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.     

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.     

Downscaling land‐use data to provide global 30″ estimates of five land‐use classes

Land‐use change is one of the biggest threats to biodiversity globally. The effects of land use on biodiversity manifest primarily at local scales which are not captured by the coarse spatial grain of current global land‐use mapping. Assessments of land‐use impacts on biodiversity across large spatial extents require data at a similar spatial grain to the ecological processes they are assessing. Here, we develop a method for statistically downscaling mapped land‐use data that combines generalized additive modeling and constrained optimization. This method was applied to the 0.5° Land‐use Harmonization data for the year 2005 to produce global 30″ (approx. 1 km²) estimates of five land‐use classes: primary habitat, secondary habitat, cropland, pasture, and urban. The original dataset was partitioned into 61 bio‐realms (unique combinations of biome and biogeographical realm) and downscaled using relationships with fine‐grained climate, land cover, landform, and anthropogenic influence layers. The downscaled land‐use data were validated using the PREDICTS database and the geoWiki global cropland dataset. Application of the new method to all 61 bio‐realms produced global fine‐grained layers from the 2005 time step of the Land‐use Harmonization dataset. Coarse‐scaled proportions of land use estimated from these data compared well with those estimated in the original datasets (mean R²: 0.68 ± 0.19). Validation with the PREDICTS database showed the new downscaled land‐use layers improved discrimination of all five classes at PREDICTS sites (P < 0.0001 in all cases). Additional validation of the downscaled cropping layer with the geoWiki layer showed an R² improvement of 0.12 compared with the Land‐use Harmonization data. The downscaling method presented here produced the first global land‐use dataset at a spatial grain relevant to ecological processes that drive changes in biodiversity over space and time. Integrating these data with biodiversity measures will enable the reporting of land‐use impacts on biodiversity at a finer resolution than previously possible. Furthermore, the general method presented here could be useful to others wishing to downscale similarly constrained coarse‐resolution data for other environmental variables.     

A multi-scale looping approach to predict spatially dynamic patterns of functional species richness in changing landscapes

Land-use/land-cover (LU/LC) change is one of the main drivers of global biodiversity change. However, the lack of detailed data on species’ local distributions is frequently a major constraint to identify effective indicators of impact and to prescribe effective conservation and management measures. Here we aim to describe and demonstrate the applicability of a novel looping approach to predict spatially dynamic ecological responses to LU/LC changes. The methodology integrates statistical downscaling, multi-model inference, stochastic-dynamic modelling and simulations, and spatial projections under a common and interactive framework. We illustrate the approach with a study of passerine foraging groups and their potential indicator role under LU/LC change scenarios. Based on the coarse occurrence data from published atlases, this approach allowed transposing species richness to fine resolutions in order to assess regional ecological integrity by up scaling the local responses of those indicators again at the landscape level. Overall, our proposed framework was able to provide realistic patterns of passerine foraging responses to LU/LC changes, highlighting the usefulness of existing databases for model-based research in addressing complex emergent problems across scales. Comparative analysis between simulations and independent field data showed a promising model performance, with consistent projections of the local passerine functional composition for a significant number of point-counts tested. Our approach represents a contribution for more universal applications in the scope of conservation and landscape planning, especially when fine resolution data is difficult to obtain due to resources constraints.     

Finessing atlas data for species distribution models

Aim The spatial resolution of species atlases and therefore resulting model predictions are often too coarse for local applications. Collecting distribution data at a finer resolution for large numbers of species requires a comprehensive sampling effort, making it impractical and expensive. This study outlines the incorporation of existing knowledge into a conventional approach to predict the distribution of Bonelli’s eagle (Aquila fasciata) at a resolution 100 times finer than available atlas data. Location Malaga province, Andalusia, southern Spain. Methods A Bayesian expert system was proposed to utilize the knowledge from distribution models to yield the probability of a species being recorded at a finer resolution (1 × 1 km) than the original atlas data (10 × 10 km). The recorded probability was then used as a weight vector to generate a sampling scheme from the species atlas to enhance the accuracy of the modelling procedure. The maximum entropy for species distribution modelling (MaxEnt) was used as the species distribution model. A comparison was made between the results of the MaxEnt using the enhanced and, the random sampling scheme, based on four groups of environmental variables: topographic, climatic, biological and anthropogenic. Results The models with the sampling scheme enhanced by an expert system had a higher discriminative capacity than the baseline models. The downscaled (i.e. finer scale) species distribution maps using a hybrid MaxEnt/expert system approach were more specific to the nest locations and were more contrasted than those of the baseline model. Main conclusions The proposed method is a feasible substitute for comprehensive field work. The approach developed in this study is applicable for predicting the distribution of Bonelli’s eagle at a local scale from a national‐level occurrence data set; however, the usefulness of this approach may be limited to well‐known species.     

Using habitat models to map diversity: pan-African species richness of ticks (Acari: Ixodida)

Aims To show how logistic regression models for individual species can be used to produce improved estimates of species richness at a continental scale; to present these data for African ticks (Acari: Ixodida); and to address the question of whether there is a latitudinal gradient in tick species richness. Location Africa. Methods A database of 34,060 collection records for African ticks is used to produce a pan‐African map of known tick species richness at 0.25 × 0.25‐degree resolution. The likely distributions of seventy‐three species are then estimated from environmental factors using logistic regression, and localities where there is a suitably high probability of occurrence for a given species are added to the original data for that species. These augmented data are combined to produce a map of the predicted pan‐African distribution of tick species richness. The relationship of species richness to latitude is considered along a transect placed across some of the more extensively collected areas. Results Maps of known and predicted pan‐African tick species richness are presented, and deficiencies in the available data are highlighted. Correlations using both known and predicted estimates of tick species richness suggest that ticks follow similar species richness patterns to those described for African mammals and birds, with a latitudinal gradient and highest species richness in east equatorial Africa. Tick species ranges are log‐normally distributed. Main conclusions Carefully constructed probability surfaces offer a more powerful approach to mapping species ranges than simple presence‐absence maps. Such models are a useful extension to current biogeographical methods and have a wide range of potential applications in ecology, epidemiology and conservation. Tick species richness at a continental scale follows similar trends to those reported for mammals and birds.     

Squamate richness in the Brazilian Cerrado and its environmental–climatic associations

We investigate patterns of species richness of squamates (lizards, snakes, and amphisbaenians) in the Brazilian Cerrado, identifying areas of particularly high richness, and testing predictions of large‐scale richness hypotheses by analysing the relationship between species richness and environmental climatic variables. We used point localities from museum collections to produce maps of the predicted distributions for 237 Cerrado squamate species, using niche‐modelling techniques. We superimposed distributions of all species on a composite map, depicting richness across the ecosystem. Then, we performed a multiple regression analysis using eigenvector‐based spatial filtering (Principal Coordinate of Neighbour Matrices) to assess environmental–climatic variables that are best predictors of species richness. We found that the environmental–climatic and spatial filters multiple regression model explained 78% of the variation in Cerrado squamate richness (r² = 0.78; F = 32.66; P < 0.01). Best predictors of species richness were: annual precipitation, precipitation seasonality, altitude, net primary productivity, and precipitation during the driest quarter. A model selection approach revealed that several mechanisms related to the different diversity hypothesis might work together to explain richness variation in the Cerrado. Areas of higher species richness in Cerrado were located mainly in the south‐west, north, extreme east, and scattered areas in the north‐west portions of the biome. Partitioning of energy among species, habitat differentiation, and tolerance to variable environments may be the primary ecological factors determining variation in squamate richness across the Cerrado. High richness areas in northern Cerrado, predicted by our models, are still poorly sampled, and biological surveys are warranted in that region. The south‐western region of the Cerrado exhibits high species richness and is also undergoing high levels of deforestation. Therefore, maintenance of existing reserves, establishment of ecological corridors among reserves, and creation of new reserves are urgently needed to ensure conservation of species in these areas.     

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.     

Evaluating dominance as a component of non-native species invasions

Many studies have quantified plant invasions by determining patterns of non‐native species establishment (i.e. richness and absolute cover). Until recently, dominance has been largely overlooked as a significant component of invasion. Therefore, we re‐examined a 6‐year data set of 323 0.1 ha plots within 18 vegetation types collected in the Grand Staircase‐Escalante National Monument from 1998 to 2003, including dominance (i.e. relative cover) in our analyses. We specifically focused on the non‐native species Bromus tectorum, a notable dominant annual grass in this system. We found that non‐native species establishment and dominance are both occurring in species‐rich, mesic vegetation types. Therefore, non‐native species dominance may result despite many equally abundant native species rather than a dominant few, and competitive exclusion does not seem to be a primary control on either non‐native species establishment or dominance in this study. Unlike patterns observed for non‐native species establishment, relative non‐native species cover could not be predicted by native species richness across vegetation types (R² < 0.001; P = 0.45). However, non‐native species richness was found to be positively correlated with relative non‐native species cover and relative B. tectorum cover (R² = 0.46, P < 0.01; R² = 0.17, P < 0.01). Analyses within vegetation types revealed predominantly positive relationships among these variables for the correlations that were significant. Regression tree analyses across vegetation types that included additional biotic and abiotic variables were a little better at predicting non‐native species dominance (PRE = 0.49) and B. tectorum dominance (PRE = 0.39) than at predicting establishment. Land managers will need to set priorities for control efforts on the more productive, species‐rich vegetation types that appear to be susceptible to both components of invasion.     

National-scale conservation assessments at an appropriate resolution

Abstract.  Most national-scale conservation assessments are carried out at a resolution that is different from the actual size of protected areas in the study region. Coincidence between nature reserves and both hotspots (areas of high species richness) and complementary areas (sets of sites within which all species are represented) have been reported. However, the resolution (size of grid cells) of the species' distribution data upon which many of these studies are based is often close to an order of magnitude larger than the size of the reserves. Presumably, only a proportion of the species recorded in the coarse grid cells will actually be present on reserves. We use fine (2 × 2-km square grid cells) and coarse (10 × 10-km square grid cells) resolution data of national distributions for breeding birds throughout Great Britain, and presence data for avian species on Royal Society for the Protection of Birds (RSPB) nature reserves, to investigate the proportion of species in the local area (100 km²) that are actually present on reserves. RSPB reserves contain between 50% and 70% of species from the local area. These proportions are significantly higher than for randomly selected, non-reserve areas, indicating that RSPB reserves contain higher concentrations of bird species than the wider countryside. Furthermore, on RSPB reserves these proportions of threatened and non-threatened species are equal, whereas in non-reserve areas the proportions of non-threatened species are significantly higher than threatened species. Thus, reserves hold a higher proportion of threatened species than occurs in the wider countryside.     

Association of Ant Predators and Edaphic Conditions with Termite Diversity in an Amazonian Rain Forest

Predation is a key determinant of prey community structure, but few studies have measured the effect of multiple predators on a highly diverse prey community. In this study, we asked whether the abundance, species richness, and species composition of a species‐rich assemblage of termites in an Amazonian rain forest is more strongly associated with the density of predatory ants or with measures of vegetation, and soil texture and chemistry. We sampled termite assemblages with standardized hand‐collecting in 30 transects arranged in a 5 km × 6 km grid in a terra firme Amazonian rain forest. For each transect, we also measured vegetation structure, soil texture, and soil phosphorus, and estimated the density of predatory ants from baits, pitfall traps, and Winkler samples. Seventy‐nine termite species were recorded, and the total density of predatory ants was the strongest single predictor of local termite abundance (r = ?0.66) and termite species richness (r = ?0.44). In contrast, termite abundance and species richness were not strongly correlated with edaphic conditions (¦r¦ < 0.01), or with the density of non‐predatory ants (r_abund = ?0.27; r_s = ?0.06). Termite species composition was correlated with soil phosphorus content (r = 0.79), clay content (r = ?0.75), and tree density (r = ?0.42). Assemblage patterns were consistent with the hypothesis that ants collectively behaved as generalist predators, reducing total termite abundance, and species richness. There was no evidence that ants behaved as keystone predators, or that any single termite species benefited from the reduction in the abundance of potential competitors.     

A weak upward elevational shift in the distributions of breeding birds in the Italian Alps

Aim To test whether bird assemblages are shifting upwards in their elevational distribution in synchrony with current climate warming and/or habitat changes. Location A gradient of elevation in the Italian Alps (Alta Valsessera, Piedmont). Methods We used data from two recent atlas surveys performed on a 1 × 1 km grid at an 11‐year interval (1992–94 and 2003–05). We modelled the elevational gradient of avifaunal composition, using a sample‐based approach, in an effort to detect evidence for an upward elevational shift of bird zonation. Changes in species richness were controlled for. The results from this analysis were compared with those obtained using a species‐based approach. Changes in climate and landscape between the two surveys were assessed using local meteorological data and Corine Land Cover maps, respectively. Results We detected small avifaunal changes between the two surveys: (1) mean elevations increased for the majority of species, but the average change was not significantly different from zero; (2) the species richness increased, but this was mainly due to an increase in sampling effort; and (3) a change in species composition was detected, which was at the limit of significance and corresponded on average to a 29‐m upward elevational shift in the distribution of the avifauna. The shift was the same for open land and forest bird communities. During the same period, the mean temperature increased by c. 1 °C in the area, and a slight trend towards vegetation closure by woody plants was detected. Main conclusions The use of fine‐scale breeding bird atlases in mountainous regions, together with ordination methods, provides a sensitive tool to test and measure elevational shifts in species ranges, but the results have to be interpreted carefully. In our case, the observed elevational shift in the distributions of the avifauna cannot unambiguously be attributed to climate warming. This shift is smaller than expected from the regional increase in temperature, which raises the question of how closely bird distributions match climate change.     

Suppression of oxidative phosphorylation confers resistance against bevacizumab in experimental glioma

Although bevacizumab initially shows high response rates in gliomas and other tumours, therapy resistance usually develops later. Because anti‐angiogenic agents are supposed to induce hypoxia, we asked whether rendering glioma cells independent of oxidative phosphorylation modulates their sensitivity against hypoxia and bevacizumab. LNT‐229 glioma cells without functional mitochondria (rho⁰) and control (rho⁺) cells were generated. LNT‐229 rho⁰‐cells displayed reduced expression of oxidative phosphorylation‐related genes and diminished oxygen consumption. Conversely, glycolysis was up‐regulated in these cells, as shown by increased lactate production and stronger expression of glucose transporter‐1 and lactate dehydrogenase‐A. However, hypoxia‐induced cell death in vitro was nearly completely abolished in the LNT‐229 rho⁰‐cells, these cells were more sensitive towards glucose restriction and the treatment with the glycolysis inhibitor 2‐deoxy‐D‐glucose. In an orthotopic mouse xenograft experiment, bevacizumab induced hypoxia as reflected by elevated Hypoxia‐inducible factor 1‐alpha staining in both, rho⁺‐ and rho⁰‐tumours. However, it prolonged survival only in the mice bearing rho⁺‐tumours (74 days vs. 105 days, p  = 0.024 log‐rank test) and had no effect on survival in mice carrying LNT‐229 rho⁰‐tumours (75 days vs. 70 days, p  = 0.52 log‐rank test). Interestingly, inhibition of glycolysis in vivo with 2‐deoxy‐D‐glucose re‐established sensitivity of rho⁰‐tumours against bevacizumab (98 days vs. 80 days, p  = 0.0001). In summary, ablation of oxidative phosphorylation in glioma cells leads to a more glycolytic and hypoxia‐resistant phenotype and is sufficient to induce bevacizumab‐refractory tumours. These results add to increasing evidence that a switch towards glycolysis is one mechanism how tumour cells may evade anti‐angiogenic treatments and suggest anti‐glycolytic strategies as promising approaches to overcome bevacizumab resistance.

     

When invasion may not be harmful: niche relations in a lizard assemblage

Some studies have suggested that non‐native species invasions may threaten local diversity by creating homogenized environments. However, many studies have been based on limited or anecdotal data, and/or have failed to consider the influence of habitat modification together with possible influences of non‐native species on native ones. Hemidactylus mabouia (Squamata, Gekkonidae) likely invaded natural environments in Brazil hundreds of years ago. Yet, little is known about whether it affects native lizard fauna. We tested whether H. mabouia negatively influences native lizard species richness and abundance on a regional scale and locally through niche overlap. We analyzed species abundance and richness of nine lizard assemblages, in five of which H. mabouia occurred. We evaluated niche overlap of species in a lizard assemblage with high H. mabouia abundance through null models. Niche axes included spatial use, temporal activity and diet. Although species abundance did not differ among sites with and without the invasive species, the presence of H. mabouia seems constrained to the richer assemblages sampled. We observed significantly higher niche overlap in spatial (?_obs = 0.63; ?_exp = 0.37; P_obs ≥ P_exp = 0.0002) and trophic axes (?_obs = 0.46; ?_exp = 0.17; P_obs ≥ P_exp < 0.001), but not in activity. When we considered all axes (three‐dimensional niche), there was no overlapping among the lizard species. Our findings did not support the hypothesis that this non‐native species negatively influences other sympatric lizard species.     

Richness patterns, species distributions and the principle of extreme deconstruction

Aim To analyse the global patterns in species richness of Viperidae snakes through the deconstruction of richness into sets of species according to their distribution models, range size, body size and phylogenetic structure, and to test if environmental drivers explaining the geographical ranges of species are similar to those explaining richness patterns, something we called the extreme deconstruction principle. Location Global. Methods We generated a global dataset of 228 terrestrial viperid snakes, which included geographical ranges (mapped at 1° resolution, for a grid with 7331 cells world‐wide), body sizes and phylogenetic relationships among species. We used logistic regression (generalized linear model; GLM) to model species geographical ranges with five environmental predictors. Sets of species richness were also generated for large and small‐bodied species, for basal and derived species and for four classes of geographical range sizes. Richness patterns were also modelled against the five environmental variables through standard ordinary least squares (OLS) multiple regressions. These subsets are replications to test if environmental factors driving species geographical ranges can be directly associated with those explaining richness patterns. Results Around 48% of the total variance in viperid richness was explained by the environmental model, but richness sets revealed different patterns across the world. The similarity between OLS coefficients and the primacy of variables across species geographical range GLMs was equal to 0.645 when analysing all viperid snakes. Thus, in general, when an environmental predictor it is important to model species geographical ranges, this predictor is also important when modelling richness, so that the extreme deconstruction principle holds. However, replicating this correlation using subsets of species within different categories in body size, range size and phylogenetic structure gave more variable results, with correlations between GLM and OLS coefficients varying from –0.46 up to 0.83. Despite this, there is a relatively high correspondence (r = 0.73) between the similarity of GLM‐OLS coefficients and R² values of richness models, indicating that when richness is well explained by the environment, the relative importance of environmental drivers is similar in the richness OLS and its corresponding set of GLMs. Main conclusions The deconstruction of species richness based on macroecological traits revealed that, at least for range size and phylogenetic level, the causes underlying patterns in viperid richness differ for the various sets of species. On the other hand, our analyses of extreme deconstruction using GLM for species geographical range support the idea that, if environmental drivers determine the geographical distribution of species by establishing niche boundaries, it is expected, at least in theory, that the overlap among ranges (i.e. richness) will reveal similar effects of these environmental drivers. Richness patterns may be indeed viewed as macroecological consequences of population‐level processes acting on species geographical ranges.     

Spatial scale,topography and thermoregulatory behaviour interact when modelling species’ thermal niches

The spatial scale at which climate and species’ occupancy data are gathered, and the resolution at which ecological models are run, can strongly influence predictions of species performance and distributions. Running model simulations at coarse rather than fine spatial resolutions, for example, can determine if a model accurately predicts the distribution of a species. The impacts of spatial scale on a model's accuracy are particularly pronounced across mountainous terrain. Understanding how these discrepancies arise requires a modelling approach in which the underlying processes that determine a species’ distribution are explicitly described. Here we use a process‐based model to explore how spatial resolution, topography and behaviour alter predictions of a species thermal niche, which in turn constrains its survival and geographic distribution. The model incorporates biophysical equations to predict the operative temperature (T_e), thermal‐dependent performance and survival of a typical insect, with a complex life‐cycle, in its microclimate. We run this model with geographic data from a mountainous terrain in South Africa using climate data at three spatial resolutions. We also explore how behavioural thermoregulation affects predictions of a species performance and survival by allowing the animal to select the optimum thermal location within each coarse‐grid cell. At the regional level, coarse‐resolution models predicted lower T_e at low elevations and higher T_e at high elevations than models run at fine‐resolutions. These differences were more prominent on steep, north‐facing slopes. The discrepancies in T_e in turn affected estimates of the species thermal niche. The modelling framework revealed how spatial resolution and topography influence predictions of species distribution models, including the potential impacts of climate change. These systematic biases must be accounted for when interpreting the outputs of future modelling studies, particularly when species distributions are predicted to shift from uniform to topographically heterogeneous landscapes.     

Patterns of floristic richness in vegetation communities of California: regional scale analysis with multi-temporal NDVI

Aim Applying water‐energy dynamics and heterogeneity theory to explain species richness via remote sensing could allow for the regional characterization and monitoring of vegetation community assemblages and their environment. We assess the relationship of multi‐temporal normalized difference vegetation index (NDVI) to plant species richness in vegetation communities. Location California, USA. Methods Sub‐regions containing species inventories for chaparral, coastal sage scrub, foothill woodland, and yellow pine forest communities were intersected with a vegetation community map and an AVHRR NDVI time series for 1990, 1991, 1992, 1995 and 1996. Principal components analysis reduced the AVHRR data to three variables representing the sum and temporal trajectories of NDVI within each community. A fourth variable representing heterogeneity was tested using the standard deviation of the first component. Quadratic forms of these variables were also tested. Species richness was analysed by stepwise regression. Results Chaparral, coastal sage scrub, and yellow pine forest had the best relationships between species richness and NDVI. Richness of chaparral was related to NDVI heterogeneity and spring greenness (r² varied between 0.26 and 0.62 depending on year of NDVI data). Richness of coastal sage scrub was nonlinearly related to annual NDVI and heterogeneity (r² 0.63–0.81), with peak richness at intermediate values. Foothill woodland richness was related to heterogeneity in a monotonic curvilinear fashion (r² 0.28–0.35). Yellow pine forest richness was negatively related to spring greenness and positively related to heterogeneity (r² 0.40–0.46). Main Conclusions While NDVI's relationship to species richness varied, the selection of NDVI variables was generally consistent across years and indicated that spatial variability in NDVI may reflect important patterns in water‐energy use that affect plant species richness. The principal component axis that should correspond closely with annual mean NPP showed a less prominent role. We conclude that plant species richness for coarse vegetation associations can be characterized and monitored at a regional scale and over long periods of time using relatively coarse resolution NDVI data.     

Distorted perception of the spatial distribution of plant diversity through uneven collecting efforts: the example of Asteraceae in Australia

Aim Our aims were (1) to compare observed, estimated and predicted patterns of species richness using the Australian native Asteraceae as an example, (2) to identify candidates for hotspots of diversity for the study group, and (3) to examine the distortion of our perception of the spatial distribution of species richness through uneven or misdirected sampling efforts. Location Australia. Methods Based on data from Australia’s Virtual Herbarium, we calculated and visualized observed species richness, the Chao1 estimate of richness, the C index of collecting completeness, and an estimate of richness derived from environmental niche modelling for grid cells at a resolution of 1°. The 20 cells with the highest diversity values were used to define hotspots of diversity. Results Uneven collecting activity results in misleading diversity patterns for the family Asteraceae. While observed species richness is much higher in central Australia than in other parts of the arid interior, this is an artefact resulting from the area being a hotspot of collecting activity. The mountain ranges of south‐eastern Australia and Tasmania are candidates for unbiased hotspots of species richness. Main conclusions Vast areas of the Australian interior are insufficiently sampled on a local scale, although most of them can be expected to be relatively species poor. Some areas in the south‐east and south‐west of the continent remain undersampled relative to their high species richness. Observed species numbers, estimators and environmental niche‐modelling all have their unique advantages and disadvantages for the inference of patterns of diversity.     

Modelling distribution patterns in a species-rich plant genus, Anthurium (Araceae), in Ecuador

Modelling potential species distributions has become a powerful tool for botanists in recent years. Using herbarium specimen data and GIS desktop software, we modelled the potential distribution of 36 endemic and 47 non‐endemic species of Anthurium (Araceae) in Ecuador based on mean annual temperature and humidity. Our results indicate the most important region for endemics in western Ecuador lies between the Andes and Coastal mountain ranges between 200 and 700 m, while for eastern Ecuador a belt of potential high diversity occurs directly along the foothills of the Andes under 1000 m. A very interesting result of this study highlights a site of predicted high species diversity at the borders of Guyas, Cañar, Bolivar, and Chimborazo, as well as sites within the Cordillera del Condor along the border with Peru. Potential richness for non‐endemic Anthurium species was similar to that of endemics with the inclusion of a large area of Amazonian lowlands in the east of the country. Over 40% of the protected areas in Ecuador occur in the eastern Amazonian lowlands, an area of low diversity for Anthurium endemics. Overall, for areas with potential high concentrations of endemic species identified in this study, only 3.1% are within Ecuador's protected areas.