Exploring the spatiotemporal dynamics of habitat suitability to improve conservation management of a vulnerable plant species

Ongoing declines in biodiversity caused by global environmental changes call for adaptive conservation management, including the assessment of habitat suitability spatiotemporal dynamics potentially affecting species persistence. Remote sensing (RS) provides a wide-range of satellite-based environmental variables that can be fed into species distribution models (SDMs) to investigate species-environment relations and forecast responses to change. We address the spatiotemporal dynamics of species’ habitat suitability at the landscape level by combining multi-temporal RS data with SDMs for analysing inter-annual habitat suitability dynamics. We implemented this framework with a vulnerable plant species (Veronica micrantha), by combining SDMs with a time-series of RS-based metrics of vegetation functioning related to primary productivity, seasonality, phenology and actual evapotranspiration. Besides RS variables, predictors related to landscape structure, soils and wildfires were ranked and combined through multi-model inference (MMI). To assess recent dynamics, a habitat suitability time-series was generated through model hindcasting. MMI highlighted the strong predictive ability of RS variables related to primary productivity and water availability for explaining the test-species distribution, along with soil, wildfire regime and landscape composition. The habitat suitability time-series revealed the effects of short-term land cover changes and inter-annual variability in climatic conditions. Multi-temporal SDMs further improved predictions, benefiting from RS time-series. Overall, results emphasize the integration of landscape attributes related to function, composition and spatial configuration for improving the explanation of ecological patterns. Moreover, coupling SDMs with RS functional metrics may provide early-warnings of future environmental changes potentially impacting habitat suitability. Applications discussed include the improvement of biodiversity monitoring and conservation strategies.     

An integrated method to create habitat suitability models for fragmented landscapes

Given the pervasive influence of human induced habitat fragmentation in ecological processes, landscape models are a welcome advance. The development of GIS software has allowed a greater use of these models and of analyses of the relationship between species and habitat variables. Habitat suitability models are thus theoretical concepts that can be used for planning in fragmented landscapes and habitat conservation. The most commonly used models are based on single species and on the assignment of suitability values for some environmental variables. Generally the cartographic basis for modeling suitability are thematic maps produced by a Boolean logic. In this paper we propose a model based on a set of focal species and on maps produced by a fuzzy classification method. Focal species, selected by an expert-based approach, provide a practical way of extending the scope of habitat suitability models to the conservation of biodiversity at landscape scale. The utilisation of a classification method that applies a continuity criterion may allow more consideration of the connectivity of an area because it allows a better detection of ecological gradients within a landscape. We applied this methodology to the Tuscany region focusing on terrestrial mammals. Performing a fuzzy classification we produced five land cover maps and through image processing operations we obtained a suitability model which applies a continuity criterion. The resulting suitability fuzzy model seems better for the study of connectivity and fragmentation, especially in areas with high spatial complexity.     

Integrating Multiple Distribution Models to Guide Conservation Efforts of an Endangered Toad

Species distribution models are used for numerous purposes such as predicting changes in species’ ranges and identifying biodiversity hotspots. Although implications of distribution models for conservation are often implicit, few studies use these tools explicitly to inform conservation efforts. Herein, we illustrate how multiple distribution models developed using distinct sets of environmental variables can be integrated to aid in identification sites for use in conservation. We focus on the endangered arroyo toad (Anaxyrus californicus), which relies on open, sandy streams and surrounding floodplains in southern California, USA, and northern Baja California, Mexico. Declines of the species are largely attributed to habitat degradation associated with vegetation encroachment, invasive predators, and altered hydrologic regimes. We had three main goals: 1) develop a model of potential habitat for arroyo toads, based on long-term environmental variables and all available locality data; 2) develop a model of the species’ current habitat by incorporating recent remotely-sensed variables and only using recent locality data; and 3) integrate results of both models to identify sites that may be employed in conservation efforts. We used a machine learning technique, Random Forests, to develop the models, focused on riparian zones in southern California. We identified 14.37% and 10.50% of our study area as potential and current habitat for the arroyo toad, respectively. Generally, inclusion of remotely-sensed variables reduced modeled suitability of sites, thus many areas modeled as potential habitat were not modeled as current habitat. We propose such sites could be made suitable for arroyo toads through active management, increasing current habitat by up to 67.02%. Our general approach can be employed to guide conservation efforts of virtually any species with sufficient data necessary to develop appropriate distribution models.     

Habitat suitability modelling for species at risk is sensitive to algorithm and scale: A case study of Blanding's turtle,Emydoidea blandingii,in Ontario,Canada

Species distribution modelling (SDM) can help conservation by providing information on the ecological requirements of species at risk. We developed habitat suitability models at multiple spatial scales for a threatened freshwater turtle, Emydoidea blandingii, in Ontario as a case study. We also explored the effect of background data selection and modelling algorithm selection on habitat suitability predictions. We used sighting records, high-resolution land cover data (25 m), and two SDM techniques: boosted regression trees; and maximum entropy modelling. The area under the receiver characteristic operating curve (AUC) for habitat suitability models tested on independent data ranged from 0.878 to 0.912 when using random background and from 0.727 to 0.741 with target-group background. E. blandingii habitat suitability was best predicted by air temperature, wetland area, open water area, road density, and cropland area. Habitat suitability increased with increasing air temperature and wetland area, and decreased with increasing cropland area. Low road density and open water increased habitat suitability, while high levels of either variable decreased habitat suitability. Robust habitat suitability maps for species at risk require using a multi-scale and multi-algorithm approach. If well used, SDM can offer insight on the habitat requirements of species at risk and help guide the development of management plans. Our results suggest that E. blandingii management plans should promote the protection of terrestrial habitat surrounding residential wetlands, halt the building of roads within and adjacent to currently occupied habitat, and identify movement corridors for isolated populations.     

Ensemble modeling to predict habitat suitability for a large‐scale disturbance specialist

To conserve habitat for disturbance specialist species, ecologists must identify where individuals will likely settle in newly disturbed areas. Habitat suitability models can predict which sites at new disturbances will most likely attract specialists. Without validation data from newly disturbed areas, however, the best approach for maximizing predictive accuracy can be unclear (Northwestern U.S.A.). We predicted habitat suitability for nesting Black‐backed Woodpeckers (Picoides arcticus; a burned‐forest specialist) at 20 recently (≤6 years postwildfire) burned locations in Montana using models calibrated with data from three locations in Washington, Oregon, and Idaho. We developed 8 models using three techniques (weighted logistic regression, Maxent, and Mahalanobis D² models) and various combinations of four environmental variables describing burn severity, the north–south orientation of topographic slope, and prefire canopy cover. After translating model predictions into binary classifications (0 = low suitability to unsuitable, 1 = high to moderate suitability), we compiled “ensemble predictions,” consisting of the number of models (0–8) predicting any given site as highly suitable. The suitability status for 40% of the area burned by eastside Montana wildfires was consistent across models and therefore robust to uncertainty in the relative accuracy of particular models and in alternative ecological hypotheses they described. Ensemble predictions exhibited two desirable properties: (1) a positive relationship with apparent rates of nest occurrence at calibration locations and (2) declining model agreement outside surveyed environments consistent with our reduced confidence in novel (i.e., “no‐analogue”) environments. Areas of disagreement among models suggested where future surveys could help validate and refine models for an improved understanding of Black‐backed Woodpecker nesting habitat relationships. Ensemble predictions presented here can help guide managers attempting to balance salvage logging with habitat conservation in burned‐forest landscapes where black‐backed woodpecker nest location data are not immediately available. Ensemble modeling represents a promising tool for guiding conservation of large‐scale disturbance specialists.     

Driven to the edge: Species distribution modeling of a Clawed Salamander (Hynobiidae: Onychodactylus koreanus) predicts range shifts and drastic decrease of suitable habitats in response to climate change

Climate change is one of the major threats to global amphibian diversity, and consequently, the species distribution is expected to shift considerably in the future. Therefore, predicting such shifts is important to guide conservation and management plans. Here, we used eight independent environmental variables and four representative concentration pathways (RCPs) to model the current and future habitat suitability of the Korean clawed salamander (Onychodactylus koreanus) and then defined the dispersal limits of the species using cost distance analysis. The current habitat suitability model generated using the maximum entropy algorithm was highly consistent with the known distribution of the species and had good predictive performance. Projections onto years 2050 and 2070 predicted a drastic decrease of habitat suitability across all RCPs, with up to 90.1% decrease of suitable area and 98.0% decrease of optimal area predicted from binary presence grids. The models also predicted a northeastward shift of habitat suitability toward high‐elevation areas and a persistence of suitability along the central ridge of the Baekdudaegan Range. This area is likely to become a climatic refugium for the species in the future, and it should be considered as an area of conservation priority. Therefore, we urge further ecological studies and population monitoring to be conducted across the range of O. koreanus. The vulnerability to rapid climate change is also shared by other congeneric species, and assessing the impacts of climate change on these other species is needed to better conserve this unique lineage of salamanders.     

Assessing congruence of opportunistic records and systematic surveys for predicting Hispaniolan mammal species distributions

Comparative assessment of the relative information content of different independent spatial data types is necessary to evaluate whether they provide congruent biogeographic signals for predicting species ranges. Opportunistic occurrence records and systematically collected survey data are available from the Dominican Republic for Hispaniola’s surviving endemic non‐volant mammals, the Hispaniolan solenodon (Solenodon paradoxus) and Hispaniolan hutia (Plagiodontia aedium); opportunistic records (archaeological, historical and recent) exist from across the entire country, and systematic survey data have been collected from seven protected areas. Species distribution models were developed in maxent for solenodons and hutias using both data types, with species habitat suitability and potential country‐level distribution predicted using seven biotic and abiotic environmental variables. Three different models were produced and compared for each species: (a) opportunistic model, with starting model incorporating abiotic‐only predictors; (b) total survey model, with starting model incorporating biotic and abiotic predictors; and (c) reduced survey model, with starting model incorporating abiotic‐only predictors to allow further comparison with the opportunistic model. All models predict suitable environmental conditions for both solenodons and hutias across a broadly congruent, relatively large area of the Dominican Republic, providing a spatial baseline of conservation‐priority landscapes that might support native mammals. Correlation between total and reduced survey models is high for both species, indicating the substantial explanatory power of abiotic variables for predicting Hispaniolan mammal distributions. However, correlation between survey models and opportunistic models is only moderately positive. Species distribution models derived from different data types can provide different predictions about habitat suitability and conservation‐priority landscapes for threatened species, likely reflecting incompleteness and bias in spatial sampling associated with both data types. Models derived using both opportunistic and systematic data must therefore be applied critically and cautiously.     

Toward community predictions: Multi‐scale modelling of mountain breeding birds' habitat suitability,landscape preferences,and environmental drivers

Across a large mountain area of the western Swiss Alps, we used occurrence data (presence‐only points) of bird species to find suitable modelling solutions and build reliable distribution maps to deal with biodiversity and conservation necessities of bird species at finer scales. We have performed a multi‐scale method of modelling, which uses distance, climatic, and focal variables at different scales (neighboring window sizes), to estimate the efficient scale of each environmental predictor and enhance our knowledge on how birds interact with their complex environment. To identify the best radius for each focal variable and the most efficient impact scale of each predictor, we have fitted univariate models per species. In the last step, the final set of variables were subsequently employed to build ensemble of small models (ESMs) at a fine spatial resolution of 100 m and generate species distribution maps as tools of conservation. We could build useful habitat suitability models for the three groups of species in the national red list. Our results indicate that, in general, the most important variables were in the group of bioclimatic variables including “Bio11” (Mean Temperature of Coldest Quarter), and “Bio 4” (Temperature Seasonality), then in the focal variables including “Forest”, “Orchard”, and “Agriculture area” as potential foraging, feeding and nesting sites. Our distribution maps are useful for identifying the most threatened species and their habitat and also for improving conservation effort to locate bird hotspots. It is a powerful strategy to improve the ecological understanding of the distribution of bird species in a dynamic heterogeneous environment.     

Predicting global habitat suitability for stony corals on seamounts

Aim Globally, species distribution patterns in the deep sea are poorly resolved, with spatial coverage being sparse for most taxa and true absence data missing. Increasing human impacts on deep‐sea ecosystems mean that reaching a better understanding of such patterns is becoming more urgent. Cold‐water stony corals (Order Scleractinia) form structurally complex habitats (dense thickets or reefs) that can support a diversity of other associated fauna. Despite their widely accepted ecological importance, records of scleractinian corals on seamounts are patchy and simply not available for most of the global ocean. The objective of this paper is to model the global distribution of suitable habitat for stony corals on seamounts. Location Seamounts worldwide. Methods We compiled a database containing all accessible records of scleractinian corals on seamounts. Two modelling approaches developed for presence‐only data were used to predict global habitat suitability for seamount scleractinians: maximum entropy modelling (Maxent) and environmental niche factor analysis (ENFA). We generated habitat‐suitability maps and used a cross‐validation process with a threshold‐independent metric to evaluate the performance of the models. Results Both models performed well in cross‐validation, although the Maxent method consistently outperformed ENFA. Highly suitable habitat for seamount stony corals was predicted to occur at most modelled depths in the North Atlantic, and in a circumglobal strip in the Southern Hemisphere between 20° and 50° S and shallower than around 1500 m. Seamount summits in most other regions appeared much less likely to provide suitable habitat, except for small near‐surface patches. The patterns of habitat suitability largely reflect current biogeographical knowledge. Environmental variables positively associated with high predicted habitat suitability included the aragonite saturation state, and oxygen saturation and concentration. By contrast, low levels of dissolved inorganic carbon, nitrate, phosphate and silicate were associated with high predicted suitability. High correlation among variables made assessing individual drivers difficult. Main conclusions Our models predict environmental conditions likely to play a role in determining large‐scale scleractinian coral distributions on seamounts, and provide a baseline scenario on a global scale. These results present a first‐order hypothesis that can be tested by further sampling. Given the high vulnerability of cold‐water corals to human impacts, such predictions are crucial tools in developing worldwide conservation and management strategies for seamount ecosystems.     

Habitat suitability models for the sand lizard Liolaemus wiegmannii based on landscape characteristics in temperate coastal dunes in Argentina

The knowledge of environmental variables associated with the species occurrence allows the recognition of sites which fulfil ecological requirements eventually used for conservation of species. The coastal dunes of Argentina are inhabited by sand lizards. Anthropic activities have severely degraded this ecosystem, affecting the habitat structure at a large scale. In this context, the effects of landscape characteristics on the sand lizard's (Liolaemus wiegmannii, Liolaemidae) presence were analysed to build habitat suitability maps along the coastal dunes of Argentina. A thematic map of study area was obtained from supervised classification of satellite images to identify landscape characteristics. Surveys were conducted during the lizard activity season, and landscape variables were measured in two spatial units. All information collected was compiled into a Geographic Information System. The relationship between the presence of lizards and landscape variables was evaluated by Generalized Linear Models. The predictions of these models were transferred by using Geographic Information System to habitat suitability maps. Almost all individuals (80%) were observed in semi‐fixed dunes. The analysis of landscape metrics in the two spatial extents showed complementary results. The habitat suitability models suggest that: (i) heterogeneous landscapes composed by disaggregated patches of semi‐fixed dunes and low or null percentages of active dunes distant from the coastline are the preferred environments, and (ii) human modifications such as urbanizations and forestation of dunes, have a negative impact on species occurrence. Suitable habitats were almost absent in those sectors of coastal dunes with highest level of urbanization, whereas they were distributed almost continuously in those areas without human disturbances.     

Silvicolous on a Small Scale: Possibilities and Limitations of Habitat Suitability Models for Small,Elusive Mammals in Conservation Management and Landscape Planning

Species distribution and endangerment can be assessed by habitat-suitability modelling. This study addresses methodical aspects of habitat suitability modelling and includes an application example in actual species conservation and landscape planning. Models using species presence-absence data are preferable to presence-only models. In contrast to species presence data, absences are rarely recorded. Therefore, many studies generate pseudo-absence data for modelling. However, in this study model quality was higher with null samples collected in the field. Next to species data the choice of landscape data is crucial for suitability modelling. Landscape data with high resolution and ecological relevance for the study species improve model reliability and quality for small elusive mammals like Muscardinus avellanarius. For large scale assessment of species distribution, models with low-detailed data are sufficient. For regional site-specific conservation issues like a conflict-free site for new wind turbines, high-detailed regional models are needed. Even though the overlap with optimally suitable habitat for M. avellanarius was low, the installation of wind plants can pose a threat due to habitat loss and fragmentation. To conclude, modellers should clearly state the purpose of their models and choose the according level of detail for species and environmental data.     

The contribution of small collections to species distribution modelling: A case study from Fuireneae (Cyperaceae)

The recent and rapid digitization of biodiversity data from natural history collection (NHC) archives has enriched collections based data repositories; this data continues to inform studies of species' geographic distributions. Here we investigate the relative impact of plant data from small natural history collections (collections with < 100,000 specimens) on species distributional models in an effort to document the potential of data from small NHCs to contribute to and inform biodiversity research. We modelled suitable habitat of five test case species from Fuireneae (Cyperaceae) in the United States using specimen records available via the Global Biodiversity Information Facility and that of data ready to mobilize from two regional small herbaria. Data were partitioned into three datasets based on their source: 1) collections-based records from large NHCs accessed GBIF, 2) collections-based records from small NHCs accessed from GBIF, and 3) collections-based records from two small regional herbaria not yet mobilized to GBIF. We extracted and evaluated the ecological niche represented for each of the three datasets by applying dataset occurrences to 14 environmental factors, and we modelled habitat suitability using Maxent to compare the represented distribution of the environmental values among the datasets. Our analyses indicate that the data from small NHCs contributed unique information in both geographic and environmental space. When data from small collections were combined with data from large collections, species models of the ecological niche resulted in more refined predictions of habitat suitability, indicating that small collections can contribute unique occurrence data which enhance species distribution models by bridging geographic collection gaps and shifting modelled predictions of suitable habitat. Inclusion of specimen records from small collections in ongoing digitization efforts is essential for generating informed models of a species' niche and distribution.     

Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus)

Aim Data on geographical ranges are essential when defining the conservation status of a species, and in evaluating levels of human disturbance. Where locality data are deficient, presence‐only ecological niche modelling (ENM) can provide insights into a species’ potential distribution, and can aid in conservation planning. Presence‐only ENM is especially important for rare, cryptic and nocturnal species, where absence is difficult to define. Here we applied ENM to carry out an anthropogenic risk assessment and set conservation priorities for three threatened species of Asian slow loris (Primates: Nycticebus). Location Borneo, Java and Sumatra, Southeast Asia. Methods Distribution models were built using maximum entropy (MaxEnt) ENM. We input 20 environmental variables comprising temperature, precipitation and altitude, along with species locality data. We clipped predicted distributions to forest cover and altitudinal data to generate remnant distributions. These were then applied to protected area (PA) and human land‐use data, using specific criteria to define low‐, medium‐ or high‐risk areas. These data were analysed to pinpoint priority study sites, suitable reintroduction zones and protected area extensions. Results A jackknife validation method indicated highly significant models for all three species with small sample sizes (n = 10 to 23 occurrences). The distribution models represented high habitat suitability within each species’ geographical range. High‐risk areas were most prevalent for the Javan slow loris (Nycticebus javanicus) on Java, with the highest proportion of low‐risk areas for the Bornean slow loris (N. menagensis) on Borneo. Eighteen PA extensions and 23 priority survey sites were identified across the study region. Main conclusions Discriminating areas of high habitat suitability lays the foundations for planning field studies and conservation initiatives. This study highlights potential reintroduction zones that will minimize anthropogenic threats to animals that are released. These data reiterate the conclusion of previous research, showing MaxEnt is a viable technique for modelling species distributions with small sample sizes.     

Microclimate species distribution models estimate lower levels of climate-related habitat loss for salamanders

Species distribution models (SDMs) largely rely on free-air temperatures at coarse spatial resolutions to predict habitat suitability, potentially overlooking important microhabitat. Integrating microclimate data into SDMs may improve predictions of organismal responses to climate change and support targeting of conservation assets at biologically relevant scales, especially for small, dispersal-limited species vulnerable to climate-change-induced range loss. We integrated microclimate data that account for the buffering effects of forest vegetation into SDMs at a very high spatial resolution (3 m²) for three plethodontid salamander species in Great Smoky Mountains National Park (North Carolina and Tennessee). Microclimate SDMs were used to characterize potential changes to future plethodontid habitat, including habitat suitability and habitat spatial patterns. Additionally, we evaluated spatial discrepancies between predictions of habitat suitability developed with microclimate and coarse-resolution, free-air climate data. Microclimate SDMs indicated substantial losses to plethodontid ranges and highly suitable habitat by mid-century, but at much more conservative levels than coarse-resolution models. Coarse-resolution SDMs generally estimated higher mid-century losses to plethodontid habitat compared to microclimate models and consistently undervalued areas containing highly suitable microhabitat. Furthermore, microclimate SDMs revealed potential areas of future gain in highly suitable habitat within current species’ ranges, which may serve as climatic microrefugia. Taken together, this study highlights the need to develop microclimate SDMs that account for vegetation and its biophysical effects on near-surface temperatures. As microclimate datasets become increasingly available across the world, their integration into correlative and mechanistic SDMs will be imperative for accurately estimating organismal responses to climate change and helping environmental managers tasked with spatially prioritizing conservation assets.     

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.     

High resolution habitat suitability modelling for an endemic restricted-range Hawaiian insect (<Emphasis Type="Italic">Nysius wekiuicola</Emphasis>, Hemiptera: Lygaeidae)

Determining potentially suitable habitat is critical for effective species conservation and management, but can be challenging in remote or sensitive areas. An approach that combines non-intrusive spatial data collection techniques and supporting field data can lead to a better understanding of landscape-scale species distributions. Here we present two habitat suitability models, at 1 and 10 m resolutions, for the endemic wēkiu bug Nysius wekiuicola, a poorly-understood resident scavenging arthropod species present on the summit of Maunakea in Hawai‘i. Our models reveal that the wēkiu bug, restricted almost entirely to portions of cinder cones above 3500 m elevation, has a high degree of habitat specificity and represents a classically rare species. Across the 55 km² study area, 850 ha of potentially suitable habitat were identified at the 10 percentile training threshold, with the core area located at the true summit. Our results show that elevation and surficial mineralogy were the strongest predictors of suitable habitat, with lesser contributions from aspect and slope. Climatic variables also likely influence wēkiu bug distribution patterns, but were not included in our models due to the coarseness of available climate data and high correlation between variables. Relatively minor differences between the two models, in terms of identifying the locations and amount of suitable wēkiu bug habitat, and a higher measure of performance for the 10 m resolution model, suggest that coarser resolution input variables may characterize suitable habitat more efficiently than very fine 1 m resolution data. The suitability models generated as a result of this study will be directly incorporated into conservation management and restoration goals, and can easily be adapted for other arthropod species, leading to a more holistic understanding of metacommunity dynamics at the Maunakea summit.     

Genetic inference as a method for modelling occurrence: A viable alternative to visual surveys

Management and conservation require a comprehensive understanding of species distributions and habitat requirements. Reliable species occurrence data are critical in the face of climate change and other anthropogenic activity, but are often difficult to obtain, particularly for wide ranging species. This directly affects ecological models of occurrence and habitat suitability and, in turn, conservation and management decisions. We used generalized linear mixed‐effects models to identify ecological determinants of occurrence for four macropod species (across a region of tropical northern Australia) using a non‐invasive genetic scat approach with and without additional observation records from visual surveys. We show that genetically derived occurrence data, alone, can be used to develop informative ecological models that describe the inter‐specific habitat requirements of macropods. Furthermore, we show that genetic scat surveys of macropods are cheaper and less time consuming to conduct, and tend to provide more occurrence records (and less false absences) than visual surveys. We conclude that indirect surveys using molecular approaches have an important role to play in modelling species' occurrence, and developing future management practices and guidelines to aid species conservation.     

Modelling the distribution of a threatened habitat: the California sage scrub

Aim Using predictive species distribution and ecological niche modelling our objectives are: (1) to identify important climatic drivers of distribution at regional scales of a locally complex and dynamic system – California sage scrub; (2) to map suitable sage scrub habitat in California; and (3) to distinguish between bioclimatic niches of floristic groups within sage scrub to assess the conservation significance of analysing such species groups. Location Coastal mediterranean‐type shrublands of southern and central California. Methods Using point localities from georeferenced herbarium records, we modelled the potential distribution and bioclimatic envelopes of 14 characteristic sage scrub species and three floristic groups (south‐coastal, coastal–interior disjunct and broadly distributed species) based upon current climate conditions. Maxent was used to map climatically suitable habitat, while principal components analysis followed by canonical discriminant analysis were used to distinguish between floristic groups and visualize species and group distributions in multivariate ecological space. Results Geographical distribution patterns of individual species were mirrored in the habitat suitability maps of floristic groups, notably the disjunct distribution of the coastal–interior species. Overlap in the distributions of floristic groups was evident in both geographical and multivariate niche space; however, discriminant analysis confirmed the separability of floristic groups based on bioclimatic variables. Higher performance of floristic group models compared with sage scrub as a whole suggests that groups have differing climate requirements for habitat suitability at regional scales and that breaking sage scrub into floristic groups improves the discrimination between climatically suitable and unsuitable habitat. Main conclusions The finding that presence‐only data and climatic variables can produce useful information on habitat suitability of California sage scrub species and floristic groups at a regional scale has important implications for ongoing efforts of habitat restoration for sage scrub. In addition, modelling at a group level provides important information about the differences in climatic niches within California sage scrub. Finally, the high performance of our floristic group models highlights the potential a community‐level modelling approach holds for investigating plant distribution patterns.     

Food and habitats requirements of the Eurasian Scops Owl (Otus scops) in Switzerland revealed by very high-resolution multi-scale models

In Europe, agricultural practices have progressively evolved towards high productivity leading either to the intensification of productive and accessible areas or to the abandonment of less profitable sites. Both processes have led to the degradation of semi-natural habitats like extensive grasslands, threatening species such as the Eurasian Scops Owl Otus scops that rely on extensively managed agricultural landscapes. In this work, we aimed to assess the habitat preferences of the Scops Owl using habitat suitability models combined with a multi-scale approach. We generated a set of multi-scale predictors, considering both biotic and abiotic variables, built on two newly developed vegetation management and orthopteran abundance models. To select the variables to incorporate in a ‘best multi-scale model’, we chose the best spatial scale for each variable using univariate models and by calculating their relative importance through multi-model inference. Next, we built ensembles of small models (ESMs) at 10 different scales from 50 to 1000 m, and an additional model with each variable at its best scale (‘best multi-scale model’). The latter performed better than most of the other ESMs and allowed the creation of a high-resolution habitat suitability map for the species. Scops Owls showed a preference for dry sites with extensive and well-structured habitats with 30–40% bush cover, and relied strongly on semi-extensive grasslands covering at least 30% of the surface within 300 m of the territory centre and with high orthopteran availability near the centre (50-m radius), revealing a need for good foraging grounds near the nest. At a larger spatial scale within a radius of 1000 m, the habitat suitability of Scops Owls was negatively related to forest cover. The resulting ESM predictions provide valuable tools for conservation planning, highlighting sites in need of particular conservation efforts together with offering estimates of the percentage of habitat types and necessary prey abundance that could be used as targets in future management plans to ensure the persistence of the population.