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.

Habitat suitability for marine fishes using presence-only modelling and multibeam sonar

Improved access to multibeam sonar and underwater video technology is enabling scientists to use spatially-explicit, predictive modelling to improve our understanding of marine ecosystems. With the growing number of modelling approaches available, knowledge of the relative performance of different models in the marine environment is required. Habitat suitability of 5 demersal fish taxa in Discovery Bay, south-east Australia, were modelled using 10 presence-only algorithms: BIOCLIM, DOMAIN, ENFA (distance geometric mean [GM], distance harmonic mean [HM], median [M], area-adjusted median [Ma], median + extremum [Me], area-adjusted median + extremum [Mae] and minimum distance [Min]), and MAXENT. Model performance was assessed using kappa and area under curve (AUC) of the receiver operator characteristic. The influence of spatial range (area of occupancy) and environmental niches (marginality and tolerance) on modelling performance were also tested. MAXENT generally performed best, followed by ENFA-GM and -HM, DOMAIN, BIOCLIM, ENFA-M, -Min, -Ma, -Mae and -Me algorithms. Fish with clearly definable niches (i.e. high marginality) were most accurately modelled. Generally, Euclidean distance to nearest reef, HSI-b (backscatter), rugosity and maximum curvature were the most important variables in determining suitable habitat for the 5 demersal fish taxa investigated. This comparative study encourages ongoing use of presence-only approaches, particularly MAXENT, in modelling suitable habitat for demersal marine fishes.

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.

Spatial patterns, landscape genetics and post virus recovery of blacklip abalone, Haliotis rubra (Leach), in the western commercial fishing zone of Victoria

Historically, collecting nearshore habitat information has been problematic. Existing methods, such as aerial and satellite image interpretation are limited due to the attenuation of light in the water column obscuring the seabed structure. The advent of airborne bathymetric LiDAR (Light Detection and Ranging) systems (laser scanning of the seabed) now provides high-resolution seabed ‘images’ in areas that were previously difficult to survey. LiDAR imagery is available for the entire coastline of Victoria, Australia to depths of around 25 m, after being initially collected for climate change modelling by the Future Coasts Program (http://www.climatechange.vic.gov.au/adapting-to-climate-change/future-coasts). This dataset has provided the opportunity to test its applicability to inform fisheries management. Detailed geophysical information combined with spatially explicit AbTrack GPS located fisheries records and targeted genetic sampling is used in this study to provide a better understanding of the extent of available fishing grounds, direction of fishing effort and stock population structure within the Victorian western zone abalone fishery.
The species distribution modelling technique MaxEnt was used to produce a potential habitat suitability map for abalone in an attempt to capture the effective footprint of the  fishery. Also, by interrogating the spatially defined effort localities, we demonstrate an approach that may be used to identify areas where fishing effort is concentrated, and how this parameter changes temporally.
Despite barriers to adult dispersal (soft sediment barriers between reef patches), the genetic study indicates that larval movement is able to homogenize the gene pool over  large geographic distances. The western, central and eastern zone abalone stocks in Victoria were found to be a single large panmictic unit. This indicates high levels of stock connectivity and no obvious impacts of Abalone Viral Ganglioneuritis (AVG) on the genetic health of western zone stocks. We used detailed seafloor structure information interpreted from LiDAR to inform a replicated hierarchical fine scale genetic sampling design. We demonstrated that there may be extensive migration among abalone stocks across the Victorian abalone fishery.
This is contrary to previous studies that suggest recruitment is highly localised. In combination, these findings provide a valuable insight into the biology of H. rubra and immediate benefits for fisheries management. We discuss these results in the context of predicting resilience and adaptive potential of H. rubra stocks to environmental pressures and the spread of heritable diseases.
Adoption pathways are also provided to benefit future stock augmentation activities to catalyse the recovery of AVG affected reef codes. As larval dispersal is likely to be spatially and temporally variable, some AVG affected stocks are likely to recover through natural recruitment, while others will benefit from augmentation activities to ‘kick-start’ stock recovery. Evidence of neutral genetic homogeneity across Victorian reef codes suggests that the relocation of animals is unlikely to have significant genetic risks; however the potential for locally adaptive genetic differences may exist, and should be taken into consideration in future stock augmentation planning.
When combined, the spatial and genetic analyses provide valuable insights into stock productivity within the western zone fishery. Reefs appear to be expansive and support much available habitat, and the movement of larvae among reef structures is likely to be extensive in this region. Consequently, we propose that colonisation success and productivity is likely to be driven by ecological factors such as resources and/or competition, or physical factors such as wave exposure.

Simplification of arboreal marsupial assemblages in response to increasing urbanization

Arboreal marsupials play an essential role in ecosystem function including regulating insect and plant populations, facilitating pollen and seed dispersal and acting as a prey source for higher-order carnivores in Australian environments. Primarily, research has focused on their biology, ecology and response to disturbance in forested and urban environments. We used presence-only species distribution modelling to understand the relationship between occurrences of arboreal marsupials and eco-geographical variables, and to infer habitat suitability across an urban gradient. We used post-proportional analysis to determine whether increasing urbanization affected potential habitat for arboreal marsupials. The key eco-geographical variables that influenced disturbance intolerant species and those with moderate tolerance to disturbance were natural features such as tree cover and proximity to rivers and to riparian vegetation, whereas variables for disturbance tolerant species were anthropogenic-based (e.g., road density) but also included some natural characteristics such as proximity to riparian vegetation, elevation and tree cover. Arboreal marsupial diversity was subject to substantial change along the gradient, with potential habitat for disturbance-tolerant marsupials distributed across the complete gradient and potential habitat for less tolerant species being restricted to the natural portion of the gradient. This resulted in highly-urbanized environments being inhabited by a few generalist arboreal marsupial species. Increasing urbanization therefore leads to functional simplification of arboreal marsupial assemblages, thus impacting on the ecosystem services they provide.

Extinction risk in cloud forest fragments under climate change and habitat loss

Aim: To quantify the consequences of major threats to biodiversity, such as climate and land-use change, it is important to use explicit measures of species persistence, such as extinction risk. The extinction risk of metapopulations can be approximated through simple models, providing a regional snapshot of the extinction probability of a species. We evaluated the extinction risk of three species under different climate change scenarios in three different regions of the Mexican cloud forest, a highly fragmented habitat that is particularly vulnerable to climate change. Location Cloud forests in Mexico.
Methods: Using Maxent, we estimated the potential distribution of cloud forest for three different time horizons (2030, 2050 and 2080) and their overlap with protected areas. Then, we calculated the extinction risk of three contrasting vertebrate species for two scenarios: (1) climate change only (all suitable areas of cloud forest through time) and (2) climate and land-use change (only suitable areas within a currently protected area), using an explicit patch-occupancy approximation model and calculating the joint probability of all populations becoming extinct when the number of remaining patches was less than five.
Results: Our results show that the extent of environmentally suitable areas for cloud forest in Mexico will sharply decline in the next 70 years. We discovered that if all habitat outside protected areas is transformed, then only species with small area requirements are likely to persist. With habitat loss through climate change only, high dispersal rates are sufficient for persistence, but this requires protection of all remaining cloud forest areas.
Main conclusions: Even if high dispersal rates mitigate the extinction risk of species due to climate change, the synergistic impacts of changing climate and land use further threaten the persistence of species with higher area requirements. Our approach for assessing the impacts of threats on biodiversity is particularly useful when there is little time or data for detailed population viability analyses.

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.

Modelling of birds and locust-control operations in Eastern Australia

Locust outbreaks provide an abundant, but unpredictable food source for many native species in Australia. For economic reasons, locust control is unavoidable and can affect a considerable area in Eastern Australia. Depending on the pesticide applied, locust control operations may affect birds in treated areas, either directly through intoxication of the predator, or indirectly, through elimination of its prey. As a preliminary step in identifying the potential impact of these operations on native species, the co-occurrence of birds and locust control operations was assessed using GIS mapping techniques. Data from the Birds of Australia New Atlas provided information about species' distribution between latitudes 17 and 37 degree S, and longitudes 136 and 152 degree E. Of the 834 species present in this region, 292 were chosen on the basis of their geographical distribution and occurrence west of the Great Dividing Range. Sightings for each species were mapped using reporting rates and number of observations per half-degree grid cells. Birds were categorised by habitat, distribution, movement and feeding habits and those species reported to consume Orthopterans were noted. APLC locust survey (1987–2000) and spraying data (1977–2002) were analysed and overlapped with soil and vegetation maps obtained from Geoscience Australia and Environment Australia to find significant hotspots for locust occurrence. These maps were then overlayed with bird distributions to identify the species most likely to be in areas of locust presence and spraying operations.

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.

Satellite imagery as a single source of predictor variables for habitat suitability modelling: how Landsat can inform the conservation of a critically endangered lemur

1. Statistical modelling of habitat suitability is an important tool for planning conservation interventions, particularly for areas where species distribution data are expensive or hard to collect. Sometimes however the predictor variables typically used in habitat suitability modelling are themselves difficult to obtain or not meaningful at the geographical extent of the study, as is the case for the Alaotran gentle lemur Hapalemur alaotrensis, a critically endangered lemur confined to the marshes of Lake Alaotra in Madagascar.2. We developed a habitat suitability model where all predictor variables, including vegetation indices and image texture measures at different scales (as surrogates for habitat structure), were derived from Landsat7 satellite imagery. Using relatively few presence records, the maximum entropy (Maxent) approach and AUC were used to assess the performance of candidate predictor variables, for studying the effect of scale, model selection and mapping suitable habitat.3. This study demonstrated the utility of satellite imagery as a single source of predictor variables for a Maxent habitat suitability model at the landscape level, within a restricted geographical extent and with a fine grain, in a case where predictor variables typically used at the macro-scale level (e.g. climatic and topographic) were not applicable.4. In the case of H. alaotrensis, the methodology generated a habitat suitability map to inform conservation management in Lake Alaotra and a replicable protocol to allow rapid updates to habitat suitability maps in the future. The exploration of candidate predictor variables allowed the identification of scales that appear ecologically relevant for the species.5. Synthesis and applications. This study presents a cost-effective combination of maximum entropy habitat suitability modelling and satellite imagery, where all predictor variables are derived solely from Landsat7 images. With a habitat modelling method like Maxent that shows good performance with few presence samples and Landsat images now freely available, the methodology can play an important role in rapid assessments of the status of species at the landscape level in data-poor regions, when typical macro-scale environmental predictors are of little use or difficult to obtain.

Frequency distribution of TATA box and extension sequences on human promoters

TATA box is one of the most important transcription factor binding sites. But the exact sequences of TATA box are still not very clear yet. In this study, we conducted a dedicated analysis on the frequency distribution of TATA Box and its extension sequences on human promoters. Sixteen TATA elements derived from TATA Box motif, TATAWAWN, were classified into three distribution patterns: peak, bottom-peak and bottom. Fourteen TATA extension sequences (up to two base extensions) were predicted to be the new TATA Box elements because of their high motif factors, which indicate their statistical significance. Statistical analysis on the promoters of mouse, zebrafish and drosophila melanogaster verified seven of these elements. It was also observed that the distribution of TATA elements on the promoters of housekeeping genes are very similar with their distribution on the promoters of tissue specific genes in human.

Large-herbivore distribution and abundance : intra-and interspecific niche variation in the tropics

Determining the biological and environmental factors that limit the distribution and abundance of organisms is central to our understanding of the niche concept and crucial for predicting how species may respond to large-scale environmental change, such as global warming. However, detailed ecological information for the majority of species has been collected only at a local scale, and insufficient consideration has been given to geographical variation in intraspecific niche requirements. To evaluate the influence of environmental and biological factors on patterns of species distribution and abundance, we conducted a detailed, broadscale study across the tropical savannas of northern Australia on the ecology of three large, sympatric marsupial herbivores (family Macropodidae): the antilopine wallaroo (Macropus antilopinus), common wallaroo (M. robustus), and eastern grey kangaroo (M. giganteus). Using information on species abundance, climate, fire history, habitat, and resource availability, we constructed species' habitat models varying from the level of the complete distribution to smaller regional areas. Multiple factors affected macropod abundance, and the importance of these factors was dependent on the spatial scale of analyses. Fire regimes, water availability, geology, and soil type and climate were most important at the large scale, whereas aspects of habitat structure and interspecific species abundance were important at smaller scales. The distribution and abundance of eastern grey kangaroos and common wallaroos were strongly influenced by climate. Our results suggest that interspecific competition between antilopine wallaroos and eastern grey kangaroos may occur. The antilopine wallaroo and eastern grey kangaroo (grazers) preferred more nutrient-rich soils than the common wallaroo (grazer/browser), which we relate to differences in feeding modes. The abundance of antilopine wallaroos was higher on sites that were burned, whereas the abundance of common wallaroos was higher on unburned sites. Future climate change predicted for Australia has the capacity to seriously affect the abundance and conservation of macropod species in tropical savannas. The results of our models suggest that, in particular, the effects of changing climatic conditions on fire regimes, habitat structure, and water availability may lead to species declines and marked changes in macropod communities.

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.