐use variables. Further, in three of the four species the inclusion of spatial coordinates to account for neighbourhood effects improved these models. Models for the common buzzard and black kite were highly predictive and easy to interpret from an ecological point of view, while models for short‐toed eagle and, particularly, booted eagle were not so easy to interpret, but still predicted better than previous distribution information. Main conclusions It is possible to build accurate predictive models for raptor distribution with a limited field survey using as predictors environmental variables derived from digital maps. These models integrated in a geographical information system produced distribution maps that were more accurate than previously published ones for the study species in the study area. Our study is an example of a methodology that could be used for many taxa and areas to improve unreliable distribution information.     

Species-specific features affect the ability of census-derived models to map winter avian distribution

We identify autoecological traits of bird species that influence the accuracy of predictive models of species distribution based on census data obtained from stratified sampling. These models would serve as a complementary approach to the development of regional bird atlases. We model the winter bird abundance of 64 terrestrial bird species in 77 census plots in Central Spain (Madrid province), using regression tree analyses. The predicted distribution of species density derived from statistical models (birds/10 ha) was compared with the published relative abundances depicted by a very accurate regional atlas of wintering birds (birds observed per 10 h). Statistical models explained an average of 41.7% of the original deviance observed in the local bird distribution (range 19.6–79.3%). Significant associations between observed relative abundances (atlas data) and predicted average densities in 1×1 km squares within 10×10 km UTMs were attained for 44 out of 64 species. Interspecific discrepancies between predicted and observed distribution maps decreased with between-year constancy in regional bird distribution and the degree of ecological specialization of species. Therefore, statistical modeling using census localities allowed us to depict geographical variations in bird abundance that were similar to those in the quantitative atlas maps. Nevertheless, bird distributions derived from statistical models are less reproducible in some species than in others, depending on their autoecological traits.     

Updating bird species distribution at large spatial scales: applications of habitat modelling to data from long-term monitoring programs

Mapping of species distributions at large spatial scales has been often based on the representation of gathered observations in a general grid atlas framework. More recently, subsampling and subsequent interpolation or habitat spatial modelling techniques have been incorporated in these projects to allow more detailed species mapping. Here, we explore the usefulness of data from long-term monitoring (LTM) projects, primarily aimed at estimating trends in species abundance and collected at shorter time intervals (usually yearly) than atlas data, to develop predictive habitat models. We modelled habitat occupancy for 99 species using a bird LTM program and evaluated the predictive accuracy of these models using independent data from a contemporary and comprehensive breeding bird atlas project from the same region. Habitat models from LTM data using generalized linear modelling were significant for all the species and generally showed a high predictive power, albeit lower than that from atlas models. Sample size and species range size and niche breadth were the most important factors behind variability in model predictive accuracy, whereas the spatial distribution of sampling units at a given sample size had minor effects. Although predictive accuracy of habitat modelling was strongly species dependent, increases in sample size and, secondarily, a better spatial distribution of sampling units should lead to more powerful predictive distribution models. We suggest that data from LTM programs, now established in a large number of countries, has the potential for being a major source of good quality data suitable for the estimation and regularly update of distributions at large spatial scales for a number of species.     

Modelling invasive alien species distributions from digital biodiversity atlases. Model upscaling as a means of reconciling data at different scales

Aim

There is a wealth of information on species occurrences in biodiversity data banks, albeit presence‐only, biased and scarce at fine resolutions. Moreover, fine‐resolution species maps are required in biodiversity conservation. New techniques for dealing with this kind of data have been reported to perform well. These fine‐resolution maps would be more robust if they could explain data at coarser resolutions at which species distributions are well represented. We present a new methodology for testing this hypothesis and apply it to invasive alien species (IAS).

Location

Catalonia, Spain.

Methods

We used species presence records from the Biodiversity data bank of Catalonia to model the distribution of ten IAS which, according to some recent studies, achieve their maximum distribution in the study area. To overcome problems inherent with the data, we prepared different correction treatments: three for dealing with bias and five for autocorrelation. We used the MaxEnt algorithm to generate models at 1‐km resolution for each species and treatment. Acceptable models were upscaled to 10 km and validated against independent 10 km occurrence data.

Results

Of a total of 150 models, 20 gave acceptable results at 1‐km resolution and 12 passed the cross‐scale validation test. No apparent pattern emerged, which could serve as a guide on modelling. Only four species gave models that also explained the distribution at the coarser scale.

Main conclusions

Although some techniques may apparently deliver good distribution maps for species with scarce and biased data, they need to be taken with caution. When good independent data at a coarser scale are available, cross‐scale validation can help to produce more reliable and robust maps. When no independent data are available for validation, however, new data gathering field surveys may be the only option if reliable fine‐scale resolution maps are needed.     

Downscaling European species atlas distributions to a finer resolution: implications for conservation planning

Aim One of the limitations to using species’ distribution atlases in conservation planning is their coarse resolution relative to the needs of local planners. In this study, a simple approach to downscale original species atlas distributions to a finer resolution is outlined. If such a procedure yielded accurate downscaled predictions, then it could be an aid to using available distribution atlases in real‐world local conservation decisions. Location Europe. Methods An iterative procedure based on generalized additive modelling is used to downscale original European 50 × 50 km distributions of 2189 plant and terrestrial vertebrate species to c. 10 × 10 km grid resolution. Models are trained on 70% of the original data and evaluated on the remaining 30%, using the receiver operating characteristic (ROC) procedure. Fitted models are then interpolated to a finer resolution. A British dataset comprising distributions of 81 passerine‐bird species in a 10 × 10 km grid is used as a test bed to assess the accuracy of the downscaled predictions. European‐wide, downscaled predictions are further evaluated in terms of their ability to reproduce: (1) spatial patterns of coincidence in species richness scores among different groups; and (2) spatial patterns of coincidence in richness, rarity and complementarity hotspots. Results There was a generally good agreement between downscaled and observed fine‐resolution distributions for passerine species in Britain (median Jaccard similarity = 70%; lower quartile = 36%; upper quartile = 88%). In contrast, the correlation between downscaled and observed passerine species richness was relatively low (rho = 0.31) indicating a pattern of error propagation through the process of overlaying downscaled distributions for many species. It was also found that measures of model accuracy in fitting original data (ROC) were a poor predictor of models’ ability to interpolate distributions at fine resolutions (rho = ?0.10). Although European hotspots were not fully coincident between observed and modelled coarse‐resolution data, or between modelled coarse resolution and modelled downscaled data, there was evidence that downscaled distributions were able to maintain original cross‐taxon coincidence of species‐richness scores, at least for terrestrial vertebrate groups. Downscaled distributions were also able to uncover important environmental gradients otherwise blurred by coarse‐resolution data. Main conclusions Despite uncertainties, downscaling procedures may prove useful to identify reserves that are more meaningfully related to local patterns of environmental variation. Potential errors arising from the presence of false positives may be reduced if downscaled‐distribution records projected to occur outside the range of original coarse‐resolution data are excluded. However, the usefulness of this procedure may be limited to data‐rich regions. If downscaling procedures are applied to data‐poor regions, then there is a need to undertake further research to understand the structure of error in models. In particular, it would be important to investigate which species are poorly modelled, where and why. Without such an assessment it is difficult to support unsupervised use of downscaled data in most real‐world situations.     

Spatial distribution and breeding territories of Bonelli’s Eagle Aquila fasciata in the western Anti-Atlas Mountains,Morocco

The North African population of Bonelli’s Eagle Aquila fasciata (Vieillot, 1822) is limited to the south by the northern fringe of the Sahara Desert. This study provides the first data on the spatial distribution and density of breeding Bonelli’s Eagles in south-west Morocco, at the southern limit of their breeding distribution in the Western Palearctic. We used broad-scale sampling to investigate spatial patterns in occupancy of territorial pairs of Bonelli’s Eagles in an area of ～29 715 km2 in the western Anti-Atlas Mountains, southern Morocco, during 2016–2018. We found 28 nesting territories containing 40 used nests, heterogeneously distributed in areas of high topographic variation, from 60 to 1 890 m asl. The average nearest-neighbour nest distance was 14.12 ± 9.90 km and varied from 2.65 km in the north-west to 37.80 km in the pre-Saharan lands in the south-west portion of the study area. This work shows the importance of the western Anti-Atlas Mountains as one of the most significant strongholds of the species in Morocco. However, there is a need for systematic analyses of the different factors affecting the distribution of the species to implement conservation actions of this peripheral population.     

Optimising long‐term monitoring projects for species distribution modelling: how atlas data may help

Long‐term biodiversity monitoring data are mainly used to estimate changes in species occupancy or abundance over time, but they may also be incorporated into predictive models to document species distributions in space. Although changes in occupancy or abundance may be estimated from a relatively limited number of sampling units, small sample size may lead to inaccurate spatial models and maps of predicted species distributions. We provide a methodological approach to estimate the minimum sample size needed in monitoring projects to produce accurate species distribution models and maps. The method assumes that monitoring data are not yet available when sampling strategies are to be designed and is based on external distribution data from atlas projects. Atlas data are typically collected in a large number of sampling units during a restricted timeframe and are often similar in nature to the information gathered from long‐term monitoring projects. The large number of sampling units in atlas projects makes it possible to simulate a broad gradient of sample sizes in monitoring data and to examine how the number of sampling units influences the accuracy of the models. We apply the method to several bird species using data from a regional breeding bird atlas. We explore the effect of prevalence, range size and habitat specialization of the species on the sample size needed to generate accurate models. Model accuracy is sensitive to particularly small sample sizes and levels off beyond a sufficiently large number of sampling units that varies among species depending mainly on their prevalence. The integration of spatial modelling techniques into monitoring projects is a cost‐effective approach as it offers the possibility to estimate the dynamics of species distributions in space and over time. We believe our innovative method will help in the sampling design of future monitoring projects aiming to achieve such integration.     

Downscaling patterns of complementarity to a finer resolution and its implications for conservation prioritization

Given species inventories of all sites in a planning area, integer programming or heuristic algorithms can prioritize sites in terms of the site's complementary value, that is, the ability of the site to complement (add unrepresented species to) other sites prioritized for conservation. The utility of these procedures is limited because distributions of species are typically available only as coarse atlases or range maps, whereas conservation planners need to prioritize relatively small sites. If such coarse‐resolution information can be used to identify small sites that efficiently represent species (i.e., downscaled), then such data can be useful for conservation planning. We develop and test a new type of surrogate for biodiversity, which we call downscaled complementarity. In this approach, complementarity values from large cells are downscaled to small cells, using statistical methods or simple map overlays. We illustrate our approach for birds in Spain by building models at coarse scale (50 × 50 km atlas of European birds, and global range maps of birds interpreted at the same 50 × 50 km grid size), using this model to predict complementary value for 10 × 10 km cells in Spain, and testing how well‐prioritized cells represented bird distributions in an independent bird atlas of those 10 × 10 km cells. Downscaled complementarity was about 63–77% as effective as having full knowledge of the 10‐km atlas data in its ability to improve on random selection of sites. Downscaled complementarity has relatively low data acquisition cost and meets representation goals well compared with other surrogates currently in use. Our study justifies additional tests to determine whether downscaled complementarity is an effective surrogate for other regions and taxa, and at spatial resolution finer than 10 × 10 km cells. Until such tests have been completed, we caution against assuming that any surrogate can reliably prioritize sites for species representation.     

Can we disentangle predator–prey interactions from species distributions at a macro‐scale? A case study with a raptor species

There is a debate on whether an influence of biotic interactions on species distributions can be reflected at macro‐scale levels. Whereas the influence of biotic interactions on spatial arrangements is beginning to be studied at local scales, similar studies at macro‐scale levels are scarce. There is no example disentangling, from other similarities with related species, the influence of predator–prey interactions on species distributions at macro‐scale levels. In this study we aimed to disentangle predator–prey interactions from species distribution data following an experimental approach including a factorial design. As a case of study we selected the short‐toed eagle because of its known specialization on certain prey reptiles. We used presence–absence data at a 100 km2 spatial resolution to extract the explanatory capacity of different environmental predictors (five abiotic and two biotic predictors) on the short‐toed eagle species distribution in peninsular Spain. Abiotic predictors were relevant climatic and topographic variables, and relevant biotic predictors were prey richness and forest density. In addition to the short‐toed eagle, we also obtained the predictor's explanatory capacities for 1) species of the same family Accipitridae (as a reference), 2) for other birds of different families (as controls) and 3) artificial species with randomly selected presences (as null models). We run 650 models to test for similarities of the short‐toed eagle, controls and null models with reference species, assessed by regressions of explanatory capacities. We found higher similarities between the short‐toed eagle and other species of the family Accipitridae than for the other two groups. Once corrected by the family effect, our analyses revealed a signal of predator–prey interaction embedded in species distribution data. This result was corroborated with additional analyses testing for differences in the concordance between the distributions of different bird categories and the distributions of either prey or non‐prey species of the short‐toed eagle. Our analyses were useful to disentangle a signal of predator–prey interactions from species distribution data at a macro‐scale. This study highlights the importance of disentangling specific features from the variation shared with a given taxonomic level.     

Uncertainty of bioclimate envelope models based on the geographical distribution of species

Aim We explored the effects of prevalence, latitudinal range and spatial autocorrelation of species distribution patterns on the accuracy of bioclimate envelope models of butterflies. Location Finland, northern Europe. Methods The data of a national butterfly atlas survey (NAFI) carried out in 1991–2003 with a resolution of 10 × 10 km were used in the analyses. Generalized additive models (GAM) were constructed, for each of 98 species, to estimate the probability of occurrence as a function of climate variables. Model performance was measured using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Observed differences in modelling accuracy among species were related to the species’ geographical attributes using multivariate GAM. Results Accuracies of the climate–butterfly models varied from low to very high (AUC values 0.59–0.99), with a mean of 0.79. The modelling performance was related negatively to the latitudinal range and prevalence, and positively to the spatial autocorrelation of the species distribution. These three factors accounted for 75.2% of the variation in the modelling accuracy. Species at the margin of their range or with low prevalence were better predicted than widespread species, and species with clumped distributions better than scattered dispersed species. Main conclusions The results from this study indicate that species’ geographical attributes highly influence the behaviour and uncertainty of species–climate models, which should be taken into account in biogeographical modelling studies and assessments of climate change impacts.     

Assessing spatial distribution of Coffea arabica L. in Ethiopia's highlands using species distribution models and geospatial analysis methods

Though there is an increase in popularity of predictive modelling for assessing the geographical distribution of species, there is still a clear gap on explaining geospatial methods to derive the presence/absence of species in terms of geospatial extent besides the ambiguity of robust models. In this paper, we evaluate four major species distribution modelling methods: Artificial Neural Network (ANN), Support Vector Machines (SVM), Maximum Entropy (MaxEnt) and Generalized Linear Model (GLM) with pseudo absence and background absence data. To investigate the efficacy of these models, we present a case study using Coffea arabica L. species in Ethiopia as there was no species distribution modelling that has been done at a local scale especially in the coffee growing areas. We made predictions on 75% subsets and validation on 25% of the 112 presence of the species records that were collected from field observation and 0.5 m spatial resolution of true colour aerial photographs. Twelve biophysical explanatory variables; climatic, remote sensing based and landscape variables were employed in modelling. The results show that MaxEnt with pseudo absence data and SVM with background absence have highest area of understory coffee presence prediction with 12.2% and 23.1% area coverage of indigenous forest, respectively. The result from the model performance test using True Positive Rate (TPR) shows that GLM and SVM with pseudo absence data performed highest (TPR = 0.821). MaxEnt and SVM were the robust modelling methods (TPR = 0.964) using background absence data.     

Where are all the fish: potential of biogeographical maps to project current and future distribution patterns of freshwater species

The dendritic structure of river networks is commonly argued against use of species atlas data for modeling freshwater species distributions, but little has been done to test the potential of grid-based data in predictive species mapping. Using four different niche-based models and three different climate change projections for the middle of the 21st century merged pairwise as well as within a consensus modeling framework, we studied the variability in current and future distribution patterns of 38 freshwater fish species across Germany. We used grid-based (11×11 km) fish distribution maps and numerous climatic, topographic, hydromorphologic, and anthropogenic factors derived from environmental maps at a finer scale resolution (250 m-1 km). Apart from the explicit predictor selection, our modeling framework included uncertainty estimation for all phases of the modeling process. We found that the predictive performance of some niche-based models is excellent independent of the predictor data set used, emphasizing the importance of a well-grounded predictor selection process. Though important, climate was not a primary key factor for any of the studied fish species groups, in contrast to substrate preferences, hierarchical river structure, and topography. Generally, distribution ranges of cold-water and warm-water species are expected to change significantly in the future; however, the extent of changes is highly uncertain. Finally, we show that the mismatch between the current and future ranges of climatic variables of more than 90% is the most limiting factor regarding reliability of our future estimates. Our study highlighted the underestimated potential of grid cell information in biogeographical modeling of freshwater species and provides a comprehensive modeling framework for predictive mapping of species distributions and evaluation of the associated uncertainties.     

Development and field validation of a regional,management‐scale habitat model: A koala Phascolarctos cinereus case study

Species distribution models have great potential to efficiently guide management for threatened species, especially for those that are rare or cryptic. We used MaxEnt to develop a regional‐scale model for the koala Phascolarctos cinereus at a resolution (250 m) that could be used to guide management. To ensure the model was fit for purpose, we placed emphasis on validating the model using independently‐collected field data. We reduced substantial spatial clustering of records in coastal urban areas using a 2‐km spatial filter and by modeling separately two subregions separated by the 500‐m elevational contour. A bias file was prepared that accounted for variable survey effort. Frequency of wildfire, soil type, floristics and elevation had the highest relative contribution to the model, while a number of other variables made minor contributions. The model was effective in discriminating different habitat suitability classes when compared with koala records not used in modeling. We validated the MaxEnt model at 65 ground‐truth sites using independent data on koala occupancy (acoustic sampling) and habitat quality (browse tree availability). Koala bellows (n = 276) were analyzed in an occupancy modeling framework, while site habitat quality was indexed based on browse trees. Field validation demonstrated a linear increase in koala occupancy with higher modeled habitat suitability at ground‐truth sites. Similarly, a site habitat quality index at ground‐truth sites was correlated positively with modeled habitat suitability. The MaxEnt model provided a better fit to estimated koala occupancy than the site‐based habitat quality index, probably because many variables were considered simultaneously by the model rather than just browse species. The positive relationship of the model with both site occupancy and habitat quality indicates that the model is fit for application at relevant management scales. Field‐validated models of similar resolution would assist in guiding management of conservation‐dependent species.     

Scaling of species distribution models across spatial resolutions and extents along a biogeographic gradient. The case of the Iberian mole Talpa occidentalis

Scaling is a key process in modelling approaches since it allows for translating information from one scale to another. However, the success of this procedure may depend on ‘source’ and ‘target’ scales, but also on the biogeographic/ecological context of the study area. We aimed to quantify the performance and success of scaling species distribution model (SDM) predictions across spatial resolution and extent along a biogeographic gradient using the Iberian mole as study case. We ran separate MaxEnt models at two extents (national and regional) using independent datasets (species locations and environmental predictors) collected at 10 km and 50 m resolutions respectively. Model performance and success of scaling SDMs were quantified on the basis of accuracy measures and spatial predictions. Complementarily, we calculated marginality and tolerance as indicators of habitat availability and niche truncation along the biogeographic gradient. Model performance increased with resolution and extent, as well as from north to south (mainly for high resolution models). When regional models were validated at different scales, their performance reduced severely, particularly in the case of coarse resolution models (some of them performed worse than random). However, when the 10 km‐national model was downscaled within regions, it performed better (AUC_test: 0.82, 0.85 and 0.55 respectively for Galicia, Madrid and Granada) than models specifically calibrated within each region at 10 km (0.47, 0.65, 0.44). Indeed, it also had a better accuracy when projected at 50 m (0.77, 0.91, 0.79) than models fitted at that resolution (0.62, 0.83, 0.96) in two of the three cases. The success of scaling model predictions decreased along the biogeographic gradient, being these differences associated to niche truncation. Models representing non‐truncated niches were more successfully scaled across resolutions and extents (particularly in areas not offering all possible habitats for species), which has important implications for SDM applications.     

Multi-scale approach for predicting fish species distributions across coral reef seascapes

Two of the major limitations to effective management of coral reef ecosystems are a lack of information on the spatial distribution of marine species and a paucity of data on the interacting environmental variables that drive distributional patterns. Advances in marine remote sensing, together with the novel integration of landscape ecology and advanced niche modelling techniques provide an unprecedented opportunity to reliably model and map marine species distributions across many kilometres of coral reef ecosystems. We developed a multi-scale approach using three-dimensional seafloor morphology and across-shelf location to predict spatial distributions for five common Caribbean fish species. Seascape topography was quantified from high resolution bathymetry at five spatial scales (5–300 m radii) surrounding fish survey sites. Model performance and map accuracy was assessed for two high performing machine-learning algorithms: Boosted Regression Trees (BRT) and Maximum Entropy Species Distribution Modelling (MaxEnt). The three most important predictors were geographical location across the shelf, followed by a measure of topographic complexity. Predictor contribution differed among species, yet rarely changed across spatial scales. BRT provided ‘outstanding’ model predictions (AUC = >0.9) for three of five fish species. MaxEnt provided ‘outstanding’ model predictions for two of five species, with the remaining three models considered ‘excellent’ (AUC = 0.8–0.9). In contrast, MaxEnt spatial predictions were markedly more accurate (92% map accuracy) than BRT (68% map accuracy). We demonstrate that reliable spatial predictions for a range of key fish species can be achieved by modelling the interaction between the geographical location across the shelf and the topographic heterogeneity of seafloor structure. This multi-scale, analytic approach is an important new cost-effective tool to accurately delineate essential fish habitat and support conservation prioritization in marine protected area design, zoning in marine spatial planning, and ecosystem-based fisheries management.     

Accounting for uncertainty in colonisation times: a novel approach to modelling the spatio‐temporal dynamics of alien invasions using distribution data

A novel, yet generic, Bayesian approach to parameter inference in a stochastic, spatio‐temporal model of dispersal and colonisation is developed and applied to the invasion of a region by an alien plant species. The method requires species distribution data from multiple time points, and accounts for temporal uncertainty in colonisation times inherent in such data. Covariates, such as climate parameters, altitude and land use, which capture variation in the suitability of sites for plant colonisation, are easily incorporated into the model. The method assumes no local extinction of occupied sites and thus is primarily applicable to modelling distribution data at relatively coarse spatial resolutions of plant species whose range is expanding over time. The implementation of the model and inference algorithm are illustrated through application to British floristic atlas data for the widespread alien Heracleum mantegazzianum (giant hogweed) assessed at a 10 × 10 km resolution in 1970 and 2000. We infer key characteristics of this species, predict its future spread, and use the resulting fitted model to inform a simulation‐based assessment of the methodology. Simulated distribution data are used to validate the inference algorithm. Our results suggest that the accuracy of inference is not sensitive to the number of distribution time points, requiring only that there are at least two points in time when distributions are mapped. We demonstrate the utility of the modelling approach by making future forecasts and historic hindcasts of the distribution of giant hogweed in Great Britain. Giant hogweed is one of the worst alien plants in Britain and has rapidly increased its range since 1970, yet we highlight that a further 20% of land area remains susceptible to colonisation by this species. We use the robustness of this case study to discuss the potential for modelling distribution data for other species and at different spatial scales.     

The effect of positional error on fine scale species distribution models increases for specialist species

Species occurrences inherently include positional error. Such error can be problematic for species distribution models (SDMs), especially those based on fine-resolution environmental data. It has been suggested that there could be a link between the influence of positional error and the width of the species ecological niche. Although positional errors in species occurrence data may imply serious limitations, especially for modelling species with narrow ecological niche, it has never been thoroughly explored. We used a virtual species approach to assess the effects of the positional error on fine-scale SDMs for species with environmental niches of different widths. We simulated three virtual species with varying niche breadth, from specialist to generalist. The true distribution of these virtual species was then altered by introducing different levels of positional error (from 5 to 500 m). We built generalized linear models and MaxEnt models using the distribution of the three virtual species (unaltered and altered) and a combination of environmental data at 5 m resolution. The models’ performance and niche overlap were compared to assess the effect of positional error with varying niche breadth in the geographical and environmental space. The positional error negatively impacted performance and niche overlap metrics. The amplitude of the influence of positional error depended on the species niche, with models for specialist species being more affected than those for generalist species. The positional error had the same effect on both modelling techniques. Finally, increasing sample size did not mitigate the negative influence of positional error. We showed that fine-scale SDMs are considerably affected by positional error, even when such error is low. Therefore, where new surveys are undertaken, we recommend paying attention to data collection techniques to minimize the positional error in occurrence data and thus to avoid its negative effect on SDMs, especially when studying specialist species.     

Modeling plant species distributions under future climates: how fine scale do climate projections need to be?

Recent studies suggest that species distribution models (SDMs) based on fine‐scale climate data may provide markedly different estimates of climate‐change impacts than coarse‐scale models. However, these studies disagree in their conclusions of how scale influences projected species distributions. In rugged terrain, coarse‐scale climate grids may not capture topographically controlled climate variation at the scale that constitutes microhabitat or refugia for some species. Although finer scale data are therefore considered to better reflect climatic conditions experienced by species, there have been few formal analyses of how modeled distributions differ with scale. We modeled distributions for 52 plant species endemic to the California Floristic Province of different life forms and range sizes under recent and future climate across a 2000‐fold range of spatial scales (0.008–16 km²). We produced unique current and future climate datasets by separately downscaling 4 km climate models to three finer resolutions based on 800, 270, and 90 m digital elevation models and deriving bioclimatic predictors from them. As climate‐data resolution became coarser, SDMs predicted larger habitat area with diminishing spatial congruence between fine‐ and coarse‐scale predictions. These trends were most pronounced at the coarsest resolutions and depended on climate scenario and species' range size. On average, SDMs projected onto 4 km climate data predicted 42% more stable habitat (the amount of spatial overlap between predicted current and future climatically suitable habitat) compared with 800 m data. We found only modest agreement between areas predicted to be stable by 90 m models generalized to 4 km grids compared with areas classified as stable based on 4 km models, suggesting that some climate refugia captured at finer scales may be missed using coarser scale data. These differences in projected locations of habitat change may have more serious implications than net habitat area when predictive maps form the basis of conservation decision making.