Patterns of species richness hotspots and estimates of their protection are sensitive to spatial resolution

Aim

Species richness is a measure of biodiversity often used in spatial conservation assessments and mapped by summing species distribution maps. Commission errors inherent those maps influence richness patterns and conservation assessments. We sought to further the understanding of the sensitivity of hotspot delineation methods and conservation assessments to commission errors, and choice of threshold for hotspot delineation.

Location

United States.

Methods

We created range maps and 30‐m and 1‐km resolution habitat maps for terrestrial vertebrates in the United States and generated species richness maps with each dataset. With the richness maps and the GAP Protected Areas Dataset, we created species richness hotspot maps and calculated the proportion of hotspots within protected areas; calculating protection under a range of thresholds for defining hotspots. Our method allowed us to identify the influence of commission errors by comparing hotspot maps.

Results

Commission errors from coarse spatial grain data and lack of porosity in the range data inflated richness estimates and altered their spatial patterns. Coincidence of hotspots from different data types was low. The 30‐m hotspots were spatially dispersed, and some were very long distances from the hotspots mapped with coarser data. Estimates of protection were low for each of the taxa. The relationship between estimates of hotspot protection and threshold choice was nonlinear and inconsistent among data types (habitat and range) and grain size (30‐m and 1‐km).

Main conclusions

Coarse mapping methods and grain sizes can introduce commission errors into species distribution data that could result in misidentifications of the regions where hotspots occur and affect estimates of hotspot protection. Hotspot conservation assessments are also sensitive to choice of threshold for hotspot delineation. There is value in developing species distribution maps with high resolution and low rates of commission error for conservation assessments.     

Assessing the accuracy of species distribution models to predict amphibian species richness patterns

1. Evaluating the distribution of species richness where biodiversity is high but has been insufficiently sampled is not an easy task. Species distribution modelling has become a useful approach for predicting their ranges, based on the relationships between species records and environmental variables. Overlapping predictions of individual distributions could be a useful strategy for obtaining estimates of species richness and composition in a region, but these estimates should be evaluated using a proper validation process, which compares the predicted richness values and composition with accurate data from independent sources. 2. In this study, we propose a simple approach to estimate model performance for several distributional predictions generated simultaneously. This approach is particularly suitable when species distribution modelling techniques that require only presence data are used. 3. The individual distributions for the 370 known amphibian species of Mexico were predicted using maxent to model data on their known presence (66,113 presence-only records). Distributions were subsequently overlapped to obtain a prediction of species richness. Accuracy was assessed by comparing the overall species richness values predicted for the region with observed and predicted values from 118 well-surveyed sites, each with an area of c. 100 km(2), which were identified using species accumulation curves and nonparametric estimators. 4. The derived models revealed a remarkable heterogeneity of species richness across the country, provided information about species composition per site and allowed us to obtain a measure of the spatial distribution of prediction errors. Examining the magnitude and location of model inaccuracies, as well as separately assessing errors of both commission and omission, highlights the inaccuracy of the predictions of species distribution models and the need to provide measures of uncertainty along with the model results. 5. The combination of a species distribution modelling method like maxent and species richness estimators offers a useful tool for identifying when the overall pattern provided by all model predictions might be representing the geographical patterns of species richness and composition, regardless of the particular quality or accuracy of the predictions for each individual species.     

A comparison of methods for mapping species ranges and species richness

Aim  Maps of species richness are the basis for applied research and conservation planning as well as for theoretical research investigating patterns of richness and the processes shaping these patterns. The method used to create a richness map could influence the results of such studies, but differences between these methods have been insufficiently evaluated. We investigate how different methods of mapping species ranges can influence patterns of richness, at three spatial resolutions.
Location  California, USA.
Methods  We created richness maps by overlaying individual species range maps for terrestrial amphibians and reptiles. The methods we used to create ranges included: point-to-grid maps, obtained by overlaying point observations of species occurrences with a grid and determining presence or absence for each cell; expert-drawn maps; and maps obtained through species distribution modelling. We also used a hybrid method that incorporated data from all three methods. We assessed the correlation and similarity of the spatial patterns of richness maps created with each of these four methods at three different resolutions.
Results  Richness maps created with different methods were more correlated at lower spatial resolutions than at higher resolutions. At all resolutions, point-to-grid richness maps estimated the lowest species richness and those derived from species distribution models the highest. Expert-drawn maps and hybrid maps showed intermediate levels of richness but had different spatial patterns of species richness from those derived with the other methods.
Main conclusions  Even in relatively well-studied areas such as California, different data sources can lead to rather dissimilar maps of species richness. Evaluating the strengths and weaknesses of different methods for creating a richness map can provide guidance for selecting the approach that is most appropriate for a given application and region.     

Range maps and species richness patterns: errors of commission and estimates of uncertainty

Range maps are often combined into “range overlap maps” to estimate spatial variation in species richness. Range maps are, in most cases, designed to represent a species’ maximum geographical extent and not patterns of occupancy within the range. As a consequence, range maps overestimate occupancy by presenting false occupancy (errors of commission) within the interior of the range. To assess the implications of errors of commission when developing and applying range overlap maps, we used neutral landscapes to simulate range maps and patterns of occupancy within ranges. We explored several scenarios based on combinations of six parameters defining biogeographical and cartographic factors typically encountered by investigators. Our results suggest that, in general, uncertainty is lowest when map resolutions are moderately fine, the majority of species have geographically restricted ranges, species occur throughout their range, patterns of occupancy within the range are not correlated among species, and geographically local and widespread species tend to occupy different regions. Several of these outcomes are associated with broad geographical extents, the scale at which range overlap maps are typically applied. Thus, under most circumstances, reasonably accurate and precise representation of species richness patterns can be achieved. However, these representations can be improved by enhancing occupancy data for widespread species – a primary source of uncertainty – and selecting a map resolution that captures relevant biological and environmental heterogeneity. Hence, by determining where a study is situated within the scenarios examined in our simulations, uncertainty and its sources and implications can be ascertained. With this knowledge, research goals, methods, and data sources can be reassessed and refined and, in the end, conclusions and recommendations can be qualified. Alternatively, unique regional, taxonomic, or cartographic factors could be included in future simulations to provide direct assessments of uncertainty.     

When the method for mapping species matters: defining priority areas for conservation of African freshwater turtles

Aim Defining priority areas for conservation is essential to minimize biodiversity loss, but the adoption of different methods for describing species distributions influences the outcomes. In order to provide a robust basis for the conservation of freshwater turtles in Africa, we compared the effect that different species‐mapping approaches had on derived patterns of species richness, species vulnerability and protected‐area representativeness. Location Africa. Methods We adopted three different approaches with increasing complexity for generating species distribution maps. The first approach was based on the geographic intersection of species records and grid squares; the second on the union of local convex polygons; and the third on inductive distribution modelling techniques. We used distribution maps, generated using these three approaches, to determine conservation priorities based on geographic patterns of species richness and vulnerability, as well as for conducting gap and irreplaceability analyses. Results We obtained markedly different distribution maps using the three methods, which in turn caused differences in conservation priorities. The grid‐square approach underestimated range sizes and species richness, while the polygon approach overestimated these attributes. The distribution modelling approach provided the most realistic outcome in terms of diversity patterns, by minimizing both commission and omission errors. An integrated map of conservation priority – derived by combining individual measures of priority based on the distribution modelling approach – identified the Gulf of Guinea coast and the Albertine Rift as major priority areas. Main conclusions Each species‐mapping approach has both advantages and disadvantages. The choice of the most appropriate approach in any given situation depends on the availability of locality records and on the relative importance of mitigating omission and commission errors. Our findings suggest that in most circumstances, the use of distribution modelling has many advantages relative to the other approaches. The priority areas identified in this study should be considered for targeting efforts to conserve Africa freshwater turtles in the coming years.     

Unprotecting the rare species: a niche‐based gap analysis for odonates in a core Cerrado area

Aim We evaluated Odonata distribution data and predicted the compositional resemblance based on niche‐based species distribution models to analyse the following questions: (1) How is estimated species richness distributed, and how can it be preserved under the actual network of conservation units (a gap analysis approach)? (2) How is the estimated odonate beta diversity distributed, and is there a better distribution of conservation units (a priority setting approach)? (3) Is the probability of being under protection a function of the potential species range size? and (4) Will the current conservation network proposals protect odonate taxa? Location Central Brazil in a core Cerrado area. Methods We generated odonate species distribution predictions based on MaxEnt and maps derived from estimated species richness, beta diversity and gap analysis for all species predicted to occur in the study area. Then, we compared these maps with current conservation units, land‐use patterns and proposals for the establishment of conservation units. Results Raw odonate species records provided limited utility for setting conservation priorities without the use of niche‐based models. However, area under the receiver operating curve (AUC) values were characterized by substantial variation that was related to the number of records. No current conservation units overlapped the areas with higher predicted richness and beta diversity, and in general, conservation units were not preserving restricted/rare species. There was a direct linear correlation between species range size and the proportion of its range protected in the current network of conservation units. Finally, we identified three areas with high odonate beta diversity where conservationist actions should be implemented. Main conclusions Current conservation units and future suggested areas do not overlap regions with high conservation values for odonates. Conservation units protect species at random, and the level of protection has a direct relationship with species range size; thus, wide‐range species are expected to be more protected than restricted or threatened species.     

The effect of energy and seasonality on avian species richness and community composition

We analyzed geographic patterns of richness in both the breeding and winter season in relation to a remotely sensed index of seasonal production (normalized difference vegetation index [NDVI]) and to measures of habitat heterogeneity at four different spatial resolutions. The relationship between avian richness and NDVI was consistent between seasons, suggesting that the way in which available energy is converted to bird species is similar at these ecologically distinct times of year. The number and proportion of migrant species in breeding communities also increased predictably with the degree of seasonality. The NDVI was a much better predictor of seasonal richness at finer spatial scales, whereas habitat heterogeneity best predicted richness at coarser spatial resolutions. While we find strong support for a positive relationship between available energy and species richness, seasonal NDVI explained at most 61% of the variation in richness. Seasonal NDVI and habitat heterogeneity together explain up to 69% of the variation in richness.     

Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches

Aim This study compares the direct, macroecological approach (MEM) for modelling species richness (SR) with the more recent approach of stacking predictions from individual species distributions (S‐SDM). We implemented both approaches on the same dataset and discuss their respective theoretical assumptions, strengths and drawbacks. We also tested how both approaches performed in reproducing observed patterns of SR along an elevational gradient. Location Two study areas in the Alps of Switzerland. Methods We implemented MEM by relating the species counts to environmental predictors with statistical models, assuming a Poisson distribution. S‐SDM was implemented by modelling each species distribution individually and then stacking the obtained prediction maps in three different ways – summing binary predictions, summing random draws of binomial trials and summing predicted probabilities – to obtain a final species count. Results The direct MEM approach yields nearly unbiased predictions centred around the observed mean values, but with a lower correlation between predictions and observations, than that achieved by the S‐SDM approaches. This method also cannot provide any information on species identity and, thus, community composition. It does, however, accurately reproduce the hump‐shaped pattern of SR observed along the elevational gradient. The S‐SDM approach summing binary maps can predict individual species and thus communities, but tends to overpredict SR. The two other S‐SDM approaches – the summed binomial trials based on predicted probabilities and summed predicted probabilities – do not overpredict richness, but they predict many competing end points of assembly or they lose the individual species predictions, respectively. Furthermore, all S‐SDM approaches fail to appropriately reproduce the observed hump‐shaped patterns of SR along the elevational gradient. Main conclusions Macroecological approach and S‐SDM have complementary strengths. We suggest that both could be used in combination to obtain better SR predictions by following the suggestion of constraining S‐SDM by MEM predictions.     

Tradeoffs of different types of species occurrence data for use in systematic conservation planning

Data on the occurrence of species are widely used to inform the design of reserve networks. These data contain commission errors (when a species is mistakenly thought to be present) and omission errors (when a species is mistakenly thought to be absent), and the rates of the two types of error are inversely related. Point locality data can minimize commission errors, but those obtained from museum collections are generally sparse, suffer from substantial spatial bias and contain large omission errors. Geographic ranges generate large commission errors because they assume homogenous species distributions. Predicted distribution data make explicit inferences on species occurrence and their commission and omission errors depend on model structure, on the omission of variables that determine species distribution and on data resolution. Omission errors lead to identifying networks of areas for conservation action that are smaller than required and centred on known species occurrences, thus affecting the comprehensiveness, representativeness and efficiency of selected areas. Commission errors lead to selecting areas not relevant to conservation, thus affecting the representativeness and adequacy of reserve networks. Conservation plans should include an estimation of commission and omission errors in underlying species data and explicitly use this information to influence conservation planning outcomes.     

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.     

Misuse of bird digital distribution maps creates reversed spatial diversity patterns in the Amazon

It is well known that bird richness in the Amazon is greater in upland forests and that seasonally flooded forest is particularly species poor. However, the misleading pattern of greater bird richness in seasonally flooded forest has emerged seemingly unnoticed numerous times in richness maps in the literature. We hypothesize that commission errors in digital distribution maps (DDMs) are the cause behind the misleading richness pattern. In the Amazon, commission errors are a consequence of the different methodological treatment given to large‐ranged versus small‐ranged habitat specialists when mapping distributions. DDMs of 1007 Amazonian birds were examined, and maps that had commission errors were corrected. We generated two richness maps, one from the overlay of original DDMs and another from the overlay of the corrected ones. We identified 291 species whose distribution maps had errors. In the original data, seasonally flooded forests showed higher species richness than upland forest, but this pattern was reverted in the corrected richness map. Commission errors were 35 times more likely in the seasonally flooded forest. We conclude that DDMs accurately portray the distribution of single species in the Amazon. Commission errors in individual maps, however, accumulate when they are overlaid, explaining the misleading pattern for birds in the Amazon. DDMs can continue to be used mapping richness, as long as, at a regional scale: (1) basic map refinements are carried, or (2) only small‐range species are used for mapping species richness.     

Weak links: 'Rapoport's rule' and large-scale species richness patterns

Many hypotheses have been proposed to explain regional species richness patterns. Among these, ‘Rapoport's rule’ has sparked considerable controversy by stating that the latitudinal gradient in species richness can be explained indirectly as a function of narrower geographic ranges for species at low latitudes. Annual climatic variability, or deviation from mean climatic conditions, has been hypothesized to moderate this phenomenon. Furthermore, taxa that avoid much of this seasonality, such as temperate zone insects that enter diapause or species that migrate, were predicted to show reduced latitudinal gradients in richness. I test the suggested link between ‘Rapoport's rule’ and species richness for two higher level insect taxa as well as for the class Mammalia. Although these taxa exhibit the well-known latitudinal gradient in species richness, simple annual climatic variability and deviation from mean annual climatic conditions provide very poor predictions of species richness in each of them. Potential evapotranspiration, a measurement of ambient climatic energy, explains most of the observed variance in regional species richness patterns for all three taxa, consistent with the species richness-energy hypothesis. I find no support for an indirect link between ‘Rapoport's rule’ and terrestrial species richness patterns in North America.     

From species distributions to meta‐communities

The extent that biotic interactions and dispersal influence species ranges and diversity patterns across scales remains an open question. Answering this question requires framing an analysis on the frontier between species distribution modelling (SDM), which ignores biotic interactions and dispersal limitation, and community ecology, which provides specific predictions on community and meta‐community structure and resulting diversity patterns such as species richness and functional diversity. Using both empirical and simulated datasets, we tested whether predicted occurrences from fine‐resolution SDMs provide good estimates of community structure and diversity patterns at resolutions ranging from a resolution typical of studies within reserves (250 m) to that typical of a regional biodiversity study (5 km). For both datasets, we show that the imprint of biotic interactions and dispersal limitation quickly vanishes when spatial resolution is reduced, which demonstrates the value of SDMs for tracking the imprint of community assembly processes across scales.     

Patterns and causes of species richness: a general simulation model for macroecology

Understanding the causes of spatial variation in species richness is a major research focus of biogeography and macroecology. Gridded environmental data and species richness maps have been used in increasingly sophisticated curve‐fitting analyses, but these methods have not brought us much closer to a mechanistic understanding of the patterns. During the past two decades, macroecologists have successfully addressed technical problems posed by spatial autocorrelation, intercorrelation of predictor variables and non‐linearity. However, curve‐fitting approaches are problematic because most theoretical models in macroecology do not make quantitative predictions, and they do not incorporate interactions among multiple forces. As an alternative, we propose a mechanistic modelling approach. We describe computer simulation models of the stochastic origin, spread, and extinction of species’ geographical ranges in an environmentally heterogeneous, gridded domain and describe progress to date regarding their implementation. The output from such a general simulation model (GSM) would, at a minimum, consist of the simulated distribution of species ranges on a map, yielding the predicted number of species in each grid cell of the domain. In contrast to curve‐fitting analysis, simulation modelling explicitly incorporates the processes believed to be affecting the geographical ranges of species and generates a number of quantitative predictions that can be compared to empirical patterns. We describe three of the ‘control knobs’ for a GSM that specify simple rules for dispersal, evolutionary origins and environmental gradients. Binary combinations of different knob settings correspond to eight distinct simulation models, five of which are already represented in the literature of macroecology. The output from such a GSM will include the predicted species richness per grid cell, the range size frequency distribution, the simulated phylogeny and simulated geographical ranges of the component species, all of which can be compared to empirical patterns. Challenges to the development of the GSM include the measurement of goodness of fit (GOF) between observed data and model predictions, as well as the estimation, optimization and interpretation of the model parameters. The simulation approach offers new insights into the origin and maintenance of species richness patterns, and may provide a common framework for investigating the effects of contemporary climate, evolutionary history and geometric constraints on global biodiversity gradients. With further development, the GSM has the potential to provide a conceptual bridge between macroecology and historical biogeography.     

Type and spatial structure of distribution data and the perceived determinants of geographical gradients in ecology: the species richness of African birds

Aim Studies exploring the determinants of geographical gradients in the occurrence of species or their traits obtain data by: (1) overlaying species range maps; (2) mapping survey‐based species counts; or (3) superimposing models of individual species’ distributions. These data types have different spatial characteristics. We investigated whether these differences influence conclusions regarding postulated determinants of species richness patterns. Location Our study examined terrestrial bird diversity patterns in 13 nations of southern and eastern Africa, spanning temperate to tropical climates. Methods Four species richness maps were compiled based on range maps, field‐derived bird atlas data, logistic and autologistic distribution models. Ordinary and spatial regression models served to examine how well each of five hypotheses predicted patterns in each map. These hypotheses propose productivity, temperature, the heat–water balance, habitat heterogeneity and climatic stability as the predominant determinants of species richness. Results The four richness maps portrayed broadly similar geographical patterns but, due to the nature of underlying data types, exhibited marked differences in spatial autocorrelation structure. These differences in spatial structure emerged as important in determining which hypothesis appeared most capable of explaining each map's patterns. This was true even when regressions accounted for spurious effects of spatial autocorrelation. Each richness map, therefore, identified a different hypothesis as the most likely cause of broad‐scale gradients in species diversity. Main conclusions Because the ‘true’ spatial structure of species richness patterns remains elusive, firm conclusions regarding their underlying environmental drivers remain difficult. More broadly, our findings suggest that care should be taken to interpret putative determinants of large‐scale ecological gradients in light of the type and spatial characteristics of the underlying data. Indeed, closer scrutiny of these underlying data — here the distributions of individual species — and their environmental associations may offer important insights into the ultimate causes of observed broad‐scale patterns.     

Scaling down distribution maps from atlas data: a test of different approaches with virtual species

Aim Conservation managers are increasingly looking for modelled projections of species distributions to inform management strategies; however, the coarse resolution of available data usually compromises their helpfulness. The aim of this paper is to delineate and test different approaches for converting coarse‐grain occurrence data into high‐resolution predictions, and to clarify the conceptual circumstances affecting the accuracy of downscaled models. Location We used environmental data from a real landscape, southern Africa, and simulated species distributions within this landscape. Methods We built 10 virtual species at a resolution of 5 arcmin, and for each species we simulated atlas range maps at four decreasing resolutions (15, 30, 60, 120 arcmin). We tested the ability of three downscaling strategies to produce high‐resolution predictions using two modelling techniques: generalized linear models and generalized boosted models. We calibrated reference models with high‐resolution data and we compared the relative reduction of predictive performance in the downscaled models by using a null model approach. We also estimated the applicability of downscaling procedures to different situations by using distribution data for Mediterranean reptiles. Results All reference models achieved high performance measures. For all strategies, we observed a reduction of predictive performance proportional to the degree of downscaling. The differences in evaluation indices between reference models and downscaled projections obtained from atlases at 15 and 30 arcmin were never statistically significant. The accuracy of projections scaled down from 60 arcmin largely depended on the combination of approach and algorithm adopted. Projections scaled down from 120 arcmin gave misleading results in all cases. Main conclusions Moderate levels of downscaling allow for reasonably accurate results, regardless of the technique used. The most general effect of scaling down coarse‐grain data is the reduction of model specificity. The models can successfully delineate a species’ environmental association up until a 12‐fold downscaling, although with an increasing approximation that causes the overestimation of true distributions. We suggest appropriate procedures to mitigate the commission error introduced by downscaling at intermediate levels (approximately 12‐fold). Reductions of grain size > 12‐fold are discouraged.     

Mapping Tropical Forest Trees Using High‐Resolution Aerial Digital Photographs

The spatial arrangement of tree species is a key aspect of community ecology. Because tree species in tropical forests occur at low densities, it is logistically challenging to measure distributions across large areas. In this study, we evaluated the potential use of canopy tree crown maps, derived from high‐resolution aerial digital photographs, as a relatively simple method for measuring large‐scale tree distributions. At Barro Colorado Island, Panama, we used high‐resolution aerial digital photographs (~0.129 m/pixel) to identify tree species and map crown distributions of four target tree species. We determined crown mapping accuracy by comparing aerial and ground‐mapped distributions and tested whether the spatial characteristics of the crown maps reflect those of the ground‐mapped trees. Nearly a quarter (22%) of the common canopy species had sufficiently distinctive crowns to be good candidates for reliable mapping. The errors of commission (crowns misidentified as a target species) were relatively low, but the errors of omission (missed canopy trees of the target species) were high. Only 40 percent of canopy individuals were mapped on the air photographs. Despite failing to accurately predict exact abundances of canopy trees, crown distributions accurately reproduced the clumping patterns and spatial autocorrelation features of three of four tree species and predicted areas of high and low abundance. We discuss a range of ecological and forest management applications for which this method can be useful.     

From topography to hydrology—The modifiable area unit problem impacts freshwater species distribution models

Species distribution models (SDMs) are statistical tools to identify potentially suitable habitats for species. For SDMs in river ecosystems, species occurrences and predictor data are often aggregated across subcatchments that serve as modeling units. The level of aggregation (i.e., model resolution) influences the statistical relationships between species occurrences and environmental predictors—a phenomenon known as the modifiable area unit problem (MAUP), making model outputs directly contingent on the model resolution. Here, we test how model performance, predictor importance, and the spatial congruence of species predictions depend on the model resolution (i.e., average subcatchment size) of SDMs. We modeled the potential habitat suitability of 50 native fish species in the upper Danube catchment at 10 different model resolutions. Model resolutions were derived using a 90‐m digital‐elevation model by using the GRASS‐GIS module r.watershed. Here, we decreased the average subcatchment size gradually from 632 to 2 km². We then ran ensemble SDMs based on five algorithms using topographical, climatic, hydrological, and land‐use predictors for each species and resolution. Model evaluation scores were consistently high, as sensitivity and True Skill Statistic values ranged from 86.1–93.2 and 0.61–0.73, respectively. The most contributing predictor changed from topography at coarse, to hydrology at fine resolutions. Climate predictors played an intermediate role for all resolutions, while land use was of little importance. Regarding the predicted habitat suitability, we identified a spatial filtering from coarse to intermediate resolutions. The predicted habitat suitability within a coarse resolution was not ported to all smaller, nested subcatchments, but only to a fraction that held the suitable environmental conditions. Across finer resolutions, the mapped predictions were spatially congruent without such filter effect. We show that freshwater SDM predictions can have consistently high evaluation scores while mapped predictions differ significantly and are highly contingent on the underlying subcatchment size. We encourage building freshwater SDMs across multiple catchment sizes, to assess model variability and uncertainties in model outcomes emerging from the MAUP.     

Disparity between range map- and survey-based analyses of species richness: patterns, processes and implications

Species richness patterns are characterized either by overlaying species range maps or by compiling geographically extensive survey data for multiple local communities. Although, these two approaches are clearly related, they need not produce identical richness patterns because species do not occur everywhere in their geographical range. Using North American breeding birds, we present the first continent‐wide comparison of survey and range map data. On average, bird species were detected on 40.5% of the surveys within their range. As a result of this range porosity, the geographical richness patterns differed markedly, with the greatest disparity in arid regions and at higher elevations. Environmental productivity was a stronger predictor of survey richness, while elevational heterogeneity was more important in determining range map richness. In addition, range map richness exhibited greater spatial autocorrelation and lower estimates of spatial turnover in species composition. Our results highlight the fact that range map richness represents species coexistence at a much coarser scale than survey data, and demonstrate that the conclusions drawn from species richness studies may depend on the data type used for analyses.     

Using potential distributions to explore determinants of Western Palaearctic migratory songbird species richness in sub-Saharan Africa

Aim  Although the breeding ranges of most Western Palaearctic migratory passerines are well documented in Europe, their overwintering ranges and patterns of species richness in Africa remain poorly understood. To illustrate potential patterns of species richness despite severely limited data, we extrapolated species ranges from a new and unique data bank of locality records that documents overwintering locations of these birds in Africa.
Location  Sub-Saharan Africa.
Methods  We predicted potential geographical distributions of 60 species of passerine birds based on overwintering records using bioclimatic models. We then combined these predictions to estimate potential species richness and explored response shapes using spatial linear regression. We also evaluated the evidence for a mid-domain effect using a one-dimensional null model.
Results  Spatial linear regression analyses of the species richness pattern revealed non-linear relationships to seasonality in precipitation, minimum net primary productivity, minimum average temperature, habitat heterogeneity, percentage of tree cover, distance from the Sahara Desert and inter-annual variability in net primary productivity. The explanatory power of these variables decreased with geographic range size. The one-dimensional null model of species richness based on distance from the Sahara Desert did not show evidence of a mid-domain effect.
Main conclusions  Distributions of migrants seem generally strongly determined by distance from the Sahara Desert working in concert with climatic effects, but this cannot adequately explain richness patterns of species with small ranges in Africa, many of which are of substantial conservation concern.