Predictive distribution models and their application in wildlife conservation

Wildlife managers are often faced with the difficult task of determining the distribution of species, and their preferred habitats, at large spatial scales. This task is even more challenging when the species of concern is in low abundance and/or the terrain is largely inaccessible. Spatially explicit distribution models, derived from multivariate statistical analyses and implemented in a geographic information system (GIS), can be used to predict the distributions of species and their habitats, thus making them a useful conservation tool. We present two such models: one for a dasyurid, the Swamp Antechinus (Antechinus minimus), and the other for a ground-dwelling bird, the Rufous Bristlebird (Dasyornis broadbenti), both of which are rare species occurring in the coastal heathlands of south-western Victoria. Models were generated using generalized linear modelling (GLM) techniques with species presence or absence as the independent variable and a series of landscape variables derived from GIS layers and high-resolution imagery as the predictors. The most parsimonious model, based on the Akaike Information Criterion, for each species then was extrapolated spatially in a GIS. Probability of species presence was used as an index of habitat suitability. Because habitat fragmentation is thought to be one of the major threats to these species, an assessment of the spatial distribution of suitable habitat across the landscape is vital in prescribing management actions to prevent further habitat fragmentation.

Testing the influence of survey method for building species distribution models of marine fishes

Accurate estimates of fish species occurrence are important to any species’ assessments and habitat suitability model. However, surveys of marine fishes are often biased by method. Surveys of marine fishes are often biased by method. Such bias could influence the interpretation of any habitat suitability model. With increasing emphasis on non-destructive sampling, underwater video techniques are commonly used without a thorough understanding of their advantages and disadvantages. This study compared data collected from baited remote underwater stereo-video systems and towed-video systems to provide occurrence data to develop habitat suitability models of nine temperate marine fishes. While numerous studies have compared modelling approaches in terms of model performance (i.e. via AUC or Kappa) the point of this paper was to highlight how very sensiblelooking, well-performing (based on AUC) models can provide different predictions of habitat suitability depending on which dataset is used.

Classifying complex human motion using point distribution models

The Point Distribution Model (PDM) has been successfully used in modelling shape variations in groups of static images. It has also been effectively adapted to temporal image sets and used to track moving bodies such as hands and walking persons. However standard models do not consider the temporal characteristics of the data and are purely models of shape. This research proposes an extension to the PDM which explicitly considers the temporal sequencing of the images in the motion. The modified model can then be built from temporal quantities such as linear velocity and acceleration which are derived from the images. The new model formulation also enables movements to be tracked and classified according to their distinguishing temporal characteristics. This has been tested against distinct sets of arm movements under varying sets of experimental conditions.

Application of species distribution models to explain and predict the distribution, abundance and assemblage structure of nearshore temperate reef fishes

 Aim: The purpose of this study was to create predictive species distribution models (SDMs) for temperate reef-associated fish species densities and fish assemblage diversity and richness to aid in marine conservation and spatial planning. Location: California, USA. Methods: Using generalized additive models, we associated fish species densities and assemblage characteristics with seafloor structure, giant kelp biomass and wave climate and used these associations to predict the distribution and assemblage structure across the study area. We tested the accuracy of these predicted extrapolations using an independent data set. The SDMs were also used to estimate larger scale abundances to compare with other estimates of species abundance (uniform density extrapolation over rocky reef and density extrapolations taking into account variations in geomorphic structure). Results: The SDMs successfully modelled the species-habitat relationships of seven rocky reef-associated fish species and showed that species' densities differed in their relationships with environmental variables. The predictive accuracy of the SDMs ranged from 0.26 to 0.60 (Pearson's r correlation between observed and predicted density values). The SDMs created for the fish assemblage-level variables had higher prediction accuracies with Pearson's r values of 0.61 for diversity and 0.71 for richness. The comparisons of the different methods for extrapolating species densities over a single marine protected area varied greatly in their abundance estimates with the uniform extrapolation (density values extrapolated evenly over the rocky reef) always estimating much greater abundances. The other two methods, which took into account variation in the geomorphic structure of the reef, provided much lower abundance estimates. Main conclusions: Species distribution models that combine geomorphic, oceanographic and biogenic habitat variables can reliably predict spatial patterns of species density and assemblage attributes of temperate reef fishes at spatial scales of 50 m. Thus, SDMs show great promise for informing spatial and ecosystem-based approaches to conservation and fisheries management. © 2015 John Wiley

Sensitivity of fine-scale species distribution models to locational uncertainty in occurrence data across multiple sample sizes

To generate realistic predictions, species distribution models require the accurate coregistration of occurrence data with environmental variables. There is a common assumption that species occurrence data are accurately georeferenced; however, this is often not the case. This study investigates whether locational uncertainty and sample size affect the performance and interpretation of fine-scale species distribution models. This study evaluated the effects of locational uncertainty across multiple sample sizes by subsampling and spatially degrading occurrence data. Distribution models were constructed for kelp (Ecklonia radiata), across a large study site (680 km2) off the coast of southeastern Australia. Generalized additive models were used to predict distributions based on fine-resolution (2·5 m cell size) seafloor variables, generated from multibeam echosounder data sets, and occurrence data from underwater towed video. The effects of different levels of locational uncertainty in combination with sample size were evaluated by comparing model performance and predicted distributions. While locational uncertainty was observed to influence some measures of model performance, in general this was small and varied based on the accuracy metric used. However, simulated locational uncertainty caused changes in variable importance and predicted distributions at fine scales, potentially influencing model interpretation. This was most evident with small sample sizes. Results suggested that seemingly high-performing, fine-scale models can be generated from data containing locational uncertainty, although interpreting their predictions can be misleading if the predictions are interpreted at scales similar to the spatial errors. This study demonstrated the need to consider predictions across geographic space rather than performance alone. The findings are important for conservation managers as they highlight the inherent variation in predictions between equally performing distribution models, and the subsequent restrictions on ecological interpretations.

Separating the influences of environment and species interactions on patterns of distribution and abundance : competition between large herbivores

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Much recent research has focused on the use of species distribution models to explore the influence(s) of environment (predominantly climate) on species’ distributions. A weakness of this approach is that it typically does not consider effects of biotic interactions, including competition, on species’ distributions.
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Here we identify and quantify the contribution of environmental factors relative to biotic factors (interspecific competition) to the distribution and abundance of three large, wide-ranging herbivores, the antilopine wallaroo (Macropus antilopinus), common wallaroo (Macropus robustus) and eastern grey kangaroo (Macropus giganteus), across an extensive zone of sympatry in tropical northern Australia.
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To assess the importance of competition relative to habitat features, we constructed models of abundance for each species incorporating habitat only and habitat + the abundance of the other species, and compared their respective likelihoods using Akaike's information criterion. We further assessed the importance of variables predicting abundance across models for each species.
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The best-supported models of antilopine wallaroo and eastern grey kangaroo abundance included both habitat and the abundance of the other species, providing evidence of interspecific competition. Contrastingly, models of common wallaroo abundance were largely influenced by climate and not the abundance of other species. The abundance of antilopine wallaroos was most influenced by water availability, eastern grey kangaroo abundance and the frequency of late season fires. The abundance of eastern grey kangaroos was most influenced by aspects of climate, antilopine wallaroo abundance and a measure of cattle abundance.
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Our study demonstrates that where census and habitat data are available, it is possible to reveal species’ interactions (and measure their relative strength and direction) between large, mobile and/or widely-distributed species for which competition is difficult to demonstrate experimentally. This allows discrimination of the influences of environmental factors and species interactions on species’ distributions, and should therefore improve the predictive power of species distribution models.

Are we predicting the actual or apparent distribution of temperate marine fishes?

Planning for resilience is the focus of many marine conservation programs and initiatives. These efforts aim to inform conservation strategies for marine regions to ensure they have inbuilt capacity to retain biological diversity and ecological function in the face of global environmental change – particularly changes in climate and resource exploitation. In the absence of direct biological and ecological information for many marine species, scientists are increasingly using spatially-explicit, predictive-modeling approaches. Through the improved access to multibeam sonar and underwater video technology these models provide spatial predictions of the most suitable regions for an organism at resolutions previously not possible. However, sensible-looking, well-performing models can provide very different predictions of distribution depending on which occurrence dataset is used. To examine this, we construct species distribution models for nine temperate marine sedentary fishes for a 25.7 km2 study region off the coast of southeastern Australia. We use generalized linear model (GLM), generalized additive model (GAM) and maximum entropy (MAXENT) to build models based on co-located occurrence datasets derived from two underwater video methods (i.e. baited and towed video) and fine-scale multibeam sonar based seafloor habitat variables. Overall, this study found that the choice of modeling approach did not considerably influence the prediction of distributions based on the same occurrence dataset. However, greater dissimilarity between model predictions was observed across the nine fish taxa when the two occurrence datasets were compared (relative to models based on the same dataset). Based on these results it is difficult to draw any general trends in regards to which video method provides more reliable occurrence datasets. Nonetheless, we suggest predictions reflecting the species apparent distribution (i.e. a combination of species distribution and the probability of detecting it). Consequently, we also encourage researchers and marine managers to carefully interpret model predictions.

How long should we ignore imperfect detection of species in the marine environment when modelling their distribution?

The application of the 'ecosystem approach' to marine conservation management demands knowledge of the distribution patterns of the target species or communities. This information is commonly obtained from species distribution models (SDMs). This article explores an important but rarely acknowledged assumption in these models: almost all species may be present, but simply not detected by the particular survey method. However, nearly all of these SDM approaches neglect this important characteristic. This leads to the violation of a fundamental assumption of these models, which presuppose the detection of a species is equal to one (i.e. at each survey locality, a species is perfectly detected). In this article, the concept of imperfect detection is discussed, how it potentially influences the prediction of species' distributions is examined, and some statistical methods that could be used to incorporate the detection probability of species in estimates of their distribution are suggested. The approaches discussed here could improve the collection and interpretation of marine biological survey data and provide a coherent way to incorporate detection probability estimates in the modelling of species distributions. This will ultimately lead to an unbiased and more rigorous understanding of the distribution of species in the marine environment.

Edge patterns in aquatic invertebrates explained by predictive models

Predictive frameworks for understanding and describing how animals respond to habitat fragmentation, particularly across edges, have been largely restricted to terrestrial systems. Abundances of zooplankton and meiofauna were measured across seagrasssand edges and the patterns compared with predictive models of edge effects. Artificial seagrass patches were placed on bare sand, and zooplankton and meiofauna were sampled with tube traps at five positions (from patch edges: 12, 60 and 130 cm into seagrass; and 12 and 60 cm onto sand). Position effects consisted of the following three general patterns: (1) increases in abundance around the seagrasssand edge (total abundance and cumaceans); (2) declining abundance from seagrass onto sand (calanoid copepods, harpacticoid copepods and amphipods); and (3) increasing abundance from seagrass onto sand (crustacean nauplii and bivalve larvae). The first two patterns are consistent with resource-distribution models, either as higher resources at the confluence of adjacent habitats or supplementation of resources from high-quality to low-quality habitat. The third pattern is consistent with reductions in zooplankton abundance as a consequence of predation or attenuation of currents by seagrass. The results show that predictive models of edge effects can apply to aquatic animals and that edges are important in structuring zooplankton and meiofauna assemblages in seagrass. © 2010 CSIRO.

Wave exposure as a predictor of benthic habitat distribution on high energy temperate reefs

The new found ability to measure physical attributes of the marine environment at high resolution across broad spatial scales has driven the rapid evolution of benthic habitat mapping as a field in its own right. Improvement of the resolution and ecological validity of seafloor habitat distribution models has, for the most part, paralleled developments in new generations of acoustic survey tools such as multibeam echosounders. While sonar methods have been well demonstrated to provide useful proxies of the relatively static geophysical patterns that reflect distribution of benthic species and assemblages, the spatially and temporally variable influence of hydrodynamic energy on habitat distribution have been less well studied. Here we investigate the role of wave exposure on patterns of distribution of near-shore benthic habitats. A high resolution spectral wave model was developed for a 624 km2 site along Cape Otway, a major coastal feature of western Victoria, Australia. Comparison of habitat classifications implemented using the Random Forests algorithm established that significantly more accurate estimations of habitat distribution were obtained by including a fine-scale numerical wave model, extended to the seabed using linear wave theory, than by using depth and seafloor morphology information alone. Variable importance measures and map interpretation indicated that the spatial variation in wave-induced bottom orbital velocity was most influential in discriminating habitat classes containing the canopy forming kelp Ecklonia radiata, a foundation kelp species that affects biodiversity and ecological functioning on shallow reefs across temperate Australasia. We demonstrate that hydrodynamic models reflecting key environmental drivers on wave-exposed coastlines are important in accurately defining distributions of benthic habitats. This study highlights the suitability of exposure measures for predictive habitat modeling on wave-exposed coastlines and provides a basis for continuing work relating patterns of biological distribution to remotely-sensed patterns of the physical environment.

Modelling habitat suitability of the swamp antechinus (Antechinus minimus maritimus) in the coastal heathlands of southern Victoria, Australia

In recent years, predictive habitat distribution models, derived by combining multivariate statistical analyses with Geographic Information System (GIS) technology, have been recognised for their utility in conservation planning. The size and spatial arrangement of suitable habitat can influence the long-term persistence of some faunal species. In southwestern Victoria, Australia, populations of the rare swamp antechinus (Antechinus minimus maritimus) are threatened by further fragmentation of suitable habitat. In the current study, a spatially explicit habitat suitability model was developed for A. minimus that incorporated a measure of vegetation structure. Models were generated using logistic regression with species presence or absence as the dependent variable and landscape variables, extracted from both GIS data layers and multi-spectral digital imagery, as the predictors. The most parsimonious model, based on the Akaike Information Criterion, was spatially extrapolated in the GIS. Probability of species presence was used as an index of habitat suitability. A negative association between A. minimus presence and both elevation and habitat complexity was evidenced, suggesting a preference for relatively low altitudes and a vegetation structure of low vertical complexity. The predictive performance of the selected model was shown to be high (91%), indicating a good fit of the model to the data. The proportion of the study area predicted as suitable habitat for A. minimus (Probability of occurrence greater-or-equal, slanted0.5) was 11.7%. Habitat suitability maps not only provide baseline information about the spatial arrangement of potentially suitable habitat for a species, but they also help to refine the search for other populations, making them an important conservation tool.

Understanding fish-habitat associations using video observations and sonar imaging

This thesis was motivated by the increasing role of species distribution models in managing marine fishes and their habitats. These models provided new information about fish-habitat associations. However, models are potentially influenced by fish behaviour towards underwater video systems.

Does urbanization have the potential to create an ecological trap for powerful owls (Ninox strenua)?

Landscape transformation associated with urbanization is one of the most damaging and pervasive impacts humans have on natural ecosystems. The response of species to increasing urbanization has become a major focus of research globally. Powerful owls ( Ninox strenua) are a top-order predator the have been shown to reside in urban environments, but increasing urbanization has also been demonstrated to significantly reduce available habitat. In this paper we use species distribution models established for key food and nesting resources of powerful owls across an urban-forest gradient to constrain habitat predictions from a previously developed powerful owl species distribution model. This multi-criteria decision analysis (MCDA) approach allowed us to investigate the impacts of urbanization on potential powerful owl habitat when challenged with food and nesting requirements. As powerful owls only use tree cavities for nesting we propose that the cue for settlement in an area is associated with the presence of habitat and food and as such breeding requirements may be disconnected from settlement requirements.Our results demonstrate that incorporation of a general prey resource (at least one group of arboreal marsupials) as a cue for settlement does not reduce the amount of available habitat for powerful owls substantially. Further constraining the model with a tree cavity resource, however, leads to a substantial reduction in powerful owl habitat in the urban and urban fringe environments. If a diverse prey resource (two or more groups of arboreal marsupials) is used as the cue for settlement, this sees a substantial reduction in available habitat in urban environments. Incorporation of tree cavities into this model does not reduce the available habitat for powerful owls substantially.We propose that powerful owls do not need a diverse prey base for survival, and that breeding resources are unlikely to be a cue for settlement. As such, we argue in this paper that increasing urbanization has the potential to create an ecological trap for powerful owls as there is a significant difference between habitat capable of supporting powerful owls, and habitat in which owls can breed.Management of powerful owls in urban environments will be difficult, but this research highlights the potential for the use of nest boxes to enhance the breeding activities in increasingly urbanized environments. Replacement of this critical resource may be able to reverse any potential ecological trap that is occurring. © 2014 Elsevier Ltd.

Implications of climate change for macrophytic rafts and their hitchhikers

Most models predicting changes to species distributions under future climate scenarios ignore dispersal processes, despite their importance in determining community structure in both terrestrial and aquatic systems ('supply-side ecology'). In the marine environment, facilitation of long-distance dispersal of coastal organisms by macrophytic rafts may be severely modified by climate impacts on raft supply, quality, and persistence, and on transport processes. Increasing storminess in the coastal zone, higher water temperatures, and changes in water circulation represent some of the key mechanisms that will directly affect rafts, while increases in herbivore metabolism due to higher water temperatures are likely to indirectly reduce raft longevity through raft consumption. Accurate predictions of climate impacts on coastal biodiversity will be con - tingent on resolution of factors influencing rafting so that this and other dispersal mechanisms can be incorporated into species distribution models. © 2011 Inter-Research.

Quantification of spatial and thematic uncertainty in the application of underwater video for benthic habitat mapping

This study presents an analysis of the application of underwater video data collected for training and validating benthic habitat distribution models. Specifically, we quantify the two major sources of error pertaining to collection of this type of reference data. A theoretical spatial error budget is developed for a positioning system used to co-register video frames to their corresponding locations at the seafloor. Second, we compare interpretation variability among trained operators assessing the same video frames between times over three hierarchical levels of a benthic classification scheme. Propagated error in the positioning system described was found to be highly correlated with depth of operation and varies from 1.5m near the surface to 5.7m in 100m of water. In order of decreasing classification hierarchy, mean overall observer agreement was found to be 98% (range 6%), 82% (range 12%) and 75% (range 17%) for the 2, 4, and 6 class levels of the scheme, respectively. Patterns in between-observer variation are related to the level of detail imposed by each hierarchical level of the classification scheme, the feature of interest, and to the amount of observer experience. © 2014 Copyright © Taylor & Francis Group, LLC.