Using a species distribution model to guide NSW surveys of the long‐footed potoroo (Potorous longipes)

Knowledge of threatened species’ distributions is essential for effective conservation decision‐making. Species distribution models (SDMs) are widely used to map species’ geographic ranges, identify new areas of suitable habitat and guide field surveys. In New South Wales (NSW), Australia, there are grave doubts about whether populations of the critically endangered long‐footed potoroo (Potorous longipes) remain extant, and identification of occupied sites is a high priority for its conservation. We used an SDM (Maxent) to identify regions in NSW that may have suitable habitat for the potoroo. The SDM was built with seven climate layers and had strong predictive performance (cross‐validated AUC = 0.94). We then combined this information on habitat suitability with vegetation and topography, to identify 58 survey sites across NSW. From April 2016 to May 2017, we undertook six field trips deploying six to eight cameras at each site for 52–63 days, resulting in 25 120 camera trap nights. A total of 215 759 images captured 43 native and feral animal species, but no long‐footed potoroos. Following the survey, newly available, independent presence and absence data were used to validate our model. A Kruskal–Wallis H test indicated that habitat suitability values were significantly higher at presence locations than absence locations (H = 58.66, d.f. = 1, P < 0.001). Finally, we refitted the Maxent model with the new data and identified additional regions that future surveys could explore. We conclude, however, that if the long‐footed potoroo remains extant in NSW, it is extremely rare.     

Testing the utility of species distribution modelling using Random Forests for a species in decline

Habitat suitability estimates derived from species distribution models (SDMs) are increasingly used to guide management of threatened species. Poorly estimating species’ ranges can lead to underestimation of threatened status, undervaluing of remaining habitat and misdirection of conservation funding. We aimed to evaluate the utility of a SDM, similar to the models used to inform government regulation of habitat in our study region, in estimating the contemporary distribution of a threatened and declining species. We developed a presence‐only SDM for the endangered New Holland Mouse (Pseudomys novaehollandiae) across Victoria, Australia. We conducted extensive camera trap surveys across model‐predicted and expert‐selected areas to generate an independent data set for use in evaluating the model, determining confidence in absence data from non‐detection sites with occupancy and detectability modelling. We assessed the predictive capacity of the model at thresholds based on (1) sum of sensitivity and specificity (SSS), and (2) the lowest presence threshold (LPT; i.e. the lowest non‐zero model‐predicted habitat suitability value at which we detected the species). We detected P. novaehollandiae at 40 of 472 surveyed sites, with strong support for the species’ probable absence from non‐detection sites. Based on our post hoc optimised SSS threshold of the SDM, 25% of our detection sites were falsely predicted as non‐suitable habitat and 75% of sites predicted as suitable habitat did not contain the species at the time of our survey. One occupied site had a model‐predicted suitability value of zero, and at the LPT, 88% of sites predicted as suitable habitat did not contain the species at the time of our survey. Our findings demonstrate that application of generic SDMs in both regulatory and investment contexts should be tempered by considering their limitations and currency. Further, we recommend engaging species experts in the extrapolation and application of SDM outputs.     

Development of an in-stream migration model for Gammarus pulex L. (Crustacea, Amphipoda) as a tool in river restoration management

Ecological models can act as interesting tools to support decision-making in river restoration management. In particular models which are capable of predicting the habitat requirements of species are of considerable importance to ensure that the planned actions have the desired effects on the aquatic ecosystem. To this end, Artificial Neural Network (ANN) models were tested and optimized for the prediction of the habitat suitability for Gammarus pulex, a relevant indicator species in water quality assessment. Although ANN models are in general quite robust with a rather high predictive reliability, the model performance had to be increased with regard to simulations for river restoration management. In particular, it has been shown that spatial and temporal expert-rules could possibly be included. Migration dynamics of downstream drift and upstream migration of the organisms and migration barriers along the river (weirs, culverted river sections,␣...) might indeed deliver important additional information on the effectiveness of the restoration plans, and also on the timing of the expected effects. In this context, an additional in-stream migration model for Gammarus pulex was developed. This migration model, implemented in a Geographical Information System (GIS), has been used to simulate a practical river restoration scenario for a river in Flanders, Belgium. The case study illustrated that the removal of a weir, at a particular site, resulted in the improvement of the habitat suitability for Gammarus pulex. The ANN models predicted that after restoration the habitat was suitable again for Gammarus pulex. The migration model indicated that the restored parts of the river would be recolonized within about 2 months. In this way, decision makers can have an idea whether and when a restoration option will have a desired effect.     

Evaluation of habitat suitability index models by global sensitivity and uncertainty analyses: a case study for submerged aquatic vegetation

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low‐quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision‐making framework will result in better‐informed, more robust decisions.     

Developing landscape habitat models for rare amphibians with small geographic ranges: a case study of Siskiyou Mountains salamanders in the western USA

To advance the development of conservation planning for rare species with small geographic ranges, we determined habitat associations of Siskiyou Mountains salamanders (Plethodon stormi) and developed habitat suitability models at fine (10 ha), medium (40 ha), and broad (202 ha) spatial scales using available Geographic Information Systems data and logistic regression analysis with an information theoretic approach. Across spatial scales, there was very little support for models with structural habitat features, such as tree canopy cover and conifer diameter. Model-averaged 95% confidence intervals for regression coefficients and associated odds ratios indicated that the occurrence of Siskiyou Mountains salamanders was positively associated with rocky soils and Pacific madrone (Abutus menziesii) and negatively associated with elevation and white fir (Abies concolor); these associations were consistent across 3 spatial scales. The occurrence of this species also was positively associated with hardwood density at the medium spatial scale. Odds ratios projected that a 10% decrease in white fir abundance would increase the odds of salamander occurrence 3.02–4.47 times, depending on spatial scale. We selected the model with rocky soils, white fir, and Oregon white oak (Quercus garryana) as the best model across 3 spatial scales and created habitat suitability maps for Siskiyou Mountains salamanders by projecting habitat suitability scores across the landscape. Our habitat suitability models and maps are applicable to selection of priority conservation areas for Siskiyou Mountains salamanders, and our approach can be easily adapted to conservation of other rare species in any geographical location.     

Evaluation of Bayesian networks for modelling habitat suitability and management of a protected area

To be effective, management of protected areas should be based on the best available evidence, including the scientific literature and expert knowledge. However, lack of such evidence in a suitable form to support decision-making may hinder effective management. Here we examine the use of Bayesian networks to support the management of protected areas, through the development of habitat suitability models for eight species of conservation concern. Bayesian networks were constructed on the basis of the scientific literature and expert knowledge, and were then tested using results from a field survey. Models of all species demonstrated very high discrimination between presence and absence sites, as indicated by AUC values >0.8, with values >0.9 obtained for four species, and Kappa values in the range of 0.4–0.9. The Bayesian networks were then used to examine the impact of different management interventions on habitat suitability of each species, including tree cutting, grazing and burning. Species differed in terms of their sensitivity to different management interventions, and model output provided evidence of both negative and positive interactions between types of intervention. These results highlight the trade-offs that must often be made when undertaking conservation management, and demonstrate the value of Bayesian networks in helping to make such trade-offs explicit. The identification of management impacts through analysis of available evidence also demonstrates the value of Bayesian networks for supporting evidence-based approaches to protected area management.     

The influences of habitat, landscape structure and climate on local distribution patterns of the nuthatch (Sitta europaea L.)

The nuthatch, Sitta europaea L., is a small (23 g), cavity-nesting woodland bird which, since the 1970s, has been expanding its range in Britain. However, within this range, the species is notably scarce in an area of eastern England. This gap in the species distribution could arise for several reasons including habitat quality, local landscape structure, regional landscape structure and climate. Field surveys and logistic models of breeding nuthatch presence/absence were used to investigate the relative influences of habitat quality, landscape structure and climate on the prevalence of nuthatches in eastern England. Field surveys of woods in the study area indicated that habitat quality was sufficient to support a nuthatch population. A model of habitat occupancy in relation to local landscape structure, developed in the Netherlands, was applied to the study area. The number of breeding pairs predicted for the study area by the model was lower than expected from habitat area alone, suggesting an additional effect of isolation. However, observed numbers were even lower than those predicted by the model. To evaluate the possible roles of climate and large-scale landscape structure on distribution, presence/absence data of breeding nuthatches at the 10-km grid square scale were related to variables describing climate and the amount and dispersion of broadleaved woodland. While climate in the study area appeared suitable, models including landscape variables suggested that the study area as a whole was unlikely to support nuthatches. Although suitable habitat was available, woodland in the study area appeared to be too isolated from surrounding nuthatch populations for colonisation to be successful. This situation may change if current increases in both national and regional populations continue, thus increasing the number of potential colonists reaching the study area. Received: 3 November 1997 / 22 January 1998     

Predicting future invasion of an invasive alien tree in a Japanese oceanic island by process-based statistical models using recent distribution maps

Modelling and predicting the potential habitat and future range expansion of invasive species can help managers to mitigate the impact of such species. Because habitat suitability and the colonization process are key determinants of range expansion, inferences drawn from invasion patterns should be based on both attributes. To predict the potential habitat and expansion rate of the invasive tree Bischofia javanica on Hahajima Island, we used simultaneous models of habitat and dispersal to estimate the effect of environment and dispersal from the source population on the current distribution. We compared the fit and the estimated magnitudes of the environment and dispersal effects in the simultaneous models with those in habitat suitability and colonization kernel models. The values of Akaike’s information criterion for the simultaneous models were better than those of the habitat suitability and colonization kernel models, indicating that the current distribution of Bischofia was determined by both environment and dispersal. The simultaneous models predicted that the potential habitat of Bischofia would be larger than that predicted by the habitat suitability model. The potential habitat distribution and future invasion predicted by the simultaneous models will contribute to the development of specific landscape-scale management plans to control this invasive species.     

Fauna habitat modelling and mapping: A review and case study in the Lower Hunter Central Coast region of NSW

Abstract Habitat models are now broadly used in conservation planning on public lands. If implemented correctly, habitat modelling is a transparent and repeatable technique for describing and mapping biodiversity values, and its application in peri‐urban and agricultural landscape planning is likely to expand rapidly. Conservation planning in such landscapes must be robust to the scrutiny that arises when biodiversity constraints are placed on developers and private landholders. A standardized modelling and model evaluation method based on widely accepted techniques will improve the robustness of conservation plans. We review current habitat modelling and model evaluation methods and provide a habitat modelling case study in the New South Wales central coast region that we hope will serve as a methodological template for conservation planners. We make recommendations on modelling methods that are appropriate when presence‐absence and presence‐only survey data are available and provide methodological details and a website with data and training material for modellers. Our aim is to provide practical guidelines that preserve methodological rigour and result in defendable habitat models and maps. The case study was undertaken in a rapidly developing area with substantial biodiversity values under urbanization pressure. Habitat maps for seven priority fauna species were developed using logistic regression models of species‐habitat relationships and a bootstrapping methodology was used to evaluate model predictions. The modelled species were the koala, tiger quoll, squirrel glider, yellow‐bellied glider, masked owl, powerful owl and sooty owl. Models ranked sites adequately in terms of habitat suitability and provided predictions of sufficient reliability for the purpose of identifying preliminary conservation priority areas. However, they are subject to multiple uncertainties and should not be viewed as a completely accurate representation of the distribution of species habitat. We recommend the use of model prediction in an adaptive framework whereby models are iteratively updated and refined as new data become available.     

The Use of Habitat Models in Conservation of Rare and Endangered Leafhopper Species (Hemiptera,Auchenorrhyncha)

For conservation of Auchenorrhyncha species, knowledge of their habitat requirements is essential. However, for most species there is no ‘quantitative’ knowledge that would allow e.g. spatially explicit predictions. Such predictions can be made by habitat models, which quantify the relationship between the environment and the occurrence of species. In two plot-based case studies – the endangered leafhopper Verdanus bensoni in mountainous grasslands and four endangered Auchenorrhyncha in urban brownfields – we used habitat models to quantify the habitat requirements of these five species and to exemplify their use for creating habitat suitability maps. In the first case study, the multivariate model showed that occurrence probabilities of the leafhopper V. bensoni increase with both decreasing nitrogen indicator values and decreasing tree cover. On urban brownfields, successional age was a driving factor for species’ occurrence. Site age largely determines a range of vegetation characteristics, which, in multivariate models, often replaced the variable age. Internal validation showed the robustness of all models. The models allow predictions of habitat quality under different management regimes (e.g. response to fertilization or abandonment for V. bensoni or to different turnover rates on brownfield sites). We discuss the application of habitat models in the conservation of Auchenorrhyncha, especially the use of habitat suitability maps.     

Use of Restored Riparian Habitat by the Endangered Least Bell's Vireo (Vireo bellii pusillus)

A primary objective of riparian restoration in California is the creation of habitat for endangered species. Four restoration sites in San Diego County were monitored between 1989 and 1993 and evaluated for their suitability as nesting habitat for Vireo bellii pusillus (Least Bell's Vireo), a state and federally endangered obligate riparian breeder. Vegetation structure at each site was quantified annually and compared to a model of canopy architecture derived from Least Bell's Vireo territories in natural habitat. Vireo use of restored habitat was documented through systematic surveys and nest monitoring. By 1993, only one site in its entirety met the habitat suitability criteria of the model, but portions of each site during all years did so. Differences between sites in the time required to develop suitable habitat—well-developed layered vegetation from the ground to under 8m in height)—were attributable largely to variation in annual rainfall. Vireos visited restoration sites to forage as early as the first growing season, but they did not establish territories or nest there until at least part of the site supported suitable habitat as determined from the model. Placement of territories and nests coincided with patches of dense vegetation characteristic of natural nesting areas. Occupation of restored sites was accelerated by the presence of adjacent mature riparian habitat, which afforded birds nest sites and/or foraging habitat lacking in the planted vegetation. Vireos nesting in restored habitat achieved success comparable to that of vireos nesting in surrounding natural habitat, and there was no evidence that productivity was reduced in created areas. These findings indicate that creating nesting habitat for this target species is feasible and suggest that the critical components of vireo nesting habitat have been captured in both the design and quantitative assessment of restoration sites.     

Distribution of medium- to large-sized African mammals based on habitat suitability models

The knowledge of the areas inhabited by a species within its distribution range and the connections among patches are critical pieces of information for successful conservation actions. The internal structure of the extent of occurrence (EO) of a species is almost always unknown, even for “well-known” flagship species. We developed a methodology to infer the area of occupancy (AO) within the EO of a species using the limited available data. We present here the results of a three years project funded by European Union to develop high-resolution models of habitat suitability for 281 medium- to large-sized African mammals across the whole continent. The existing literature was reviewed and all data on the geographic distribution and environmental preferences of the selected species were collected. For each species, these data were then expressed in terms of key variables available as GIS layers at a resolution of 1 km² over the entire African continent. The AO of each species was obtained merging the information on the ecological needs of the species and the values of ecological variables over the region identified as EO. The habitat suitability models were evaluated through direct field work in four countries (Morocco, Cameroon, Uganda, Botswana) chosen as representatives of the environmental and species diversity of Africa. More than 81% of models had positive true skill statistics (TSS) values, indicating models performing better than random. Rigorous modeling procedures supported by ad-hoc field evaluation allowed the production of high-resolution habitat suitability models useful for conservation applications.     

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

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

Evaluation of Habitat Suitability Models for Forest Passerines Using Demographic Data

ABSTRACT Habitat suitability is often used as a surrogate for demographic responses (i.e., abundance, survival, fecundity, or population viability) in the application of habitat suitability index (HSI) models. Whether habitat suitability actually relates to demographics, however, has rarely been evaluated. We validated HSI models of breeding habitat suitability for wood thrush (Hylocichla mustelina) and yellow-breasted chat (Icteria virens) in Missouri, USA. First, we evaluated HSI models as a predictor of 3 demographic responses: within-site territory density, site-level territory density, and nest success. We demonstrated a link between HSI values and all 3 types of demographic responses for the yellow-breasted chat and site-level territory density for the wood thrush. Second, we evaluated support for models containing HSI values, models containing measured habitat features (e.g., tree age, tree species, ecological land type), and models containing management treatments (e.g., even-aged and uneven-aged forest regeneration treatments) for each demographic response using model selection. Models containing HSI values received more support, in general, than models containing only habitat features or management treatments for all 3 types of wildlife response. The assumption that changes in habitat suitability represent wildlife demographic response to vegetation change is supported by our models. However, differences in species ecology may contribute to the degree to which HSI values are related to specific demographic responses. We recommend validation of HSI models with the particular demographic data of interest (i.e., density, productivity) to increase confidence in the model used for conservation planning.     

Small-scale habitat use of black grouse (Tetrao tetrix L.) and rock ptarmigan (Lagopus muta helvetica Thienemann) in the Austrian Alps

We investigated the small-scale habitat use of two grouse species, black grouse (Tetrao tetrix L.) and rock ptarmigan (Lagopus muta helvetica Thienemann) in a study area in the Austrian Central Alps in summer. To build habitat suitability models, we applied multiple logistic regression using presence–absence data from fieldwork as the response variable and a set of habitat characteristics as explanatory variables, respectively. To gain a better understanding of the mechanisms that drive habitat selection, we tested for two-way interaction terms before excluding any variables from the initial variable set. Four explanatory variables significantly contributed to the black grouse model: dwarf shrub cover, dwarf shrub height, patchiness and ant hills. The final model for rock ptarmigan contained three explanatory variables: dwarf shrub cover, rock cover and dwarf shrub height. Most notably, the interaction terms dwarf shrub cover × patchiness in the black grouse model and dwarf shrub cover × dwarf shrub height, rock cover × dwarf shrub height in the rock ptarmigan model point out trade-off mechanisms between food, cover and overview providing features. Thus, our models do not only identify the parameters that mainly drive habitat selection, but also deepen our understanding about the causal relationships between these factors. Therefore, the information gained in this study allows for a deduction of appropriate habitat management strategies and supports conservation efforts of local stakeholders.     

Implications of Climate Change for Bird Conservation in the Southwestern U.S. under Three Alternative Futures

Future expected changes in climate and human activity threaten many riparian habitats, particularly in the southwestern U.S. Using Maximum Entropy (MaxEnt3.3.3) modeling, we characterized habitat relationships and generated spatial predictions of habitat suitability for the Lucy’s warbler (Oreothlypis luciae), the Southwestern willow flycatcher (Empidonax traillii extimus) and the Western yellow-billed cuckoo (Coccyzus americanus). Our goal was to provide site- and species-specific information that can be used by managers to identify areas for habitat conservation and/or restoration along the Rio Grande in New Mexico. We created models of suitable habitat for each species based on collection and survey samples and climate, biophysical, and vegetation data. We projected habitat suitability under future climates by applying these models to conditions generated from three climate models for 2030, 2060 and 2090. By comparing current and future distributions, we identified how habitats are likely to change as a result of changing climate and the consequences of those changes for these bird species. We also examined whether land ownership of high value sites shifts under changing climate conditions. Habitat suitability models performed well. Biophysical characteristics were more important that climate conditions for predicting habitat suitability with distance to water being the single most important predictor. Climate, though less important, was still influential and led to declines of suitable habitat of more than 60% by 2090. For all species, suitable habitat tended to shrink over time within the study area leaving a few core areas of high importance. Overall, climate changes will increase habitat fragmentation and reduce breeding habitat patch size. The best strategy for conserving bird species within the Rio Grande will include measures to maintain and restore critical habitat refugia. This study provides an example of a presence-only habitat model that can be used to inform the management of species at intermediate scales.     

Nest site selection of a primary hole‐nesting passerine reveals means to developing sustainable forestry

Anthropogenic habitat loss and fragmentation affect populations worldwide. For example, many bird populations of boreal forests have declined due to intensive forestry. To target conservation actions for such species, determining the key factors that affect their habitat selection is essential. Remote sensing methods provide highly potential means to measure habitat variables over large areas. We aim at identifying the key‐features of habitats by utilizing remote sensing data. As a case example, we study the nest site selection of a primary hole‐nesting passerine, the willow tit Poecile montanus, in a managed forest landscape. Using presence–absence data, we determine the most important habitat characteristics of the nest sites for three spatial scales by generalized linear mixed effect models. Our results highlight the importance of the availability of nesting sites – standing decaying deciduous trees – in the nest site selection of P. montanus. It seems to prefer moist habitats with high densities of deciduous trees and to avoid open areas, but does not require mature or intact habitats. Most of the nest site selection seems to occur within small scales. In this case, remote sensing data alone was insufficient for producing reliable models, but adding information of an ecologically important feature from direct field surveys greatly improved model performances. For the conservation and maintenance of dead wood dependent species, changes in forestry practices are necessary to keep the key characteristics of the habitat. Most importantly, continuous availability of standing decaying wood should be secured.     

A Spatial Modeling Approach to Identify Potential Butternut Restoration Sites in Mammoth Cave National Park

Incorporation of disease resistance is nearly complete for several important North American hardwood species threatened by exotic fungal diseases. The next important step toward species restoration would be to develop reliable tools to delineate ideal restoration sites on a landscape scale. We integrated spatial modeling and remote sensing techniques to delineate potential restoration sites for Butternut (Juglans cinerea L.) trees, a hardwood species being decimated by an exotic fungus, in Mammoth Cave National Park (MCNP), Kentucky. We first developed a multivariate habitat model to determine optimum Butternut habitats within MCNP. Habitat characteristics of 54 known Butternut locations were used in combination with eight topographic and land use data layers to calculate an index of habitat suitability based on Mahalanobis distance (D²). We used a bootstrapping technique to test the reliability of model predictions. Based on a threshold value for the D² statistic, 75.9% of the Butternut locations were correctly classified, indicating that the habitat model performed well. Because Butternut seedlings require extensive amounts of sunlight to become established, we used canopy cover data to refine our delineation of favorable areas for Butternut restoration. Areas with the most favorable conditions to establish Butternut seedlings were limited to 291.6 ha. Our study provides a useful reference on the amount and location of favorable Butternut habitat in MCNP and can be used to identify priority areas for future Butternut restoration. Given the availability of relevant habitat layers and accurate location records, our approach can be applied to other tree species and areas.     

Identifying Core Areas in a Species’ Range Using Temporal Suitability Analysis: an Example Using Little Bustards <Emphasis Type="Italic">Tetrax Tetrax</Emphasis> L. in Spain

Variations in habitat quality impact on breeding success, leading to strong selection pressure for the best sites to be occupied first during a population increase and last during a decline. Coupled with dispersal and metapopulation processes, the result is that snapshot surveys of wildlife distributions may fail to reveal core areas that conservation seeks to protect. At a local scale, territory occupancy is a good indicator of quality but data are not readily available to assess occupancy for rarer species, in remote areas, and over large spatial extents. We introduce temporal suitability analysis as a way to generate an analogue of occupancy from a single survey and illustrate it using data on the little bustard in Spain. We first used Generalised Additive Modelling (GAM) to build a predictive distribution model using Geographic Information System (GIS) coverages and satellite imagery, and then applied the model retrospectively to a time series of satellite images to produce one distribution map for each year. These annual maps differed in the extent of Spain predicted as suitable for little bustards. By overlaying the maps, we identified areas predicted as suitable in one to n years. We show that this temporal suitability map correlates with a conventional habitat suitability map based on a single year but contains extra information on hierarchical use of habitats and the lag between suitability and use. The technique may be applied at a variety of spatial scales to reveal changes in expected occupancy as land use or external factors determining land cover types vary over time.