Modeling regional-scale habitat of forest birds when land management guidelines are needed but information is limited

Conservation planning at broad spatial scales facilitates coherence between local land management and objectives set at the state or provincial level. Habitat suitability models are commonly used to identify key areas for conservation planning. The challenge is that habitat suitability models are data hungry, which limits their applicability to species for which detailed information exists, but managers need to address the needs of all at-risk species. We propose a modeling approach useful for regional-scale conservation planning that accommodates limited species knowledge, and identifies what managers should aim for at the local scale. For twenty at-risk bird species, we built models to identify potential habitat using both literature information and empirical data. Species occupancy within potential habitat depends on the presence of intrinsic elements, which we identified for each species so that managers can enhance these elements as appropriate. For most species, the estimated amount of habitat needed to meet population targets was <10% of the mapped potential habitat, with notable exceptions for Northern Goshawk (Accipiter gentilis; 100%), Brown Thrasher (Toxostoma rufum; 63.7%), and Veery (Catharus fuscescens; 17.9%). Model validation showed that interior forest species models performed best. Our modeling framework allowed us to build potential habitat models to various endpoints for different species, depending on the information available, and revealed a number of species for which basic natural history data are missing. Our potential habitat models provide regional perspective and guide local habitat management, and assist in identifying research priorities.     

Using multi-scale modelling to predict habitat suitability for species of conservation concern: The grey long-eared bat as a case study

Although spatial scale is important for understanding ecological processes and guiding conservation planning, studies combining a range of scales are rare. Habitat suitability modelling has been used traditionally to study broad-scale patterns of species distribution but can also be applied to address conservation needs at finer scales. We studied the ability of presence-only species distribution modelling to predict patterns of habitat selection at broad and fine spatial scales for one of the rarest mammals in the UK, the grey long-eared bat (Plecotus austriacus). Models were constructed with Maxent using broad-scale distribution data from across the UK (excluding Northern Ireland) and fine-scale radio-tracking data from bats at one colony. Fine-scale model predictions were evaluated with radio-tracking locations from bats from a distant colony, and compared with results of traditional radio-tracking data analysis methods (compositional analysis of habitat selection). Broad-scale models indicated that winter temperature, summer precipitation and land cover were the most important variables limiting the distribution of the grey long-eared bat in the UK. Fine-scale models predicted that proximity to unimproved grasslands and distance to suburban areas determine foraging habitat suitability around maternity colonies, while compositional analysis also identified unimproved grasslands as the most preferred foraging habitat type. This strong association with unimproved lowland grasslands highlights the potential importance of changes in agricultural practices in the past century for wildlife conservation. Hence, multi-scale models offer an important tool for identifying conservation requirements at the fine landscape level that can guide national-level conservation management practices.     

The importance of ecological scale for wildlife conservation in naturally fragmented environments: A case study of the brush-tailed rock-wallaby (Petrogale penicillata)

Determining the ecologically relevant spatial scales for predicting species occurrences is an important concept when determining species-environment relationships. Therefore species distribution modelling should consider all ecologically relevant spatial scales. While several recent studies have addressed this problem in artificially fragmented landscapes, few studies have researched relevant ecological scales for organisms that also live in naturally fragmented landscapes. This situation is exemplified by the Australian rock-wallabies’ preference for rugged terrain and we addressed the issue of scale using the threatened brush-tailed rock-wallaby (Petrogale penicillata) in eastern Australia. We surveyed for brush-tailed rock-wallabies at 200 sites in southeast Queensland, collecting potentially influential site level and landscape level variables. We applied classification trees at either scale to capture a hierarchy of relationships between the explanatory variables and brush-tailed rock-wallaby presence/absence. Habitat complexity at the site level and geology at the landscape level were the best predictors of where we observed brush-tailed rock-wallabies. Our study showed that the distribution of the species is affected by both site scale and landscape scale factors, reinforcing the need for a multi-scale approach to understanding the relationship between a species and its environment. We demonstrate that careful design of data collection, using coarse scale spatial datasets and finer scale field data, can provide useful information for identifying the ecologically relevant scales for studying species-environment relationships. Our study highlights the need to determine patterns of environmental influence at multiple scales to conserve specialist species such as the brush-tailed rock-wallaby in naturally fragmented landscapes.     

Woody species diversity in temperate Andean forests: The need for new conservation strategies

Chile has more than half of the temperate forests in the southern hemisphere. These have been included among the most threatened eco-regions in the world, because of the high degree of endemism and presence of monotypic genera. In this study, we develop empirical models to investigate present and future spatial patterns of woody species richness in temperate forests in south-central Chile. Our aims are both to increase understanding of species richness patterns in such forests and to develop recommendations for forest conservation strategies. Our data were obtained at multiple spatial scales, including field sampling, climate, elevation and topography data, and land-cover and spectrally derived variables from satellite sensor imagery. Climatic and land-cover variables most effectively accounted for tree species richness variability, while only weak relationships were found between explanatory variables and shrub species richness. The best models were used to obtain prediction maps of tree species richness for 2050, using data from the Hadley Centre’s HadCM3 model. Current protected areas are located far from the areas of highest tree conservation value and our models suggest this trend will continue. We therefore suggest that current conservation strategies are insufficient, a trend likely to be repeated across many other areas. We propose the current network of protected areas should be increased, prioritizing sites of both current and future importance to increase the effectiveness of the national protected areas system. In this way, target sites for conservation can also be chosen to bring other benefits, such as improved water supply to populated areas.     

Risk assessment: Simultaneously prioritizing the control of invasive plant species and the conservation of rare plant species

Although the consequences of the homogenization of Earth’s flora and fauna are not well understood, experts agree that biological invasions pose hazards to rare species. As a result, there is a need for a systematic approach to assess risks from invasive species. The Relative Risk Model can be adapted to assess combinations of rare species, invasive species, and regions. It also can be applied to different taxonomic groups and at different spatial scales. This flexibility makes it a promising tool for invasive species risk assessment. We used the Relative Risk Model to quantify risks posed to endangered plant species by non-indigenous invasive plant species in Nebraska.We modeled the suitable habitats for eight invasive plant species, which we subsequently compared to documented occurrences of endangered plant species in a Geographic Information System. We combined this data with an assessment of the ecological impacts of each invasive species in a regional risk assessment framework to simultaneously calculate relative risk scores for invasive plant species, imperiled plant species, and subregions. We assessed uncertainty with Monte Carlo simulations.The results of this assessment are discrete values indicating the relative threat posed by invasive species to rare species, the relative risk posed to the rare species, and the relative risk in subregions. Results indicate that the invasive species Elaeagnus angustifolia and Rhamnus cathartica pose the greatest risks to endangered plants in Nebraska. The rare species Panax quinquefolius and the subregion Western Corn Belt Plains show the highest risk scores.     

Predicting the invasion risk by the alien bee-hawking Yellow-legged hornet Vespa velutina nigrithorax across Europe and other continents with niche models

Vespa velutina nigrithorax, an Asian bee-hawking hornet, has been unintentionally introduced in south-western France before 2004 and is currently widely spreading across the country. Its arrival in northern Spain was reported in 2010. The potential invasion risk of the species is assessed using climatic suitability models. We used eight different modelling techniques within an ensemble forecast framework to show that the invasion success in south-western France could have been predicted using data from the native Asian range of the species, while we further used data from both the native and invaded ranges (including a recently established population in Korea) to better predict its potential invasion range across all continents. Results are discussed in terms of the interest of ecological niche modelling for invasion biology, realised niche of the invasive wasp, potential threats to native entomofauna and economic impacts of this new predator. A particular attention is paid to beekeeping activities that are nowadays already threatened by a wide panel of adversary factors.     

Habitat-suitability modelling to assess the effects of land-use changes on Dupont’s lark Chersophilus duponti: A case study in the Layna Important Bird Area

Habitat-suitability modelling is being increasingly used as a tool for conservation biology. Although studies at large spatial scales are more appropriate for reserve design and management, there is a scarcity of published work on local, high-resolution applications of such models. In this work we develop high-resolution habitat models (1 ha) and study habitat preferences (focal points) of Dupont’s lark Chersophilus duponti, an endangered shrub-steppe passerine, in the partially overlapping Special Protected Area for birds (SPA) and Important Bird Area (IBA) of “páramos de Layna” (NW Spain), to assess both the adequacy of the reserve’s limits and the effect of land-use changes on the species’ population size. Both analytical approaches show that the Dupont’s lark favours flat areas characterized by small shrubs with bare ground, so that, for example, a conversion of dry crops to shrubs promoted by agri-environment schemes under CAP could increase the population size up to 80%. Although the IBA and SPA are similar in size - as compelled by EU environmental policy - the latter shows rugged topography typically avoided by the species. We further discuss the possible conflict between EU environmental and agricultural policies on the conservation of this species and suggest it can be addressed with our study approach.     

A landscape model for quantifying the trade-off between conservation needs and economic constraints in the management of a semi-natural grassland community

We present a landscape model to investigate the ecological consequences and costs of different management regimes in semi-natural grasslands. The model integrates dynamic abiotic conditions, management (i.e. disturbance) regime and response of more than 50 characteristic plant and insect species by modelling the dynamics of relevant niche parameters as predictors for species distribution models. We compare our results for exemplary scenarios differing in spatial and temporal scales and exemplary species belonging to different functional groups through several steps of aggregation.Our analysis aims at the question whether an infrequent massive disturbance by rototilling can serve as a less expensive alternative to annual mowing for preserving the characteristic species composition of open dry grasslands in Southern Germany. Rototilling results in a shifting mosaic determining the habitat quality for plant and animal species that may reduce the survival of local or regional populations.For some meadow species as well as the encroaching shrub species, rototilling has a detrimental effect on regional habitat quality. Other species, e.g. weeds and annual pioneers, strongly benefit or show only negligible reaction. Since this is a multi-objective problem, there is a no magic bullet in selecting an optimum scenario of measures. But by visualising the trade-off between ecological consequences and costs, our model is a valuable tool for conservation managers providing a sound scientific basis for management decisions relying on available ecological knowledge. It is also an interesting example for a model describing complex communities in a relatively simple way, simultaneously considering the main driving factors.     

Extension of landscape-based population viability models to ecoregional scales for conservation planning

Landscape-based population models are potentially valuable tools in facilitating conservation planning and actions at large scales. However, such models have rarely been applied at ecoregional scales. We extended landscape-based population models to ecoregional scales for three species of concern in the Central Hardwoods Bird Conservation Region and compared model projections against long-term trend data from the North American Breeding Bird Survey. We used a spatially-explicit demographic model and structured the regional population into ecological subsections on the basis of habitat, landscape patterns, and demographic rates to assess species viability. Our model projections were within 2% of the Breeding Bird Survey trends over the last 40 years for each species. Wood thrush (Hylocichla mustelina) populations remained relatively stable over the simulation and worm-eating warbler (Helmitheros vermivorus) abundance increased throughout most of the time period until reaching carrying capacity. In contrast, the prairie warbler (Dendroica discolor) population steadily declined by 0.59% annually. The combination of habitat and demographic modeling allowed us to create models that address processes driving these populations at all scales, which is critical to understanding how regional populations respond to landscape processes such as habitat loss and fragmentation. Therefore, because it is spatially explicit and directly addresses population growth and viability, this approach provides a valuable foundation to planning conservation strategies, offering the ability to identify the most salient risks to viability and explore ways to address them.     

Patterns and processes of carbon, water and energy cycles across northern Australian landscapes: From point to region

Savannas comprise a large proportion of the terrestrial land surface and are highly varied in their composition, structure and function. These characteristics alter land-atmosphere exchanges of heat, water, carbon dioxide and other trace gases, which feed back to the climate at multiple scales. Australian savannas provide significant ecosystem services and further systematic scientific study is needed to sustainably manage these ecosystems. We undertook an interdisciplinary research effort to understand the patterns and processes of carbon, water and energy cycles across northern Australian landscapes across scales from point to region. We quantified the land surface-atmosphere exchanges across the vast region of Australian savannas using a hierarchical, integrated measurement and modelling approach to determine regional greenhouse gas and water budgets. The research team comprised groups from seven institutions and four countries. The research effort comprised of a multi- year measurement and modelling endeavour and culminated in an intensive field program held in September 2008 (late dry season). The program aimed to improve our knowledge of the spatial and temporal variability of land-atmosphere exchanges and the processes driving them as well as our ability to remotely sense them and simulate them using land surface models, which are crucial components of Global Climate Models. We attempted to robustly integrate small scale processes, such as leaf photosynthesis to larger scales applicable to modelling and remote sensing for the savanna region. In this paper we provide the scientific background and our overall approach to the program with reference to the papers in this “Savanna Patterns of Energy and Carbon Integrated Across the Landscape” (SPECIAL) issue.     

Environmental filtering vs. resource-based niche partitioning in diverse soil animal assemblages

Terrestrial invertebrates constitute most of described animal biodiversity and soil is a major reservoir of this diversity. In the classical attempt to understand the processes supporting biodiversity, ecologists are currently seeking to unravel the differential roles of environmental filtering and competition for resources in niche partitioning processes: these processes are in principle distinct although they may act simultaneously, interact at multiple spatial and temporal scales, and are often confounded in studies of soil communities. We used a novel combination of methods based on stable isotopes and trait analysis to resolve these processes in diverse oribatid mite assemblages at spatial scales at which competition for resources could in principle be a major driver. We also used a null model approach based on a general neutral model of beta diversity. A large and significant fraction of community variation was explainable in terms of linear and periodic spatial structures in the distribution of organic C, N and soil structure: species were clearly arranged along an environmental, spatially structured gradient. However, competition related trait differences did not map onto the distances separating species along the environmental gradient and neutral models provided a satisfying approximation of beta diversity patterns. The results represent the first robust evidence that in very diverse soil arthropod assemblages resource-based niche partitioning plays a minor role while environmental filtering remains a fundamental driver of species distribution.     

Role of climatic niche models in focal-species-based conservation planning: Assessing potential effects of climate change on Northern Spotted Owl in the Pacific Northwest, USA

Although well-studied vertebrates such as the Northern Spotted Owl (NSO) are often used as focal species in regional conservation plans, range shifts associated with climate change may compromise this role. I used the Maxent (maximum entropy) method to develop NSO distribution models from data on NSO locations, forest age, and an ensemble of climate projections. NSO presence was positively associated with the proportion of old and mature forest at two spatial scales. Winter precipitation was the most important climate variable, consistent with previous studies suggesting negative effects on survival and recruitment. Model results suggest that initial niche expansion may be followed by a contraction as climate change intensifies, but this prediction is uncertain due to variability in predicted changes in precipitation between climate projections. Although new reserves created by the US Northwest Forest Plan prioritized areas with greater biological importance for the NSO than did pre-existing reserves, the latter areas, which lie predominantly at higher elevations, increase in importance under climate change. In contrast with previous analyses of the region’s localized old-forest-associated species, vegetation rather than climate dominated NSO distribution models. Rigorous assessment of the implications of climate change for focal species requires development of dynamic vegetation models that incorporate effects of competitor species and altered disturbance regimes. The results suggest that, lacking such data, models that combine climate data with current data on habitat factors such as vegetation can inform conservation planning by providing less-biased estimates of potential range shifts than do niche models based on climate variables alone.     

The behaviour of soil process models of ammonia volatilization at contrasting spatial scales

Process models are commonly used in soil science to obtain predictions at a spatial scale that is different from the scale at which the model was developed, or the scale at which information on model inputs is available. When this happens, the model and its inputs require aggregation or disaggregation to the application scale, and this is a complex problem. Furthermore, the validity of the aggregated model predictions depends on whether the model describes the key processes that determine the process outcome at the target scale. Different models may therefore be required at different spatial scales. In this paper we develop a diagnostic framework which allows us to judge whether a model is appropriate for use at one or more spatial scales both with respect to the prediction of variations at those scale and in the requirement for disaggregation of the inputs. We show that spatially nested analysis of the covariance of predictions with measured process outcomes is an efficient way to do this. This is applied to models of the processes that lead to ammonia volatilization from soil after the application of urea. We identify the component correlations at different scales of a nested scheme as the diagnostic with which to evaluate model behaviour. These correlations show how well the model emulates components of spatial variation of the target process at the scales of the sampling scheme. Aggregate correlations were identified as the most pertinent to evaluate models for prediction at particular scales since they measure how well aggregated predictions at some scale correlate with aggregated values of the measured outcome. There are two circumstances under which models are used to make predictions. In the first case only the model is used to predict, and the most useful diagnostic is the concordance aggregate correlation. In the second case model predictions are assimilated with observations which should correct bias in the prediction, and errors in the variance; the aggregate correlations would be the most suitable diagnostic.     

Improving social acceptability of marine protected area networks: A method for estimating opportunity costs to multiple gear types in both fished and currently unfished areas

We present a novel method for calculating the opportunity costs to fishers from their displacement by the establishment of marine protected areas (MPAs). We used a fishing community in Kubulau District, Fiji to demonstrate this method. We modelled opportunity costs as a function of food fish abundance and probability of catch, based on gear type and market value of species. Count models (including Poisson, negative binomial and two zero-inflated models) were used to predict spatial abundance of preferred target fish species and were validated against field surveys. A profit model was used to investigate the effect of restricted access to transport on costs to fishers. Spatial distributions of fish within the three most frequently sighted food fish families (Acanthuridae, Lutjanidae, Scaridae) varied, with greatest densities of Lutjanidae and Acanthuridae on barrier forereefs and greatest densities of Scaridae on submerged reefs. Modelled opportunity cost indicated that highest costs to fishers arise from restricting access to the barrier forereefs. We included our opportunity cost model in Marxan, a decision support tool used for MPA design, to examine potential MPA configurations for Kubulau District, Fiji Islands. We identified optimum areas for protection in Kubulau with: (a) the current MPA network locked in place; and (b) a clean-slate approach. Our method of modelling opportunity cost gives an unbiased estimate for multiple gear types in a marine environment and can be applied to other regions using existing species data.     

The level of threat to restricted-range bird species can be predicted from mapped data on land use and human population

A key element in the efficient allocation of scarce resources for conservation is the identification of areas of high biological value and high threat. Habitat loss and human population density have proved useful predictors of spatial variation in the current threat status of species albeit at coarse spatial resolution. We present a global analysis, intersecting Endemic Bird Areas (EBAs), to which restricted-range bird species are endemic, with fine-scale data of agricultural extent and human population density and test: (a) how well variation in land use mapped at 0.5° × 0.5° resolution predicts spatial variation in threat status of species and (b) how the predictive power compares with that of human population density mapped at the same resolution. Variation among EBAs in the proportion of restricted-range species that are threatened can be predicted by both the proportion of land used for agriculture and human population density. Agricultural land use was a better predictor of threat status than human population. Further, the average levels of threat attributable to agriculture were better predicted by land use than human population density, whereas threats due to causes other than agriculture were equally well predicted by land use and human population density. We fitted quantitative empirical models to describe these relationships. These results could be used, together with spatially explicit future scenarios of land use change, to project the geographical distribution and magnitude of future threats to birds at global and regional scales.     

Quantifying conservation concern - Bayesian statistics, birds and the red lists

For species that are still widespread, obtaining accurate and precise measures of population change inevitably means gathering representative sample data rather than undertaking a complete census. In the UK, a system of raising ‘alerts’ utilises stochastic models for such data to identify species in rapid (>50%) or moderate (25-50%) decline across various temporal and spatial scales. Considerable improvements in interpretation can be made by explaining annual fluctuations in terms of explicit population models (rather than trends of an arbitrary mathematical form); through the simultaneous modelling of data from a complete or partial census with those providing information on the demographic rates employed in these models; and through adopting a Bayesian rather than a frequentist statistical approach. A Bayesian approach is natural for quantifying, in the form of a probability, the support provided by the data for assigning a species to each of the categories. Based on territory mapping and ringing data for the lapwing Vanellus vanellus, we describe such an approach. Trends are estimated more precisely than those under models previously employed in the alerts context. Some smoothing is induced, but realistic responses to years of severe weather are retained, and these are expressed also via model-averaged trends in key demographic parameters. We discuss the conservation implications for this declining species, and the wider potential arising from the ability to quantify confidence that population change has exceeded a threshold either generating conservation concern or justifying a subsequent programme of action for recovery.     

Regional simulation of long-term organic carbon stock changes in cropland soils using the DNDC model: 1. Large-scale model validation against a spatially explicit data set

Because of the large spatial and temporal variability of soil organic carbon (SOC) dynamics, a modelling approach is crucial in detailed regional analyses. Several estimates of regional scale SOC sequestration potential have been made using dynamic soil organic matter (SOM) models which have been linked to spatial databases contained within a Geographic Information System. In all these previous studies, a large‐scale model validation, which provides information on the general model performance for the study area under concern, was impossible because of lack of data. A data set of over 190 000 SOC measurements, grouped as means per community and covering the period 1989–2000 was available for Flanders in northern Belgium. In order to validate the DNDC model at a large spatial scale, we used this data set along with detailed pH, soil texture and crop areas which were all available at the municipality scale to simulate SOC stocks for the entire study area during the period 1990–2000. A minor adjustment of the initial distribution of SOC in the model's SOC pool was necessary to fit the simulated SOC stock changes to the measured decrease of −475 kg OC ha⁻¹ year⁻¹ (0–30 cm). Although DNDC was able to simulate the SOC stock changes well for the whole study area, the simulated decrease in the SOC stocks was overestimated for communities predominantly having sandy textures and underestimated for communities with silt loam to silt textures. This study also urges caution with the application of SOM models at regional scales after limited validation or calibration at the field scale as these do not guarantee good simulation of spatial variation in SOC changes.     

Habitat selection by the common wombat (Vombatus ursinus) in disturbed environments: Implications for the conservation of a ‘common’ species

The construction of habitat models is a repeatable technique for describing and mapping species distributions, the utility of which lies in enabling management to predict where a species is likely to occur within a landscape. Typically, habitat models have been used to establish habitat requirements for threatened species; however they have equal applicability for modelling local populations of common species. Often, few data exist on local populations of common species, and issues of abundance and habitat selection at varying scales are rarely addressed. We provide a habitat suitability model for the common wombat (Vombatus ursinus) in southern New South Wales. This species is currently perceived as abundant throughout its extensive range across temperate regions of eastern Australia, yet little factual survey data exist and populations appear under threat. We use wombat burrows to reflect habitat selection and as our basis for ecological modelling. We found that environmental variables representing proximity to cover, measures of vegetation and proximity to watercourses are important predictors of burrow presence. Extrapolation of habitat models identified an abundance of habitat suitable for burrows. However, burrows in many suitable areas were abandoned. Our estimate of the population size was similar to the total annual mortality associated with road-kill. Theoretically, given the availability of suitable habitat, common wombat populations in the region should be thriving. It seems likely that this area once supported a much higher number of wombats; however limiting factors such as road mortality and disease have reduced the populations. The persistence of wombats in the study region must be supported by migration from other populations. Our findings challenge the perception that wombats are currently common and not in need of monitoring, suggesting that perceptions of abundance are often clouded by socio-political motives rather than informed by biological and ecological factors.     

Models of the habitat associations and distributions of insectivorous bats of the Top End of the Northern Territory, Australia

Generalised linear modelling (GLM) was used to develop habitat models for 25 of the 28 microchiropteran bat species that occur in the wet-dry tropics of the Northern Territory (the ‘Top End’). Based on these models, a geographic information system (GIS) was used to derive probability of occurrence maps for each species. Almost all of the models identified a unique combination of environmental variables, and the resulting probability of occurrence maps revealed contrasting predicted distributions. The reliability of the models was variable. Based on model variances, 11 of the species models were considered to be weak (<30% of the deviance captured) whereas seven models were robust (>40% of the deviance captured). ROC plot analysis suggested all models were at least moderately robust (area under the ROC curve >0.7). Annual rainfall and habitat complexity were identified as significant variables in the majority of the models. All of the spatial models were combined to derive a probability map of species richness of microchiropteran bats in the Top End. This map shows greatest species richness in the north-west and north-central parts of the study area.