Using individual tracking data to validate the predictions of species distribution models

Combining ranger records and biogeographical models to identify the current and potential distribution of an expanding mesocarnivore in southern Europe

Human-wildlife conflicts (HWC) are increasing and are potentially harmful to both people and wildlife. Understanding the current and potential distribution of wildlife species involved in HWC, such as carnivores, is essential for implementing management and conservation measures for such species. In this study, we assessed both the current distribution and potential distribution (forecast) of the Egyptian mongoose (Herpestes ichneumon) in the central part of the Iberian Peninsula. We acquired data concerning mongoose occurrences through an online questionnaire sent to environmental rangers. We used the municipality level as the sampling unit because all municipalities within the study area were covered at least by one ranger. Using the information provided by rangers (i.e. occurrences in their municipalities), we constructed environmental favourability distribution models to assess current and potential mongoose distribution through current distribution models (CDM) and ecological models (EM), respectively. >300 rangers participated in the survey and mongooses were reported in a total of 181 of 921 municipalities studied. The CDM model showed a current distribution mainly concentrated on the western part of the study area, where intermediate-high favourability values predominated. The EM model revealed a wider potential distribution, including the south-east part of the study area, which was also characterised by intermediate-high favourability values. Our predictions were verified using independent data, including confirmation of mongoose reproduction by rangers, reports by other experts, and field sampling in some areas. Our innovative approach based on an online survey to rangers coupled with environmental favourability models is shown to be a useful methodology for assessing the current distribution of cryptic but expanding wildlife species, while also enabling estimations of future steps in their expansion. The approach proposed may help policy decision-makers seeking to ensure the conservation of expanding wildlife species, for example, by designing awareness campaigns in areas where the target species is expected to arrive.

A holistic and comprehensive data approach validates the distribution of the critically endangered flapper skate (Dipturus intermedius)

Morphological similarities between skates of the genus Dipturus in the north-eastern Atlantic and Mediterranean have resulted in longstanding confusion, misidentification and misreporting. Current evidence indicates that the common skate is best explained as two species, the flapper skate (Dipturus intermedius) and the common blue skate (D. batis). However, some management and conservation initiatives developed prior to the separation continue to refer to common skate (as 'D. batis'). This taxonomic uncertainty can lead to errors in estimating population viability, distribution range, and impact on fisheries management and conservation status. Here, we demonstrate how a concerted taxonomic approach, using molecular data and a combination of survey, angler and fisheries data, in addition to expert witness statements, can be used to build a higher resolution picture of the current distribution of D. intermedius. Collated data indicate that flapper skate has a more constrained distribution compared to the perceived distribution of the 'common skate', with most observations recorded from Norway and the western and northern seaboards of Ireland and Scotland, with occasional specimens from Portugal and the Azores. Overall, the revised spatial distribution of D. intermedius has significantly reduced the extant range of the species, indicating a possibly fragmented distribution range.

Ecoregional and temporal dynamics of dugong habitat use in a complex coral reef lagoon ecosystem

Mobile marine species display complex and nonstationary habitat use patterns that require understanding to design effective management measures. In this study, the spatio-temporal habitat use dynamics of the vulnerable dugong (Dugong dugon) were modelled from 16 satellite-tagged individuals in the coral reef lagoonal ecosystems of New Caledonia, South Pacific. Dugong residence time was calculated along the interpolated tracks (9371 hourly positions) to estimate intensity of use in three contrasting ecoregions, previously identified through hierarchical clustering of lagoon topographic characteristics. Across ecoregions, differences were identified in dugong spatial intensity of use of shallow waters, deeper lagoon waters and the fore-reef shelf outside the barrier reef. Maps of dugong intensity of use were predicted from these ecological relationships and validated with spatial density estimates derived from aerial surveys conducted for population assessment. While high correlation was found between the two datasets, our study extended the spatial patterns of dugong distribution obtained from aerial surveys across the diel cycle, especially in shallow waters preferentially used by dugongs at night/dusk during high tide. This study has important implications for dugong conservation and illustrates the potential benefits of satellite tracking and dynamic habitat use modelling to inform spatial management of elusive and mobile marine mammals.

Combining modern tracking data and historical records improves understanding of the summer habitats of the Eastern Lesser White-fronted Goose Anser erythropus

The Lesser White-fronted Goose (Anser erythropus), smallest of the "gray" geese, is listed as Vulnerable on the IUCN Red List and protected in all range states. There are three populations, with the least studied being the Eastern population, shared between Russia and China. The extreme remoteness of breeding enclaves makes them largely inaccessible to researchers. As a substitute for visitation, remotely tracking birds from wintering grounds allows exploration of their summer range. Over a period of three years, and using highly accurate GPS tracking devices, eleven individuals of A. erythropus were tracked from the key wintering site of China, to summering, and staging sites in northeastern Russia. Data obtained from that tracking, bolstered by ground survey and literature records, were used to model the summer distribution of A. erythropus. Although earlier literature describes a patchy summer range, the model suggests a contiguous summer habitat range is possible, although observations to date cannot confirm A. erythropus is present throughout the modeled range. The most suitable habitats are located along the coasts of the Laptev Sea, primarily the Lena Delta, in the Yana-Kolyma Lowland, and smaller lowlands of Chukotka with narrow riparian extensions upstream along major rivers such as the Lena, Indigirka, and Kolyma. The probability of A. erythropus presence is related to areas with altitude less than 500 m with abundant wetlands, especially riparian habitat, and a climate with precipitation of the warmest quarter around 55 mm and mean temperature around 14°C during June-August. Human disturbance also affects site suitability, with a gradual decrease in species presence starting around 160 km from human settlements. Remote tracking of animal species can bridge the knowledge gap required for robust estimation of species distribution patterns in remote areas. Better knowledge of species' distribution is important in understanding the large-scale ecological consequences of rapid global change and establishing conservation management strategies.

Validation of presence-only models for conservation planning and the application to whales in a multiple-use marine park

Identification of species' Biologically Important Areas (BIAs) is fundamental to conservation planning and species distribution models (SDMs) are a powerful tool commonly used to do this. Presence-only data are increasingly being used to develop SDMs to aid the conservation decision-making process. The application of presence-only SDMs for marine species' is particularly attractive due to often logistical and economic costs of obtaining systematic species' distribution data. However, robust model validation is important for conservation management applications that require accurate and reliable species' occurrence data (e.g., spatially explicit risk assessments). This is commonly done using a random subset of the data and less commonly with fully independent test data. Here, we apply a spatial block cross-validation (CV) approach to validate a MaxEnt presence-only model using independent presence/absence survey data for a highly mobile, marine species (humpback whale, Megaptera novaengliae) in the Great Barrier Reef (GBR). A MaxEnt model was developed using opportunistic whale sightings (2003-2007) and then used to identify areas differing in habitat suitability (low, medium, high) to conduct a systematic, line-transect, aerial survey (2012) and derive a density surface model. A spatial block CV buffering strategy was used to validate the MaxEnt model, using the opportunistic sightings as training data and independent aerial survey sightings data as test data. Moderate performance measures indicate MaxEnt was reliable in identifying the distribution patterns of a mobile whale species on their breeding ground, indicated by areas of high density aligned to areas of high habitat suitability. Furthermore, we demonstrate that MaxEnt models can be useful and cost-effective for designing a sampling scheme to undertake systematic surveys that significantly reduces sampling effort. In this study, higher quality information on whale reproductive class (calf vs. non-calf groups) was obtained that the presence-only data lacked, while sampling only 18% of the GBR World Heritage Area. The validation approach using fully independent data provides greater confidence in the MaxEnt model, which indicates significant overlap with the main breeding ground of humpback whales and the inner shipping route. This is important when evaluating presence-only models within certain conservation management applications, such as spatial risk assessments.

The MIAmaxent R package: Variable transformation and model selection for species distribution models

The widely used "Maxent" software for modeling species distributions from presence-only data (Phillips et al., Ecological Modelling, 190, 2006, 231) tends to produce models with high-predictive performance but low-ecological interpretability, and implications of Maxent's statistical approach to variable transformation, model fitting, and model selection remain underappreciated. In particular, Maxent's approach to model selection through lasso regularization has been shown to give less parsimonious distribution models-that is, models which are more complex but not necessarily predictively better-than subset selection. In this paper, we introduce the MIAmaxent R package, which provides a statistical approach to modeling species distributions similar to Maxent's, but with subset selection instead of lasso regularization. The simpler models typically produced by subset selection are ecologically more interpretable, and making distribution models more grounded in ecological theory is a fundamental motivation for using MIAmaxent. To that end, the package executes variable transformation based on expected occurrence-environment relationships and contains tools for exploring data and interrogating models in light of knowledge of the modeled system. Additionally, MIAmaxent implements two different kinds of model fitting: maximum entropy fitting for presence-only data and logistic regression (GLM) for presence-absence data. Unlike Maxent, MIAmaxent decouples variable transformation, model fitting, and model selection, which facilitates methodological comparisons and gives the modeler greater flexibility when choosing a statistical approach to a given distribution modeling problem.