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.     

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.     

Species distribution modelling for the people: unclassified landsat TM imagery predicts bird occurrence at fine resolutions

Aim

Assessing the influence of land cover in species distribution modelling is limited by the availability of fine‐resolution land‐cover data appropriate for most species responses. Remote‐sensing technology offers great potential for predicting species distributions at large scales, but the cost and required expertise are prohibitive for many applications. We test the usefulness of freely available raw remote‐sensing reflectance data in predicting species distributions of 40 commonly occurring bird species in western Oregon.

Location

Central Coast Range, Cascade and Klamath Mountains Oregon, USA.

Methods

Information on bird observations was collected from 4598 fixed‐radius point counts. Reflectance data were obtained using 30‐m resolution Landsat imagery summarized at scales of 150, 500, 1000 and 2000 m. We used boosted regression tree (BRT) models to analyse relationships between distributions of birds and reflectance values and evaluated prediction performance of the models using area under the receiver operating characteristic curve (AUC) values.

Results

Prediction success of models using all reflectance values was high (mean AUC = 0.79 ± 0.10 SD). Further, model performance using individual reflectance bands exceeded those that used only Normalized Difference Vegetation Index (NDVI). The relative influence of band 4 predictors was highest, indicating the importance of variables associated with vegetation biomass and photosynthetic activity. Across spatial scales, the average influence of predictors at the 2000 m scale was greatest.

Main Conclusions

We demonstrate that unclassified remote‐sensing imagery can be used to produce species distribution models with high prediction success. Our study is the first to identify general patterns in the usefulness of spectral reflectances for species distribution modelling of multiple species. We conclude that raw Landsat Thematic Mapper data will be particularly useful in species distribution models when high‐resolution predictions are required, including habitat change detection studies, identification of fine‐scale biodiversity hotspots and reserve design.

     

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.     

Biotic interactions improve prediction of boreal bird distributions at macro-scales

Aim The role of biotic interactions in influencing species distributions at macro‐scales remains poorly understood. Here we test whether predictions of distributions for four boreal owl species at two macro‐scales (10 × 10 km and 40 × 40 km grid resolutions) are improved by incorporating interactions with woodpeckers into climate envelope models. Location Finland, northern Europe. Methods Distribution data for four owl and six woodpecker species, along with data for six land cover and three climatic variables, were collated from 2861 10 × 10 km grid cells. Generalized additive models were calibrated using a 50% random sample of the species data from western Finland, and by repeating this procedure 20 times for each of the four owl species. Models were fitted using three sets of explanatory variables: (1) climate only; (2) climate and land cover; and (3) climate, land cover and two woodpecker interaction variables. Models were evaluated using three approaches: (1) examination of explained deviance; (2) four‐fold cross‐validation using the model calibration data; and (3) comparison of predicted and observed values for independent grid cells in eastern Finland. The model accuracy for approaches (2) and (3) was measured using the area under the curve of a receiver operating characteristic plot. Results At 10‐km resolution, inclusion of the distribution of woodpeckers as a predictor variable significantly improved the explanatory power, cross‐validation statistics and the predictive accuracy of the models. Inclusion of land cover led to similar improvements at 10‐km resolution, although these improvements were less apparent at 40‐km resolution for both land cover and biotic interactions. Main conclusions Predictions of species distributions at macro‐scales may be significantly improved by incorporating biotic interactions and land cover variables into models. Our results are important for models used to predict the impacts of climate change, and emphasize the need for comprehensive evaluation of the reliability of species–climate impact models.     

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.     

The role of climate,water and biotic interactions in shaping biodiversity patterns in arid environments across spatial scales

Aim

Desert ecosystems, with their harsh environmental conditions, hold the key to understanding the responses of biodiversity to climate change. As desert community structure is influenced by processes acting at different spatial scales, studies combining multiple scales are essential for understanding the conservation requirements of desert biota. We investigated the role of environmental variables and biotic interactions in shaping broad and fine‐scale patterns of diversity and distribution of bats in arid environments to understand how the expansion of nondesert species can affect the long‐term conservation of desert biodiversity.

Location

Levant, Eastern Mediterranean.

Methods

We combine species distribution modelling and niche overlap statistics with a statistical model selection approach to integrate interspecific interactions into broadscale distribution models and fine‐scale analysis of ecological requirements. We focus on competition between desert bats and mesic species that recently expanded their distribution into arid environment following anthropogenic land‐use changes.

Results

We show that both climate and water availability limit bat distributions and diversity across spatial scales. The broadscale distribution of bats was determined by proximity to water and high temperatures, although the latter did not affect the distribution of mesic species. At the fine‐scale, high levels of bat activity and diversity were associated with increased water availability and warmer periods. Desert species were strongly associated with warmer and drier desert types. Range and niche overlap were high among potential competitors, but coexistence was facilitated through fine‐scale spatial partitioning of water resources.

Main conclusions

Adaptations to drier and warmer conditions allow desert‐obligate species to prevail in more arid environments. However, this competitive advantage may disappear as anthropogenic activities encroach further into desert habitats. We conclude that reduced water availability in arid environments under future climate change projections pose a major threat to desert wildlife because it can affect survival and reproductive success and may increase competition over remaining water resources.     

Beyond climate control on species range: The importance of soil data to predict distribution of Amazonian plant species

Aim

To evaluate the relative importance of climatic versus soil data when predicting species distributions for Amazonian plants and to gain understanding of potential range shifts under climate change.

Location

Amazon rain forest.

Methods

We produced species distribution models (SDM) at 5‐km spatial resolution for 42 plant species (trees, palms, lianas, monocot herbs and ferns) using species occurrence data from herbarium records and plot‐based inventories. We modelled species distribution with Bayesian logistic regression using either climate data only, soil data only or climate and soil data together to estimate their relative predictive powers. For areas defined as unsuitable to species occurrence, we mapped the difference between the suitability predictions obtained with climate‐only versus soil‐only models to identify regions where climate and soil might restrict species ranges independently or jointly.

Results

For 40 out of the 42 species, the best models included both climate and soil predictors. The models including only soil predictors performed better than the models including only climate predictors, but we still detected a drought‐sensitive response for most of the species. Edaphic conditions were predicted to restrict species occurrence in the centre, the north‐west and in the north‐east of Amazonia, while the climatic conditions were identified as the restricting factor in the eastern Amazonia, at the border of Roraima and Venezuela and in the Andean foothills.

Main conclusions

Our results revealed that soil data are a more important predictor than climate of plant species range in Amazonia. The strong control of species ranges by edaphic features might reduce species’ abilities to track suitable climate conditions under a drought‐increase scenario. Future challenges are to improve the quality of soil data and couple them with process‐based models to better predict species range dynamics under climate change.     

The performance of range maps and species distribution models representing the geographic variation of species richness at different resolutions

Aim The method used to generate hypotheses about species distributions, in addition to spatial scale, may affect the biodiversity patterns that are then observed. We compared the performance of range maps and MaxEnt species distribution models at different spatial resolutions by examining the degree of similarity between predicted species richness and composition against observed values from well‐surveyed cells (WSCs). Location Mexico. Methods We estimated amphibian richness distributions at five spatial resolutions (from 0.083° to 2°) by overlaying 370 individual range maps or MaxEnt predictions, comparing the similarity of the spatial patterns and correlating predicted values with the observed values for WSCs. Additionally, we looked at species composition and assessed commission and omission errors associated with each method. Results MaxEnt predictions reveal greater geographic differences in richness between species rich and species poor regions than the range maps did at the five resolutions assessed. Correlations between species richness values estimated by either of the two procedures and the observed values from the WSCs increased with decreasing resolution. The slopes of the regressions between the predicted and observed values indicate that MaxEnt overpredicts observed species richness at all of the resolutions used, while range maps underpredict them, except at the finest resolution. Prediction errors did not vary significantly between methods at any resolution and tended to decrease with decreasing resolution. The accuracy of both procedures was clearly different when commission and omission errors were examined separately. Main conclusions Despite the congruent increase in the geographic richness patterns obtained from both procedures as resolution decreases, the maps created with these methods cannot be used interchangeably because of notable differences in the species compositions they report.     

Spatial resolution impacts projected plant responses to climate change on topographically complex islands

Aim

Understanding how grain size affects our ability to characterize species responses to ongoing climate change is of crucial importance in the context of an increasing awareness for the substantial difference that exists between coarse spatial resolution macroclimatic data sets and the microclimate actually experienced by organisms. Climate change impacts on biodiversity are expected to peak in mountain areas, wherein the differences between macro and microclimates are precisely the largest. Based on a newly generated fine-scale environmental data for the Canary Islands, we assessed whether data at 100 m resolution is able to provide more accurate predictions than available data at 1 km resolution. We also analysed how future climate suitability predictions of island endemic bryophytes differ depending on the grain size of grids.

Location

Canary Islands.

Time period

Present (1979–2013) and late-century (2071–2100).

Taxa

Bryophytes.

Methods

We compared the accuracy and spatial predictions using ensemble of small models for 14 Macaronesian endemic bryophyte species. We used two climate data sets: CHELSA v1.2 (~1 km) and CanaryClim v1.0 (100 m), a downscaled version of the latter utilizing data from local weather stations. CanaryClim also encompasses future climate data from five individual model intercomparison projects for three warming shared socio-economic pathways.

Results

Species distribution models generated from CHELSA and CanaryClim exhibited a similar accuracy, but CanaryClim predicted buffered warming trends in mid-elevation ridges. CanaryClim consistently returned higher proportions of newly suitable pixels (8%–28%) than CHELSA models (0%–3%). Consequently, the proportion of species predicted to occupy pixels of uncertain suitability was higher with CHELSA (3–8 species) than with CanaryClim (0–2 species).

Main conclusions

The resolution of climate data impacted the predictions rather than the performance of species distribution models. Our results highlight the crucial role that fine-resolution climate data sets can play in predicting the potential distribution of both microrefugia and new suitable range under warming climate.     

Temporal resolutions in species distribution models of highly mobile marine animals: Recommendations for ecologists and managers

While ecologists have long recognized the influence of spatial resolution on species distribution models (SDMs), they have given relatively little attention to the influence of temporal resolution. Considering temporal resolutions is critical in distribution modelling of highly mobile marine animals, as they interact with dynamic oceanographic processes that vary at time‐scales from seconds to decades. We guide ecologists in selecting temporal resolutions that best match ecological questions and ecosystems, and managers in applying these models. We group the temporal resolutions of environmental variables used in SDMs into three classes: instantaneous, contemporaneous and climatological. We posit that animal associations with fine‐scale and ephemeral features are best modelled with instantaneous covariates. Associations with large scale and persistent oceanographic features are best modelled with climatological covariates. Associations with mesoscale features are best modelled with instantaneous or contemporaneous covariates if ephemeral processes are present or interannual variability occurs, and climatological covariates if seasonal processes dominate and interannual variability is weak.     

Patterns of alien plant distribution at multiple spatial scales in a large national park: implications for ecology, management and monitoring

Aim Spatial scale is critical for understanding and managing biological invasions. In providing direction to managing alien plant invasions, much emphasis is placed on collecting spatially explicit data. However, insufficient thought is often given to how the data are to be used, frequently resulting in the incompatibility of the data for different uses. This paper explores the role of spatial scale in interpreting, managing and monitoring alien plant invasions in a large protected area. Location Kruger National Park, South Africa. Methods Using 27,000 spatially‐explicit records of invasive alien plants for the Kruger National Park (> 20,000 km²) we assessed alien plant species richness per cell at nine different scales of resolution. Results When assessing the patterns of alien plants at the various scales of resolution, almost identical results are obtained when working at scales of quarter‐degree grids and quaternary watersheds (the fourth level category in South Africa's river basin classification system). Likewise, insights gained from working at resolutions of 0.1–0.5 km and 1–5 km are similar. At a scale of 0.1 × 0.1 km cells, only 0.4% of the Kruger National Park is invaded, whereas > 90% of the park is invaded when mapped at the quarter‐degree cell resolution. Main conclusions Selecting the appropriate scale of resolution is crucial when evaluating the distribution and abundance of alien plant invasions, understanding ecological processes, and operationalizing management applications and monitoring strategies. Quarter‐degree grids and quaternary watersheds are most useful at a regional or national scale. Grid cells of 1 to 25 km² are generally useful for establishing priorities for and planning management interventions. Fine‐scale data are useful for informing management in areas which are small in extent; they also provide the detail appropriate for assessing patterns and rates of invasion.     

Species interactions weakly modify climate‐induced tree co‐occurrence patterns

Aims

Species distributions are hypothesized to be underlain by a complex association of processes that span multiple spatial scales including biotic interactions, dispersal limitation, fine‐scale resource gradients and climate. Species disequilibrium with climate may reflect the effects of non‐climatic processes on species distributions, yet distribution models have rarely directly considered non‐climatic processes. Here, we use a Joint Species Distribution Model (JSDM) to investigate the influence of non‐climatic factors on species co‐occurrence patterns and to directly quantify the relative influences of climate and alternative processes that may generate correlated responses in species distributions, such as species interactions, on tree co‐occurrence patterns.

Location

US Rocky Mountains.

Methods

We apply a Bayesian JSDM to simultaneously model the co‐occurrence patterns of ten dominant tree species across the Rocky Mountains, and evaluate climatic and residual correlations from the fitted model to determine the relative contribution of each component to observed co‐occurrence patterns. We also evaluate predictions generated from the fitted model relative to a single‐species modelling approach.

Results

For most species, correlation due to climate covariates exceeded residual correlation, indicating an overriding influence of broad‐scale climate on co‐occurrence patterns. Accounting for covariance among species did not significantly improve predictions relative to a single‐species approach, providing limited evidence for a strong independent influence of species interactions on distribution patterns.

Conclusions

Overall, our findings indicate that climate is an important driver of regional biodiversity patterns and that interactions between dominant tree species contribute little to explain species co‐occurrence patterns among Rocky Mountain trees.     

The influence of non‐climate predictors at local and landscape resolutions depends on the autecology of the species

Species distribution models have come under criticism for being too simplistic for making robust future forecasts, partly because they assume that climate is the main determinant of geographical range at large spatial extents and coarse resolutions, with non‐climate predictors being important only at finer scales. We suggest that this paradigm might be obscured by species movement patterns. To explore this we used contrasting kangaroo (family Macropodidae) case studies: two species with relatively small, stable home ranges (Macropus giganteus and M. robustus) and three species with more extensive, adaptive ranging behaviour (M. antilopinus, M. fuliginosus and M. rufus). We predicted that non‐climate predictors will be most influential to model fit and predictive performance at local spatial resolution for the former species and at landscape resolution for the latter species. We compared residuals autocovariate – boosted regression tree (RAC‐BRT) model statistics with and without species‐specific non‐climate predictors (habitat, soil, fire, water and topography), at local‐ and landscape‐level spatial resolutions (5 and 50 km). As predicted, the influence of non‐climate predictors on model fit and predictive performance (compared with climate‐only models) was greater at 50 compared with 5 km resolution for M. rufus and M. fuliginosus and the opposite trend was observed for M. giganteus. The results for M. robustus and M. antilopinus were inconclusive. Also notable was the difference in inter‐scale importance of climate predictors in the presence of non‐climate predictors. In conclusion, differences in autecology, particularly relating to space use, may contribute to the importance of non‐climate predictors at a given scale, not model scale per se. Further exploration of this concept across a range of species is encouraged and findings may contribute to more effective conservation and management of species at ecologically meaningful scales.     

Geographic range determinants of two commercially important marine molluscs

Aim We modelled the spatial abundance patterns of two abalone species (Haliotis rubra Donovan 1808 and H. laevigata Leach 1814) inhabiting inshore rocky reefs to better understand the importance of current sea surface temperature (SST) (among other predictors) and, ultimately, the effect of future climate change, on marine molluscs. Location Southern Australia. Methods We used an ensemble species distribution modelling approach that combined likelihood‐based generalized linear models and boosted regression trees. For each modelling technique, a two‐step procedure was used to predict: (1) the current probability of presence, followed by (2) current abundance conditional on presence. The resulting models were validated using an independent, spatially explicit dataset of abalone abundance patterns in Victoria. Results For both species, the presence of reef was the main driver of abalone occurrence, while SST was the main driver of spatial abundance patterns. Predictive maps at c. 1‐km resolution showed maximal abundance on shallow coastal reefs characterized by mild winter SSTs for both species. Main conclusions Sea surface temperature was a major driver of abundance patterns for both abalone species, and the resulting ensemble models were used to build fine‐resolution predictive range maps (c. 1 km) that incorporate measures of habitat suitability and quality in support of resource management. By integrating this output with structured spatial population models, a more robust understanding of the potential impacts of threatening human processes such as climate change can be established.     

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.     

The contributions of topoclimate and land cover to species distributions and abundance: fine‐resolution tests for a mountain butterfly fauna

Aim Models relating species distributions to climate or habitat are widely used to predict the effects of global change on biodiversity. Most such approaches assume that climate governs coarse‐scale species ranges, whereas habitat limits fine‐scale distributions. We tested the influence of topoclimate and land cover on butterfly distributions and abundance in a mountain range, where climate may vary as markedly at a fine scale as land cover. Location Sierra de Guadarrama (Spain, southern Europe) Methods We sampled the butterfly fauna of 180 locations (89 in 2004, 91 in 2005) in a 10,800 km² region, and derived generalized linear models (GLMs) for species occurrence and abundance based on topoclimatic (elevation and insolation) or habitat (land cover, geology and hydrology) variables sampled at 100‐m resolution using GIS. Models for each year were tested against independent data from the alternate year, using the area under the receiver operating characteristic curve (AUC) (distribution) or Spearman's rank correlation coefficient (r_s) (abundance). Results In independent model tests, 74% of occurrence models achieved AUCs of > 0.7, and 85% of abundance models were significantly related to observed abundance. Topoclimatic models outperformed models based purely on land cover in 72% of occurrence models and 66% of abundance models. Including both types of variables often explained most variation in model calibration, but did not significantly improve model cross‐validation relative to topoclimatic models. Hierarchical partitioning analysis confirmed the overriding effect of topoclimatic factors on species distributions, with the exception of several species for which the importance of land cover was confirmed. Main conclusions Topoclimatic factors may dominate fine‐resolution species distributions in mountain ranges where climate conditions vary markedly over short distances and large areas of natural habitat remain. Climate change is likely to be a key driver of species distributions in such systems and could have important effects on biodiversity. However, continued habitat protection may be vital to facilitate range shifts in response to climate change.     

Using remote sensing to model tree species distribution in Peruvian lowland Amazonia

Species distribution models for Amazonian trees have mostly been produced at scales and resolutions that are too broad and coarse for practical use in either conservation or forestry. On the other hand, several studies have shown that elevation and the medium‐resolution remote sensing data available via Landsat imagery can be successfully used to detect differences in plant species composition in Amazonia. Therefore, it seems likely that the same data can also be used to predict geographical distributions of individual taxa. Here we use remotely sensed data and a maximum entropy algorithm (MaxEnt) to generate landscape‐scale distribution models at 30‐m‐resolution for five economically important timber tree genera (Apuleia, Amburana, Crepidospermum, Dipteryx, and Manilkara). Individual Landsat Thematic Mapper bands and normalized difference vegetation index yielded acceptable model performance, and the use of averaging filters (3 × 3 and 5 × 5 pixel low‐pass filters) improved model performance further. Including elevation as a predictor also improved model performance for all the genera. Our results suggest that it is possible to use Landsat bands and elevation as predictors for modeling the potential distribution of tree species in lowland Amazonia at a fine enough resolution to facilitate the practical management of forest resources.     

Upscaling leaf area index in an Arctic landscape through multiscale observations

Monitoring and understanding global change requires a detailed focus on upscaling, the process for extrapolating from the site‐specific scale to the smallest scale resolved in regional or global models or earth observing systems. Leaf area index (LAI) is one of the most sensitive determinants of plant production and can vary by an order of magnitude over short distances. The landscape distribution of LAI is generally determined by remote sensing of surface reflectance (e.g. normalized difference vegetation index, NDVI) but the mismatch in scales between ground and satellite measurements complicates LAI upscaling. Here, we describe a series of measurements to quantify the spatial distribution of LAI in a sub‐Arctic landscape and then describe the upscaling process and its associated errors. Working from a fine‐scale harvest LAI–NDVI relationship, we collected NDVI data over a 500 m × 500 m catchment in the Swedish Arctic, at resolutions from 0.2 to 9.0 m in a nested sampling design. NDVI scaled linearly, so that NDVI at any scale was a simple average of multiple NDVI measurements taken at finer scales. The LAI–NDVI relationship was scale invariant from 1.5 to 9.0 m resolution. Thus, a single exponential LAI–NDVI relationship was valid at all these scales, with similar prediction errors. Vegetation patches were of a scale of ～0.5 m and at measurement scales coarser than this, there was a sharp drop in LAI variance. Landsat NDVI data for the study catchment correlated significantly, but poorly, with ground‐based measurements. A variety of techniques were used to construct LAI maps, including interpolation by inverse distance weighting, ordinary Kriging, External Drift Kriging using Landsat data, and direct estimation from a Landsat NDVI–LAI calibration. All methods produced similar LAI estimates and overall errors. However, Kriging approaches also generated maps of LAI estimation error based on semivariograms. The spatial variability of this Arctic landscape was such that local measurements assimilated by Kriging approaches had a limited spatial influence. Over scales >50 m, interpolation error was of similar magnitude to the error in the Landsat NDVI calibration. The characterisation of LAI spatial error in this study is a key step towards developing spatio‐temporal data assimilation systems for assessing C cycling in terrestrial ecosystems by combining models with field and remotely sensed data.     

Predicting distributions,habitat preferences and associated conservation implications for a genus of rare fishes,seahorses (Hippocampus spp.)

Aim

To identify useful sources of species data and appropriate habitat variables for species distribution modelling on rare species, with seahorses as an example, deriving ecological knowledge and spatially explicit maps to advance global seahorse conservation.

Location

The shallow seas.

Methods

We applied a typical species distribution model (SDM), maximum entropy, to examine the utility of (1) two versions of habitat variables (habitat occurrences vs. proximity to habitats) and (2) three sources of species data: quality research‐grade (RG) data, quality‐unknown citizen science (CS) and museum‐collection (MC) data. We used the best combinations of species data and habitat variables to predict distributions and estimate species–habitat relations and threatened status for seahorse species.

Results

We demonstrated that using “proximity to habitats” and integrating all species datasets (RG, CS and MC) derived models with the highest accuracies among all dataset variations. Based on this finding, we derived reliable models for 33 species. Our models suggested that only 0.4% of potential seahorse range was suitable to more than three species together; seahorse biogeographic epicentres were mainly in the Philippines; and proximity to sponges was an important habitat variable. We found that 12 “Data Deficient” species might be threatened based on our predictions according to IUCN criteria.

Main conclusions

We highlight that using proper habitat variables (e.g., proximity to habitats) is critical to determine distributions and key habitats for low‐mobility animals; collating and integrating quality‐unknown occurrences (e.g., CS and MC) with quality research data are meaningful for building SDMs for rare species. We encourage the application of SDMs to estimate area of occupancy for rare organisms to facilitate their conservation status assessment.