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.     

The cost of complexity in forecasts of population abundances is reduced but not eliminated by borrowing information across space using a hierarchical approach

Anticipating ecological changes is paramount if we are to manage biodiversity and the services they provide to humanity. When forecasting population abundances, studies have shown that simple statistical models often have better forecast performance than complex models. These studies have evaluated forecasts of models fitted separately to data from single sites (single-site approach). Here, we aim to contrast the forecast performance and forecast horizon between a single-site approach and a hierarchical multi-site approach where a single model is fitted to data from multiple-sites, and to investigate how they vary with model complexity. We used 5273 population time series on 84 species from the Swedish breeding bird survey program, and found that simple models on average had better forecast performance and forecast horizon than complex models for both the single- and the multi-site approach. However, the cost of complexity was considerably reduced under the multi-site approach, while the proportion of species for which complex models had better forecast performance than simple models was also much larger than under the single-site approach. This suggests that the multi-site approach is useful for inclusion of more detailed processes which may benefit forecasts for some species and which are of importance for managers. Still, our results are in line with some previous studies suggesting that it is surprisingly difficult to construct complex models that, on average, beat trivial baseline forecasts.     

Modeling the distribution of a wide‐ranging invasive species using the sampling efforts of expert and citizen scientists

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Relationships between survival and habitat suitability of semi‐aquatic mammals

Spatial distribution and habitat selection are integral to the study of animal ecology. Habitat selection may optimize the fitness of individuals. Hutchinsonian niche theory posits the fundamental niche of species would support the persistence or growth of populations. Although niche‐based species distribution models (SDMs) and habitat suitability models (HSMs) such as maximum entropy (Maxent) have demonstrated fair to excellent predictive power, few studies have linked the prediction of HSMs to demographic rates. We aimed to test the prediction of Hutchinsonian niche theory that habitat suitability (i.e., likelihood of occurrence) would be positively related to survival of American beaver (Castor canadensis), a North American semi‐aquatic, herbivorous, habitat generalist. We also tested the prediction of ideal free distribution that animal fitness, or its surrogate, is independent of habitat suitability at the equilibrium. We estimated beaver monthly survival probability using the Barker model and radio telemetry data collected in northern Alabama, United States from January 2011 to April 2012. A habitat suitability map was generated with Maxent for the entire study site using landscape variables derived from the 2011 National Land Cover Database (30‐m resolution). We found an inverse relationship between habitat suitability index and beaver survival, contradicting the predictions of niche theory and ideal free distribution. Furthermore, four landscape variables selected by American beaver did not predict survival. The beaver population on our study site has been established for 20 or more years and, subsequently, may be approaching or have reached the carrying capacity. Maxent‐predicted increases in habitat use and subsequent intraspecific competition may have reduced beaver survival. Habitat suitability‐fitness relationships may be complex and, in part, contingent upon local animal abundance. Future studies of mechanistic SDMs incorporating local abundance and demographic rates are needed.     

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.     

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.     

Modeling bird species distribution change in fire prone Mediterranean landscapes: incorporating species dispersal and landscape dynamics

Species distribution models (SDMs) have traditionally been founded on the assumption that species distributions are in equilibrium with environmental conditions and that these species–environment relationships can be used to estimate species responses to environmental changes. Insight into the validity of this assumption can be obtained from comparing the performance of correlative species distribution models with more complex hybrid approaches, i.e. correlative and process‐based models that explicitly include ecological processes, thereby accounting for mismatches between habitat suitability and species occupancy patterns. Here we compared the ability of correlative SDMs and hybrid models, which can accommodate non‐equilibrium situations arising from dispersal constraints, to reproduce the distribution dynamics of the ortolan bunting Emberiza hortulana in highly dynamic, early successional, fire driven Mediterranean landscapes. Whereas, habitat availability was derived from a correlative statistical SDM, occupancy was modeled using a hybrid approach combining a grid‐based, spatially‐explicit population model that explicitly included bird dispersal with the correlative model. We compared species occupancy patterns under the equilibrium assumption and different scenarios of species dispersal capabilities. To evaluate the predictive capability of the different models, we used independent species data collected in areas affected to different degree by fires. In accordance with the view that disturbance leads to a disparity between the suitable habitat and the occupancy patterns of the ortolan bunting, our results indicated that hybrid modeling approaches were superior to correlative models in predicting species spatial dynamics. Furthermore, hybrid models that incorporated short dispersal distances were more likely to reproduce the observed changes in ortolan bunting distribution patterns, suggesting that dispersal plays a key role in limiting the colonization of recently burnt areas. We conclude that SDMs used in a dynamic context can be significantly improved by using combined hybrid modeling approaches that explicitly account for interactions between key ecological constraints such as dispersal and habitat suitability that drive species response to environmental changes.     

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.     

Multiple stressors facilitate the spread of a non‐indigenous bivalve in the Mediterranean Sea

Aim

The introduction of non‐indigenous species (NIS) via man‐made corridors connecting previously disparate oceanic regions is increasing globally. However, the environmental and anthropogenic factors facilitating invasion dynamics and their interactions are still largely unknown. This study compiles and inputs available data for the NIS bivalve Brachidontes pharaonis across the invaded biogeographic range in the Mediterranean basin into a species distribution model to predict future spread under a range of marine scenarios.

Location

Mediterranean Sea.

Methods

A systematic review produced the largest presence database ever assembled to inform the selection of biological, chemical and physical factors linked to the spread of B. pharaonis through the Suez Canal. We carried out a sensitivity analysis to simulate current and future trophic and salinity scenarios. A species distribution model was run to determine key drivers of invasion, quantify interactive impacts arising from a range of trophic states, salinity conditions and climatic scenarios and forecast future trajectories for the spread of NIS into new regions under multiple‐parameter scenarios (based on the main factors identified from the systematic review).

Results

Impacts on invasion trajectory arising from climate change and interactions with increasing salinity from the new opening of the canal were the primary drivers of expansion across the basin, the effects of which were further enhanced by eutrophication. Predictions of the current distribution were most accurate when multiple stressors were used to drive the model. A habitat suitability index developed at a subcontinental scale from model outputs identified novel favourable conditions for future colonization at specific locations under 2030 and 2050 climatic scenarios.

Main conclusions

Future expansion of B. pharaonis will be enhanced by climate‐facilitated increased sea temperature, interacting with increasing pressures from salinity and eutrophication. The spatially explicit risk output maps of invasions represent a powerful visual product for use in communication of the spread of NIS and decision‐support tools for scientists and policymakers. The suggested approach, the observed distribution pattern and driving processes can be applied to other NIS species and regions by providing novel forecasts of species occurrences under future multiple stressor scenarios and the location of suitable recipient habitats with respect to anthropogenic and environmental parameters.     

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.     

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.     

Projecting marine species range shifts from only temperature can mask climate vulnerability

Climate change is causing range shifts in many marine species, with implications for biodiversity and fisheries. Previous research has mainly focused on how species' ranges will respond to changing ocean temperatures, without accounting for other environmental covariates that could affect future distribution patterns. Here, we integrate habitat suitability modeling approaches, a high‐resolution global climate model projection, and detailed fishery‐independent and ‐dependent faunal datasets from one of the most extensively monitored marine ecosystems—the U.S. Northeast Shelf. We project the responses of 125 species in this region to climate‐driven changes in multiple oceanographic factors (e.g., ocean temperature, salinity, sea surface height) and seabed characteristics (i.e., rugosity and depth). Comparing model outputs based on ocean temperature and seabed characteristics to those that also incorporated salinity and sea surface height (proxies for primary productivity and ocean circulation features), we explored how an emphasis on ocean temperature in projecting species' range shifts can impact assessments of species' climate vulnerability. We found that multifactor habitat suitability models performed better in explaining and predicting species historical distribution patterns than temperature‐based models. We also found that multifactor models provided more concerning assessments of species' future distribution patterns than temperature‐based models, projecting that species' ranges will largely shift northward and become more contracted and fragmented over time. Our results suggest that using ocean temperature as a primary determinant of range shifts can significantly alter projections, masking species' climate vulnerability, and potentially forestalling proactive management.     

Niche models tell half the story: spatial context and life‐history traits influence species responses to global change

Aim While niche models are typically used to assess the vulnerability of species to climate change, they have been criticized for their limited assessment of threats other than climate change. We attempt to evaluate this limitation by combining niche models with life‐history models to investigate the relative influence of climate change and a range of fire regimes on the viability of a long‐lived plant population. Specifically, we investigate whether range shift due to climate change is a greater threat to an obligate seeding fire‐prone shrub than altered fire frequency and how these two threatening processes might interact. Location Australian sclerophyll woodland and heathland. Methods The study species is Leucopogon setiger, an obligate seeding fire‐prone shrub. A spatially explicit stochastic matrix model was constructed for this species and linked with a dynamic niche model and fire risk functions representing a suite of average fire return intervals. We compared scenarios with a variety of hypothetical patches, a patch framework based upon current habitat suitability and one with dynamic habitat suitability based on climate change scenarios A1FI and A2. Results Leucopogon setiger was found to be sensitive to fire frequency, with shorter intervals reducing expected minimum abundances (EMAs). Spatial decoupling of fires across the landscape reduced the vulnerability of the species to shortened fire frequencies. Shifting habitat, while reducing EMAs, was less of a threat to the species than frequent fire. Main conclusions Altered fire regime, in particular more frequent fires relative to the historical regime, was predicted to be a strong threat to this species, which may reflect a vulnerability of obligate seeders in general. Range shifts induced by climate change were a secondary threat when habitat reductions were predicted. Incorporating life‐history traits into habitat suitability models by linking species distribution models with population models allowed for the population‐level evaluation of multiple stressors that affect population dynamics and habitat, ultimately providing a greater understanding of the impacts of global change than would be gained by niche models alone. Further investigations of this type could elucidate how particular bioecological factors can affect certain types of species under global change.     

Predicting the potential distribution of a riparian invasive plant: the effects of changing climate,flood regimes and land‐use patterns

Climate change is likely to affect plants in multiple ways, but predicting the consequences for habitat suitability requires a process‐based understanding of the interactions. This is at odds with existing approaches that are mostly phenomenological and largely restricted to predicting the effects of changing temperature and rainfall on species distributions at a coarse spatial scale. We examine the multiple effects of climate change, including predicting the effects of altered flood regimes and land‐use change, on the potential distribution of the invasive riparian species lippia (Phyla canescens) across a 26 000 km² catchment in eastern Australia. We determined habitat suitability for lippia by combining process‐understanding of experts and an eco‐physiological bioclimatic model within a Bayesian belief network. The bioclimatic model predicted substantial changes in habitat suitability by 2070 under both a wetter (Echam Mark 3) and drier (Hadley Centre Mark 2) climate change scenario, but only the more likely drier scenario reduced suitability in our test region. The area suitable for lippia was predicted to increase at least threefold with increased flooding under a wet climate scenario, although this would be partially negated by land‐use change to cultivation. The region would become unsuitable to lippia with reduced flooding under a drier scenario irrespective of land‐use changes, although existing populations would persist if grazing persisted. Independent field validation verified model structure and parameterization, and therefore the opinion of experts, but identified site‐scale deficiencies in the available environmental data layers. Model predictions suggest that adaptation options for managing lippia will be greatly reduced under a drying scenario, but identify potential restoration opportunities under either scenario. This work highlights the value of predictive models that incorporate process‐understanding at sufficiently fine spatial resolution to capture the important processes underpinning habitat suitability.     

On the use of climate covariates in aquatic species distribution models: are we at risk of throwing out the baby with the bath water?

Species distribution models (SDMs) in river ecosystems can incorporate climate information by using air temperature and precipitation as surrogate measures of instream conditions or by using independent models of water temperature and hydrology to link climate to instream habitat. The latter approach is preferable but constrained by the logistical burden of developing water temperature and hydrology models. We therefore assessed whether regional scale, freshwater SDM predictions are fundamentally different when climate data versus instream temperature and hydrology are used as covariates. Maximum entropy (MaxEnt) SDMs were built for 15 freshwater fishes using one of two covariate sets: 1) air temperature and precipitation (climate variables) in combination with physical habitat variables; or 2) water temperature, hydrology (instream variables) and physical habitat. Three procedures were then used to compare results from climate vs instream models. First, equivalence tests assessed average pairwise differences (site‐specific comparisons throughout each species’ range) among climate and instream models. Second, ‘congruence’ tests determined how often the same stream segments were assigned high habitat suitability by climate and instream models. Third, Schoener's D and Warren's I niche overlap statistics quantified range‐wide similarity in predicted habitat suitability from climate vs instream models. Equivalence tests revealed small, pairwise differences in habitat suitability between climate and instream models (mean pairwise differences in MaxEnt raw scores for all species < 3 × 10^–4). Congruence tests showed a strong tendency for climate and instream models to predict high habitat suitability at the same stream segments (median congruence = 68%). D and I statistics reflected a high margin of overlap among climate and instream models (median D = 0.78, median I = 0.96). Overall, we found little support for the hypothesis that SDM predictions are fundamentally different when climate versus instream covariates are used to model fish species’ distributions at the scale of the Columbia Basin.     

How citizen science could improve species distribution models and their independent assessment

1. Species distribution models (SDM) have been increasingly developed in recent years, but their validity is questioned. Their assessment can be improved by the use of independent data, but this can be difficult to obtain and prohibitive to collect. Standardized data from citizen science may be used to establish external evaluation datasets and to improve SDM validation and applicability.
2. We used opportunistic presence‐only data along with presence–absence data from a standardized citizen science program to establish and assess habitat suitability maps for 9 species of amphibian in western France. We assessed Generalized Additive and Random Forest Models’ performance by (1) cross‐validation using 30% of the opportunistic dataset used to calibrate the model or (2) external validation using different independent datasets derived from citizen science monitoring. We tested the effects of applying different combinations of filters to the citizen data and of complementing it with additional standardized fieldwork.
3. Cross‐validation with an internal evaluation dataset resulted in higher AUC (Area Under the receiver operating Curve) than external evaluation causing overestimation of model accuracy and did not select the same models; models integrating sampling effort performed better with external validation. AUC, specificity, and sensitivity of models calculated with different filtered external datasets differed for some species. However, for most species, complementary fieldwork was not necessary to obtain coherent results, as long as the citizen science data were strongly filtered.
4. Since external validation methods using independent data are considered more robust, filtering data from citizen sciences may make a valuable contribution to the assessment of SDM. Limited complementary fieldwork with volunteer''s participation to complete ecological gradients may also possibly enhance citizen involvement and lead to better use of SDM in decision processes for nature conservation.

     

Vanishing islands in the sky? A comparison of correlation- and mechanism-based forecasts of range dynamics for montane salamanders under climate change

Forecasting the effects of climate change on species and populations is a fundamental goal of conservation biology, especially for montane endemics which seemingly are under the greatest threat of extinction given their association with cool, high elevation habitats. Species distribution models (also known as niche models) predict where on the landscape there is suitable habitat for a species of interest. Correlative niche modeling, the most commonly employed approach to predict species' distributions, relies on correlations between species' localities and current environmental data. This type of model could spuriously forecast less future suitable habitat because species' current distributions may not adequately represent their thermal tolerance, and future climate conditions may not be analogous to current conditions. We compared the predicted distributions for three montane species of Plethodon salamanders in the southern Appalachian Mountains of North America using a correlative modeling approach and a mechanistic model. The mechanistic model incorporates species-specific physiology, morphology and behavior to predict an annual energy budget on the landscape. Both modeling approaches performed well at predicting the species' current distributions and predicted that all species could persist in habitats at higher elevation through 2085. The mechanistic model predicted more future suitable habitat than the correlative model. We attribute these differences to the mechanistic approach being able to model shifts in key range-limiting biological processes (changes in surface activity time and energy costs) that the correlative approach cannot. Choice of global circulation model (GCM) contributed significantly to distribution predictions, with a tenfold difference in future suitability based on GCM, indicating that GCM variability should be either directly included in models of species distributions or, indirectly, through the use of multi-model ensemble averages. Our results indicate that correlative models are over-predicting habitat loss for montane species, suggesting a critical need to incorporate mechanisms into forecasts of species' range dynamics.     

Predicting the spatial distribution of the blue-spotted maskray Neotrygon kuhlii (Myliobatiformes, Dasyatidae) on the Australian North and Northwest Shelf comparing two different methods of habitat modeling

Knowledge about distribution and habitat requirements of species is important for analyzing their role in marine ecosystems or establishing sanctuaries. However, knowledge is scarce especially in many chondrichthyan species. In this study, the spatial distribution of the stingray Neotrygon kuhlii on the Australian North and Northwest Shelf was predicted model-based for the first time. Predictions based on two different types of habitat suitability models, logistic regression and maximum entropy modeling. Catch data of N. kuhlii from Australian trawl surveys combined with randomly selected pseudo-absences were used for modeling together with data sets of several environmental variables. Both modeling methods yielded plausible and validated habitat suitability models containing water depth and salinity as significant independent variables. The model-based predictions of the probability of occurrence of N. kuhlii were similar for both methods and thus emphasized the goodness of the models. Following the predictions, N. kuhlii has its highest probability of occurrence in about 60 m water depth and at a salinity of about 35 PSU. The results indicate that both modeling methods are powerful tools to predict spatial distribution and habitat quality for marine fish species. Therefore, they are suitable for detecting possible distribution in areas with only few field records.     

Temporal transferability of wildlife habitat models: implications for habitat monitoring

Aim Temporal transferability is an important issue when habitat models are used beyond the time frame corresponding to model development, but has not received enough attention, particularly in the context of habitat monitoring. While the combination of remote sensing technology and habitat modelling provides a useful tool for habitat monitoring, the effect of incorporating remotely sensed data on model transferability is unclear. Therefore, our objectives were to assess how different satellite‐derived variables affect temporal transferability of habitat models and their usefulness for habitat monitoring. Location Wolong Nature Reserve, Sichuan Province, China. Methods We modelled giant panda habitat with the maximum entropy algorithm using panda presence data collected in two time periods and four different sets of predictor variables representing land surface phenology. Each predictor variable set contained either a time series of smoothed wide dynamic range vegetation index (WDRVI) or 11 phenology metrics, both derived from single‐year or multi‐year (i.e. 3‐year) remotely sensed imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). We evaluated the ability of models obtained with these four variable sets to predict giant panda habitat within and across time periods by using threshold‐independent and threshold‐dependent evaluation methods and five indices of temporal transferability. Results Our results showed that models developed with the four variable sets were all useful for characterizing and monitoring giant panda habitat. However, the models developed using multi‐year data exhibited significantly higher temporal transferability than those developed using single‐year data. In addition, models developed with phenology metrics, especially when using multi‐year data, exhibited significantly higher temporal transferability than those developed with the time series. Main conclusions The integration of land surface phenology, captured by high temporal resolution remotely sensed imagery, with habitat modelling constitutes a suitable tool for characterizing wildlife habitat and monitoring its temporal dynamics. Using multi‐year phenology metrics reduces model complexity, multicollinearity among predictor variables and variability caused by inter‐annual climatic fluctuations, thereby increasing the temporal transferability of models. This study provides useful guidance for habitat monitoring through the integration of remote sensing technology and habitat modelling, which may be useful for the conservation of the giant panda and many other species.     

Integrating species distribution models and interacting particle systems to predict the spread of an invasive alien plant

Aim We demonstrate how to integrate two widely used tools for modelling the spread of invasive plants, and compare the performance of the combined model with that of its individual components using the recent range dynamics of the invasive annual weed Ambrosia artemisiifolia L. Location Austria. Methods Species distribution models, which deliver habitat‐based information on potential distributions, and interacting particle systems, which simulate spatio‐temporal range dynamics as dependent on neighbourhood configurations, were combined into a common framework. We then used the combined model to simulate the invasion of A. artemisiifolia in Austria between 1990 and 2005. For comparison, simulations were also performed with models that accounted only for habitat suitability or neighbourhood configurations. The fit of the three models to the data was assessed by likelihood ratio tests, and simulated invasion patterns were evaluated against observed ones in terms of predictive discrimination ability (area under the receiver operating characteristic curve, AUC) and spatial autocorrelation (Moran’s I). Results The combined model fitted the data significantly better than the single‐component alternatives. Simulations relying solely on parameterized spread kernels performed worst in terms of both AUC and spatial pattern formation. Simulations based only on habitat information correctly predicted infestation of susceptible areas but reproduced the autocorrelated patterns of A. artemisiifolia expansion less adequately than did the integrated model. Main conclusions Our integrated modelling approach offers a flexible tool for forecasts of spatio‐temporal invasion patterns from landscape to regional scales. As a further advantage, scenarios of environmental change can be incorporated consistently by appropriately updating habitat suitability layers. Given the susceptibility of many alien plants, including A. artemisiifolia, to both land use and climate changes, taking such scenarios into account will increasingly become relevant for the design of proactive management strategies.