Translating habitat class to land cover to map area of habitat of terrestrial vertebrates

Area of habitat (AOH) is defined as the “habitat available to a species, that is, habitat within its range” and is calculated by subtracting areas of unsuitable land cover and elevation from the range. The International Union for the Conservation of Nature (IUCN) Habitats Classification Scheme provides information on species habitat associations, and typically unvalidated expert opinion is used to match habitat to land-cover classes, which generates a source of uncertainty in AOH maps. We developed a data-driven method to translate IUCN habitat classes to land cover based on point locality data for 6986 species of terrestrial mammals, birds, amphibians, and reptiles. We extracted the land-cover class at each point locality and matched it to the IUCN habitat class or classes assigned to each species occurring there. Then, we modeled each land-cover class as a function of IUCN habitat with (SSG, using) logistic regression models. The resulting odds ratios were used to assess the strength of the association between each habitat and land-cover class. We then compared the performance of our data-driven model with those from a published translation table based on expert knowledge. We calculated the association between habitat classes and land-cover classes as a continuous variable, but to map AOH as binary presence or absence, it was necessary to apply a threshold of association. This threshold can be chosen by the user according to the required balance between omission and commission errors. Some habitats (e.g., forest and desert) were assigned to land-cover classes with more confidence than others (e.g., wetlands and artificial). The data-driven translation model and expert knowledge performed equally well, but the model provided greater standardization, objectivity, and repeatability. Furthermore, our approach allowed greater flexibility in the use of the results and uncertainty to be quantified. Our model can be modified for regional examinations and different taxonomic groups.     

Boosting freshwater fish conservation with high-resolution distribution mapping across a large territory

The lack of high-resolution distribution maps for freshwater species across large extents fundamentally challenges biodiversity conservation worldwide. We devised a simple framework to delineate the distributions of freshwater fishes in a high-resolution drainage map based on stacked species distribution models and expert information. We applied this framework to the entire Chinese freshwater fish fauna (>1600 species) to examine high-resolution biodiversity patterns and reveal potential conflicts between freshwater biodiversity and anthropogenic disturbances. The correlations between spatial patterns of biodiversity facets (species richness, endemicity, and phylogenetic diversity) were all significant (r = 0.43–0.98, p < 0.001). Areas with high values of different biodiversity facets overlapped with anthropogenic disturbances. Existing protected areas (PAs), covering 22% of China's territory, protected 25–29% of fish habitats, 16–23% of species, and 30–31% of priority conservation areas. Moreover, 6–21% of the species were completely unprotected. These results suggest the need for extending the network of PAs to ensure the conservation of China's freshwater fishes and the goods and services they provide. Specifically, middle to low reaches of large rivers and their associated lakes from northeast to southwest China hosted the most diverse species assemblages and thus should be the target of future expansions of the network of PAs. More generally, our framework, which can be used to draw high-resolution freshwater biodiversity maps combining species occurrence data and expert knowledge on species distribution, provides an efficient way to design PAs regardless of the ecosystem, taxonomic group, or region considered.     

A condition metric for Eucalyptus woodland derived from expert evaluations

The evaluation of ecosystem quality is important for land‐management and land‐use planning. Evaluation is unavoidably subjective, and robust metrics must be based on consensus and the structured use of observations. We devised a transparent and repeatable process for building and testing ecosystem metrics based on expert data. We gathered quantitative evaluation data on the quality of hypothetical grassy woodland sites from experts. We used these data to train a model (an ensemble of 30 bagged regression trees) capable of predicting the perceived quality of similar hypothetical woodlands based on a set of 13 site variables as inputs (e.g., cover of shrubs, richness of native forbs). These variables can be measured at any site and the model implemented in a spreadsheet as a metric of woodland quality. We also investigated the number of experts required to produce an opinion data set sufficient for the construction of a metric. The model produced evaluations similar to those provided by experts, as shown by assessing the model's quality scores of expert‐evaluated test sites not used to train the model. We applied the metric to 13 woodland conservation reserves and asked managers of these sites to independently evaluate their quality. To assess metric performance, we compared the model's evaluation of site quality with the managers’ evaluations through multidimensional scaling. The metric performed relatively well, plotting close to the center of the space defined by the evaluators. Given the method provides data‐driven consensus and repeatability, which no single human evaluator can provide, we suggest it is a valuable tool for evaluating ecosystem quality in real‐world contexts. We believe our approach is applicable to any ecosystem.     

Spatial uncertainty analysis of population models

This paper describes an approach for conducting spatial uncertainty analysis of spatial population models, and illustrates the ecological consequences of spatial uncertainty for landscapes with different properties. Spatial population models typically simulate birth, death, and migration on an input map that describes habitat. Typically, only a single “reference” map is available, but we can imagine that a collection of other, slightly different, maps could be drawn to represent a particular species’ habitat. As a first approximation, our approach assumes that spatial uncertainty (i.e., the variation among values assigned to a location by such a collection of maps) is constrained by characteristics of the reference map, regardless of how the map was produced. Our approach produces lower levels of uncertainty than alternative methods used in landscape ecology because we condition our alternative landscapes on local properties of the reference map. Simulated spatial uncertainty was higher near the borders of patches. Consequently, average uncertainty was highest for reference maps with equal proportions of suitable and unsuitable habitat, and no spatial autocorrelation. We used two population viability models to evaluate the ecological consequences of spatial uncertainty for landscapes with different properties. Spatial uncertainty produced larger variation among predictions of a spatially explicit model than those of a spatially implicit model. Spatially explicit model predictions of final female population size varied most among landscapes with enough clustered habitat to allow persistence. In contrast, predictions of population growth rate varied most among landscapes with only enough clustered habitat to support a small population, i.e., near a spatially mediated extinction threshold. We conclude that spatial uncertainty has the greatest effect on persistence when the amount and arrangement of suitable habitat are such that habitat capacity is near the minimum required for persistence.     

Use of Habitats as Surrogates of Biodiversity for Efficient Coral Reef Conservation Planning in Pacific Ocean Islands

Abstract: Marine protected areas (MPAs) have been highlighted as a means toward effective conservation of coral reefs. New strategies are required to more effectively select MPA locations and increase the pace of their implementation. Many criteria exist to design MPA networks, but generally, it is recommended that networks conserve a diversity of species selected for, among other attributes, their representativeness, rarity, or endemicity. Because knowledge of species’ spatial distribution remains scarce, efficient surrogates are urgently needed. We used five different levels of habitat maps and six spatial scales of analysis to identify under which circumstances habitat data used to design MPA networks for Wallis Island provided better representation of species than random choice alone. Protected‐area site selections were derived from a rarity–complementarity algorithm. Habitat surrogacy was tested for commercial fish species, all fish species, commercially harvested invertebrates, corals, and algae species. Efficiency of habitat surrogacy varied by species group, type of habitat map, and spatial scale of analysis. Maps with the highest habitat thematic complexity provided better surrogates than simpler maps and were more robust to changes in spatial scales. Surrogates were most efficient for commercial fishes, corals, and algae but not for commercial invertebrates. Conversely, other measurements of species‐habitat associations, such as richness congruence and composition similarities provided weak results. We provide, in part, a habitat‐mapping methodology for designation of MPAs for Pacific Ocean islands that are characterized by habitat zonations similar to Wallis. Given the increasing availability and affordability of space‐borne imagery to map habitats, our approach could appreciably facilitate and improve current approaches to coral reef conservation and enhance MPA implementation.     

Use of opportunistic sightings and expert knowledge to predict and compare Whooping Crane stopover habitat

Predicting a species’ distribution can be helpful for evaluating management actions such as critical habitat designations under the U.S. Endangered Species Act or habitat acquisition and rehabilitation. Whooping Cranes (Grus americana) are one of the rarest birds in the world, and conservation and management of habitat is required to ensure their survival. We developed a species distribution model (SDM) that could be used to inform habitat management actions for Whooping Cranes within the state of Nebraska (U.S.A.). We collated 407 opportunistic Whooping Crane group records reported from 1988 to 2012. Most records of Whooping Cranes were contributed by the public; therefore, developing an SDM that accounted for sampling bias was essential because observations at some migration stopover locations may be under represented. An auxiliary data set, required to explore the influence of sampling bias, was derived with expert elicitation. Using our SDM, we compared an intensively managed area in the Central Platte River Valley with the Niobrara National Scenic River in northern Nebraska. Our results suggest, during the peak of migration, Whooping Crane abundance was 262.2 (90% CI 40.2?3144.2) times higher per unit area in the Central Platte River Valley relative to the Niobrara National Scenic River. Although we compared only 2 areas, our model could be used to evaluate any region within the state of Nebraska. Furthermore, our expert‐informed modeling approach could be applied to opportunistic presence‐only data when sampling bias is a concern and expert knowledge is available.     

Using expert knowledge to support Endangered Species Act decision-making for data-deficient species

Many questions relevant to conservation decision-making are characterized by extreme uncertainty due to lack of empirical data and complexity of the underlying ecologic processes, leading to a rapid increase in the use of structured protocols to elicit expert knowledge. Published ecologic applications often employ a modified Delphi method, where experts provide judgments anonymously and mathematical aggregation techniques are used to combine judgments. The Sheffield elicitation framework (SHELF) differs in its behavioral approach to synthesizing individual judgments into a fully specified probability distribution for an unknown quantity. We used the SHELF protocol remotely to assess extinction risk of three subterranean aquatic species that are being considered for listing under the U.S. Endangered Species Act. We provided experts an empirical threat assessment for each known locality over a video conference and recorded judgments on the probability of population persistence over four generations with online submission forms and R-shiny apps available through the SHELF package. Despite large uncertainty for all populations, there were key differences between species’ risk of extirpation based on spatial variation in dominant threats, local land use and management practices, and species’ microhabitat. The resulting probability distributions provided decision makers with a full picture of uncertainty that was consistent with the probabilistic nature of risk assessments. Discussion among experts during SHELF's behavioral aggregation stage clearly documented dominant threats (e.g., development, timber harvest, animal agriculture, and cave visitation) and their interactions with local cave geology and species’ habitat. Our virtual implementation of the SHELF protocol demonstrated the flexibility of the approach for conservation applications operating on budgets and time lines that can limit in-person meetings of geographically dispersed experts.     

Understanding the drivers of expert opinion when classifying species as extinct

The criteria as laid out by the International Union for the Conservation of Nature (IUCN) Red List are the gold standard by which the extinction risk of a species is assessed and where appropriate biological extinctions are declared. However, unlike all other categories, the category of extinct lacks a quantitative framework for assigning this category. Given its subjective nature, we surveyed expert assessors working on a diversity of taxa to explore the attributes they used to declare a species extinct. Using a choice experiment approach, we surveyed 674 experts from the IUCN Species Survival Commission specialist groups and taskforces. Data availability, time from the last sighting, detectability, habitat availability, and population decline were all important attributes favored by assessors when inferring extinction. Respondents with red-listing experience assigned more importance to the attributes data availability, time from the last sighting, and detectability when considering a species extinction, whereas those respondents working with well-known taxa gave more importance to the time from the last sighting. Respondents with no red-listing experience and those working with more well-known taxa (i.e., mammals and birds) were overall less likely to consider species extinct. Our findings on the importance assessors place on attributes used to declare a species extinct provide a basis for informing the development of specific criteria for more accurately assessing species extinctions.     

Predicting and Mapping Potential Whooping Crane Stopover Habitat to Guide Site Selection for Wind Energy Projects

Migratory stopover habitats are often not part of planning for conservation or new development projects. We identified potential stopover habitats within an avian migratory flyway and demonstrated how this information can guide the site‐selection process for new development. We used the random forests modeling approach to map the distribution of predicted stopover habitat for the Whooping Crane (Grus americana), an endangered species whose migratory flyway overlaps with an area where wind energy development is expected to become increasingly important. We then used this information to identify areas for potential wind power development in a U.S. state within the flyway (Nebraska) that minimize conflicts between Whooping Crane stopover habitat and the development of clean, renewable energy sources. Up to 54% of our study area was predicted to be unsuitable as Whooping Crane stopover habitat and could be considered relatively low risk for conflicts between Whooping Cranes and wind energy development. We suggest that this type of analysis be incorporated into the habitat conservation planning process in areas where incidental take permits are being considered for Whooping Cranes or other species of concern. Field surveys should always be conducted prior to construction to verify model predictions and understand baseline conditions. Predicción y Mapeo del Hábitat Potencial de Descanso de la Grulla Americana para Guiar la Selección de Sitios para Proyectos de Energía Eólica.     

Modeling population effects of the Deepwater Horizon oil spill on a long-lived species

The 2010 Deepwater Horizon (DWH) oil spill exposed common bottlenose dolphins (Tursiops truncatus) in Barataria Bay, Louisiana to heavy oiling that caused increased mortality and chronic disease and impaired reproduction in surviving dolphins. We conducted photographic surveys and veterinary assessments in the decade following the spill. We assigned a prognostic score (good, fair, guarded, poor, or grave) for each dolphin to provide a single integrated indicator of overall health, and we examined temporal trends in prognostic scores. We used expert elicitation to quantify the implications of trends for the proportion of the dolphins that would recover within their lifetime. We integrated expert elicitation, along with other new information, in a population dynamics model to predict the effects of observed health trends on demography. We compared the resulting population trajectory with that predicted under baseline (no spill) conditions. Disease conditions persisted and have recently worsened in dolphins that were presumably exposed to DWH oil: 78% of those assessed in 2018 had a guarded, poor, or grave prognosis. Dolphins born after the spill were in better health. We estimated that the population declined by 45% (95% CI 14–74) relative to baseline and will take 35 years (95% CI 18–67) to recover to 95% of baseline numbers. The sum of annual differences between baseline and injured population sizes (i.e., the lost cetacean years) was 30,993 (95% CI 6607–94,148). The population is currently at a minimum point in its recovery trajectory and is vulnerable to emerging threats, including planned ecosystem restoration efforts that are likely to be detrimental to the dolphins’ survival. Our modeling framework demonstrates an approach for integrating different sources and types of data, highlights the utility of expert elicitation for indeterminable input parameters, and emphasizes the importance of considering and monitoring long-term health of long-lived species subject to environmental disasters. Article impact statement: Oil spills can have long-term consequences for the health of long-lived species; thus, effective restoration and monitoring are needed.     

Predicting the distributions of marine organisms at the global scale

We present and evaluate AquaMaps, a presence-only species distribution modelling system that allows the incorporation of expert knowledge about habitat usage and was designed for maximum output of standardized species range maps at the global scale. In the marine environment there is a significant challenge to the production of range maps due to large biases in the amount and location of occurrence data for most species. AquaMaps is compared with traditional presence-only species distribution modelling methods to determine the quality of outputs under equivalently automated conditions. The effect of the inclusion of expert knowledge to AquaMaps is also investigated. Model outputs were tested internally, through data partitioning, and externally against independent survey data to determine the ability of models to predict presence versus absence. Models were also tested externally by assessing correlation with independent survey estimates of relative species abundance. AquaMaps outputs compare well to the existing methods tested, and inclusion of expert knowledge results in a general improvement in model outputs. The transparency, speed and adaptability of the AquaMaps system, as well as the existing online framework which allows expert review to compensate for sampling biases and thus improve model predictions are proposed as additional benefits for public and research use alike.     

Rank aggregation of local expert knowledge for conservation planning of the critically endangered saola

There has been much recent interest in using local knowledge and expert opinion for conservation planning, particularly for hard‐to‐detect species. Although it is possible to ask for direct estimation of quantities such as population size, relative abundance is easier to estimate. However, an expert's knowledge is often geographically restricted relative to the area of interest. Combining (or aggregating) experts’ assessments of relative abundance is difficult when each expert only knows a part of the area of interest. We used Google's PageRank algorithm to aggregate ranked abundance scores elicited from local experts through a rapid rural‐appraisal method. We applied this technique to conservation planning for the saola (Pseudoryx nghetinhensis), a poorly known bovid. Near a priority landscape for the species, composed of 3 contiguous protected areas, we asked groups of local people to indicate relative abundances of saola and other species by placing beans on community maps. For each village, we used this information to rank areas within the knowledge area of that village for saola abundance. We used simulations to compare alternative methods to aggregate the rankings from the different villages. The best‐performing method was then used to produce a single map of relative abundance across the entire landscape, an area larger than that known to any one village. This map has informed prioritization of surveys and conservation action in the continued absence of direct information about the saola.     

Ecological niche modeling of customary medicinal plant species used by Australian Aborigines to identify species-rich and culturally valuable areas for conservation

Customary medicinal plant species used by Australian Aborigines are disappearing rapidly with its associated knowledge, due to the loss of habitats. Conservation and protection of these species is important as they represent sources of novel therapeutic phytochemical compounds and are culturally valuable. Information on the spatial distribution and use of customary medicinal plants is often inadequate and fragmented, posing limitations on the identification and conservation of species-rich areas and culturally valuable habitats.In this study, the habitat suitability modeling program, MaxEnt, was used to predict the potential ecological niches of 431 customary medicinal plant species, based on bioclimatic variables. Specimen locality records were obtained from the Global Biodiversity Information Facility (GBIF) data portal and from Australia's Virtual Herbarium (AVH).Ecological niche models of 414 predicted species, which had 30 or more occurrence points, were used to produce maps indicating areas that were ecologically suitable for multiple species (concordance of high predicted ecological suitability) and having cultural values. For the concordance map, individual species niche models were thresholded and summed. To derive a map of culturally valuable areas, customary medicinal uses from Customary Medicinal Knowledgebase (CMKb) (www.biolinfo.org/cmkb) were used to weight individual species models, resulting in a value within each grid cell reflecting its cultural worth.Even though the available information is scarce and fragmented, our approach provides an opportunity to infer areas predicted to be suitable for multiple species (i.e. concordance hotspots) and to estimate the cultural value of a particular geographical area. Our results also indicate that to conserve bio-cultural diversity, comprehensive information and active participation of Aboriginal communities is indispensable.     

Protection of Mammal Diversity in Central America

Abstract:  Central America is exceptionally rich in biodiversity, but varies widely in the attention its countries devote to conservation. Protected areas, widely considered the cornerstone of conservation, were not always created with the intent of conserving that biodiversity. We assessed how well the protected-area system of Central America includes the region's mammal diversity. This first required a refinement of existing range maps to reduce their extensive errors of commission (i.e., predicted presences in places where species do not occur). For this refinement, we used the ecological limits of each species to identify and remove unsuitable areas from the range. We then compared these maps with the locations of protected areas to measure the habitat protected for each of the region's 250 endemic mammals. The species most vulnerable to extinction—those with small ranges—were largely outside protected areas. Nevertheless, the most strictly protected areas tended toward areas with many small-ranged species. To improve the protection coverage of mammal diversity in the region, we identified a set of priority sites that would best complement the existing protected areas. Protecting these new sites would require a relatively small increase in the total area protected, but could greatly enhance mammal conservation.     

Are existing vegetation maps adequate to predict bird distributions?

Bird species are selective on the vegetation types in which they are found but predictive models of bird distribution based on variables derived from land-use/land-cover maps tend to have limited success. It has been suggested that accuracy of existing maps used to derive predictors is in part responsible for the limited success of bird distribution models. In two areas of 4900 km² of Western Andalusia, Spain, we compared the predictive ability of bird distribution models derived from two existing general-purpose land-use/land-cover maps, which differ in their resolution and accuracy: a coarse scale vegetation map of Europe, the CORINE land-cover map, and a detailed regional map, the 1995 land-use/land-cover map of Andalusia from the SINAMBA (Consejerı́a de Medio Ambiente, Junta de Andalucı́a). We compared the bird distribution models derived from these general-purpose vegetation maps with models derived from two more accurate structural vegetation maps built considering directly variables that influence bird habitat selection, one built from satellite images for this study and another obtained by improving the resolution and accuracy of the SINAMBA map with satellite data. We sampled the presence/absence of bird species at 857 points using 15-min point surveys. Predictive models for 54 bird species were built with generalised additive models (GAMs), using as potential predictors the same set of landscape and vegetation structure variables measured on each map. We compared for each bird species the predictive accuracy of the best model derived from each map. Vegetation structure measured at bird sample points was used as ground-truth for comparing the accuracy of vegetation maps. Although maps differed in their resolution and accuracy, the results show that all of them produced similarly accurate bird distribution models, with a mixed map produced with both thematic and satellite information being the best. The models derived from the more accurate vegetation structure maps obtained from satellite data were not more accurate than those derived directly from the SINAMBA or CORINE maps. Our results suggest that some general-purpose land-use/land-cover maps are accurate enough to derive bird distribution models. There is a certain limit to improve vegetation maps above which there is no effect in their power to predict bird distribution.     

The effects of shifting vegetation mosaics on habitat suitability for coastal dune fauna—a case study on sand lizards (Lacerta agilis)

Encroachment of tall grasses and shrubs in coastal dunes has resulted in loss of vegetation heterogeneity. This is expected to have negative effects on animal diversity. To counteract encroachment and develop structural heterogeneity grazing is a widely used management practice. Here, we aim to functionally interpret changes in vegetation composition and configuration following grazing management on habitat suitability for sand lizards. Aerial photographs taken over a period of 16 years were used to quantify changes in vegetation composition. A GIS-based method was developed to calculate habitat suitability for sand lizards in a spatially explicit manner, encompassing differences in vegetation structure and patch size. From 1987 to 2003 dune vegetation shifted from small patches of moss and sand to larger patches covered by shrubs and grasses. Grazing management did not have any significant effect on the overall level of heterogeneity, measured as habitat suitability for sand lizards. However, on a more local scale highly suitable patches in 1987 were deteriorating whereas unsuitable patches became more suitable in 2003. This inversion results from a broad shift with shrubs being a limiting habitat element in 1987 to sandy patches being the limiting element in 2003. Future changes are believed to negatively impact sand lizards. The habitat suitability model has proven to be a useful tool to functionally interpret changes in coastal dune vegetation heterogeneity from an animal’s perspective. Further research should aim to include multiple species operating on different scale levels to fully capture the natural landscape dynamics.     

Extinction, Colonization, and Metapopulations: Environmental Tracking by Rare Species

Extinction and metapopulation theories emphasize that stochastic fluctuations in local populations cause extinction and that local extinctions generate empty habitat patches that are then available for recolonization. Metapopulation persistence depends on the balance of extinction and colonization in a static environment. For many rare and declining species, I argue (1) that extinction is usually the deterministic consequence of the local environment becoming unsuitable (through habitat loss or modification, introduction of a predator, etc.); (2) that the local environment usually remains unsuitable following local extinction, so extinctions only rarely generate empty patches of suitable habitat; and (3) that colonization usually follows improvement of the local environment for a particular species (or long-distance transfer by humans). Thus, persistence depends predominantly on whether organisms are able to track the shifting spatial mosaic of suitable environmental conditions or on maintainance of good conditions locally.     

Dispersal Can Limit Local Plant Distribution

The ability of species to establish new populations at unoccupied sites is a critical feature in the maintenance of biological diversity, and it has taken on new importance as a result of global climate change and expected changes in species distribution. To examine the dispersal potential of plant species, seeds of four annual plant species were experimentally dispersed 40 to 600 m from existing populations in Massachusetts (U.S.A.) to 34 nearby unoccupied but apparently suitable sites. At three of these sites new populations were established that persisted for four generations and expanded slowly in area. At seven sites, a small initial population eventually died out. At the 24 other sites, new populations did not become established, indicating that the sites were in some way unsuitable, that not enough seeds arrived, or that conditions suitable for seed germination do not occur every year. These results suggest that some species may be unable to disperse naturally out of their existing ranges in response to global climate change, particularly if habitat fragmentation creates barriers to dispersal. These species may have to be assisted to reach suitable sites nearby to prevent their extinction in the wild.     

Predicting suitable habitat for the European lobster (Homarus gammarus), on the Basque continental shelf (Bay of Biscay), using Ecological-Niche Factor Analysis

Predicting species distribution and habitat suitability (HS) modelling, across broad spatial scales, is now a major challenge in marine ecology. The resulting knowledge is of considerable use in supporting the implementation of environmental legislation, integrated coastal zone management and ecosystem-based fisheries management. This contribution considers the identification of seafloor morphological characteristics, together with wave energy conditions, that determine the presence of European lobster (Homarus gammarus); and it predicts suitable habitats over the Basque continental shelf (Bay of Biscay), in summer. The results obtained, by applying Ecological-Niche Factor Analysis (ENFA), indicate that lobster habitat differs considerably from the mean environmental condition over the study area; likewise, that it is restrictive in terms of the range of conditions in which they dwell. The best of the environmental predictors found to be: distance to the rock substrate; Benthic Position Index; wave flux over the seafloor; and the underlying bathymetry. A habitat suitability map was produced, with a high model quality (Boyce index: 0.98 ± 0.06). The most suitable habitat for European lobster are locations at the boundary between sedimentary- and rocky-bottoms, coincident with seafloor depressions with a steep slope, with medium to high wave energy conditions, and located within a range of water depths of 35–40 m. This approach demonstrates the applicability of the method in case studies where only presence data are available, together with the inclusion of environmental variables obtained from different sources.     

Crustacean habitat potential mapping in a tidal flat using remote sensing and GIS

To make a macrofaunal (crustacean) habitat potential map, the spatial distribution of ecological variables in the Hwangdo tidal flat, Korea, was explored. Spatial variables were mapped using remote sensing and a geographic information system (GIS) combined with field observations. A frequency ratio (FR) and logistic regression (LR) model were employed to map the macrofauna potential area for the Ilyoplax dentimerosa, a crustacean species. Spatial variables affecting the tidal macrofauna distribution were selected based on abundance and biomass and used within a spatial database derived from remotely sensed data of various types of sensors. The spatial variables included the intertidal digital elevation model (DEM), slope, distance from a tidal channel, tidal channel density, surface sediment facies, spectral reflectance of the near infrared (NIR) bands and the tidal exposure duration. The relation between the I. dentimerosa and each spatial variable was calculated using the FR and LR. The species was randomly divided into a training set (70%) to analyse habitat potential using FR and LR and a test set (30%) to validate the predicted habitat potential map. The relations were overlaid to produce a habitat potential map with the species potential index (SPI) value for each pixel. The potential habitat maps were compared with the surveyed habitat locations such as validation data set. The comparison results showed that the LR model (accuracy is 85.28%) is better in prediction than the FR (accuracy is 78.96%) model. The performance of models gave satisfactory accuracies. The LR provides the quantitative influence of variables on a potential habitat of species; otherwise, the FR shows the quantitative influence of a class in each variable. The combination of a GIS-based frequency ratio and logistic regression models and remote sensing with field observations is an effective method to determine locations favorable for macrofaunal species occurrences in a tidal flat.