Dataset Information

Foraging habitat choice of White-tailed Tropicbirds revealed by fine-scale GPS tracking and remote sensing.

ABSTRACT: Background:The introduction of animal tracking technology has rapidly advanced our understanding of seabird foraging ecology. Tracking data is particularly powerful when combined with oceanographic information derived from satellite remote sensing, allowing insights into the functional mechanisms of marine ecosystems. While this framework has been used extensively over the last two decades, there are still vast ocean regions and many seabird species for which information is scarce, particularly in tropical oceans. Methods:In this study we tracked the movement at high GPS recording frequency of 15 White-tailed Tropicbirds (Phaethon lepturus) during chick-rearing from a colony in Fernando de Noronha (offshore of Northeast Brazil). Flight behaviours of travelling and searching for food were derived from GPS data and examined in relation to satellite-sensed oceanographic variables (sea surface temperature, turbidity and chlorophyll-a concentration). Results:White-tailed Tropicbirds showed marked preference for clear and warm sea surface waters, which are indicative of low primary productivity but are likely the best habitat for preying upon flying fish. Discussion:These findings are consistent with previous studies showing that foraging habitat choices of tropical seabirds may not be driven by primary productivity, as has been widely shown for non-tropical species.

SUBMITTER: Santos CD

PROVIDER: S-EPMC6339477 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Foraging habitat choice of White-tailed Tropicbirds revealed by fine-scale GPS tracking and remote sensing.

Santos Carlos D CD Campos Leila F A S LFAS Efe Márcio A MA

PeerJ 20190116

<h4>Background</h4>The introduction of animal tracking technology has rapidly advanced our understanding of seabird foraging ecology. Tracking data is particularly powerful when combined with oceanographic information derived from satellite remote sensing, allowing insights into the functional mechanisms of marine ecosystems. While this framework has been used extensively over the last two decades, there are still vast ocean regions and many seabird species for which information is scarce, parti ...[more]

PMID: 30671305

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Project description:AimMacroecological studies that require habitat suitability data for many species often derive this information from expert opinion. However, expert-based information is inherently subjective and thus prone to errors. The increasing availability of GPS tracking data offers opportunities to evaluate and supplement expert-based information with detailed empirical evidence. Here, we compared expert-based habitat suitability information from the International Union for Conservation of Nature (IUCN) with habitat suitability information derived from GPS-tracking data of 1,498 individuals from 49 mammal species.LocationWorldwide.Time period1998-2021.Major taxa studiedForty-nine terrestrial mammal species.MethodsUsing GPS data, we estimated two measures of habitat suitability for each individual animal: proportional habitat use (proportion of GPS locations within a habitat type), and selection ratio (habitat use relative to its availability). For each individual we then evaluated whether the GPS-based habitat suitability measures were in agreement with the IUCN data. To that end, we calculated the probability that the ranking of empirical habitat suitability measures was in agreement with IUCN's classification into suitable, marginal and unsuitable habitat types.ResultsIUCN habitat suitability data were in accordance with the GPS data (> 95% probability of agreement) for 33 out of 49 species based on proportional habitat use estimates and for 25 out of 49 species based on selection ratios. In addition, 37 and 34 species had a > 50% probability of agreement based on proportional habitat use and selection ratios, respectively.Main conclusionsWe show how GPS-tracking data can be used to evaluate IUCN habitat suitability data. Our findings indicate that for the majority of species included in this study, it is appropriate to use IUCN habitat suitability data in macroecological studies. Furthermore, we show that GPS-tracking data can be used to identify and prioritize species and habitat types for re-evaluation of IUCN habitat suitability data.

Project description:The increasing spatiotemporal accuracy of Global Navigation Satellite Systems (GNSS) tracking systems opens the possibility to infer animal behaviour from tracking data. We studied the relationship between high-frequency GNSS data and behaviour, aimed at developing an easily interpretable classification method to infer behaviour from location data. Behavioural observations were carried out during tracking of cows (Bos Taurus) fitted with high-frequency GPS (Global Positioning System) receivers. Data were obtained in an open field and forested area, and movement metrics were calculated for 1 min, 12 s and 2 s intervals. We observed four behaviour types (Foraging, Lying, Standing and Walking). We subsequently used Classification and Regression Trees to classify the simultaneously obtained GPS data as these behaviour types, based on distances and turning angles between fixes. GPS data with a 1 min interval from the open field was classified correctly for more than 70% of the samples. Data from the 12 s and 2 s interval could not be classified successfully, emphasizing that the interval should be long enough for the behaviour to be defined by its characteristic movement metrics. Data obtained in the forested area were classified with a lower accuracy (57%) than the data from the open field, due to a larger positional error of GPS locations and differences in behavioural performance influenced by the habitat type. This demonstrates the importance of understanding the relationship between behaviour and movement metrics, derived from GNSS fixes at different frequencies and in different habitats, in order to successfully infer behaviour. When spatially accurate location data can be obtained, behaviour can be inferred from high-frequency GNSS fixes by calculating simple movement metrics and using easily interpretable decision trees. This allows for the combined study of animal behaviour and habitat use based on location data, and might make it possible to detect deviations in behaviour at the individual level.

Project description:As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071-2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios.

Dataset Information

Foraging habitat choice of White-tailed Tropicbirds revealed by fine-scale GPS tracking and remote sensing.

Publications

Foraging habitat choice of White-tailed Tropicbirds revealed by fine-scale GPS tracking and remote sensing.

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