Predicting Cetacean Habitats from Their Energetic Needs and the Distribution of Their Prey in Two Contrasted Tropical Regions

Lambert, Charlotte; Mannocci, Laura; Lehodey, Patrick; Ridoux, Vincent

doi:10.1371/journal.pone.0105958

Cited by 56 publications

(47 citation statements)

References 67 publications

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“…In the future, habitat-based models of cetacean distribution may be improved if modeled ocean data can produce reliable estimates for additional biological variables (e.g., phytoplankton, zooplankton), which are more closely linked to cetacean prey [92,93].…”

Section: Discussionmentioning

confidence: 99%

Moving Towards Dynamic Ocean Management: How Well Do Modeled Ocean Products Predict Species Distributions?

et al. 2016

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Species distribution models are now widely used in conservation and management to predict suitable habitat for protected marine species. The primary sources of dynamic habitat data have been in situ and remotely sensed oceanic variables (both are considered "measured data"), but now ocean models can provide historical estimates and forecast predictions of relevant habitat variables such as temperature, salinity, and mixed layer depth. To assess the performance of modeled ocean data in species distribution models, we present a case study for cetaceans that compares models based on output from a data assimilative implementation of the Regional Ocean Modeling System (ROMS) to those based on measured data. Specifically, we used seven years of cetacean line-transect survey data collected between 1991 and 2009 to develop predictive habitat-based models of cetacean density for 11 species in the California Current Ecosystem. Two different generalized additive models were compared: one built with a full suite of ROMS output and another built with a full suite of measured data. Model performance was assessed using the percentage of explained deviance, root mean squared error (RMSE), observed to predicted density ratios, and visual inspection of predicted and observed distributions. Predicted distribution patterns were similar for models using ROMS output and measured data, and showed good concordance between observed sightings and model predictions. Quantitative measures of predictive ability were also similar between model types, and RMSE values were almost identical. The overall demonstrated success of the ROMS-based models opens new opportunities for dynamic species management and biodiversity monitoring because ROMS output is available in near real time and can be forecast.

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Section: Discussionmentioning

confidence: 99%

Moving Towards Dynamic Ocean Management: How Well Do Modeled Ocean Products Predict Species Distributions?

et al. 2016

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“…Reilly (1990) suggested that seasonal dolphin abundance in the Eastern Tropical Pacific was more likely influenced by the ease of capture of prey than prey abundance. Likewise, other studies support the theory that prey patch dynamics, or the catchability of prey, are more important in determining predator distribution than biomass of the prey, particularly for relatively shallow diving cetaceans such as delphinids (Lambert et al, 2014). A more holistic approach to assessing a potential prey field needs to include relative abundance, biomass, energy content, as well as the costs associated with hunting, capturing, and handling each prey species.…”

Section: Distribution Of Predator and Preymentioning

confidence: 99%

“…In turn, predators are expected to behave according to the optimal foraging theory, which states that a predator maximizes energy intake by balancing the energy expended in searching and capturing prey with the energy gained from metabolizing that food (van Baalen et al, 2001;Spitz et al, 2010a). The distribution patterns (Barros and Wells, 1998;Lambert et al, 2014), group size, and social structure of social predators are thus influenced by the distribution and composition of prey (O'Donoghue et al, 1998;Meynier et al, 2008;Foster et al, 2012). However, studying the dynamics of apex predators is challenging due to their wide distribution, low densities, and ability to evade detection, particularly in the marine environment.…”

Section: Introductionmentioning

confidence: 99%

Dolphin Prey Availability and Calorific Value in an Estuarine and Coastal Environment

2016

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Prey density has long been associated with prey profitability for a predator, but prey quality has seldom been quantified. We assessed the potential prey availability and calorific value for Indo-Pacific bottlenose dolphins (Tursiops aduncus) in an estuarine and coastal environment of temperate south-western Australia. Fish were sampled using three methods (21.5 m beach seine, multi-mesh gillnet, and fish traps), across three regions (Estuary, Bay, and Ocean) in the study area. The total biomass and numbers of all species and those of potential dolphin prey were determined in austral summers and winters between 2007 and 2010. The calorific value of 19 species was determined by bomb calorimetry. The aim of the research was to evaluate the significance of prey availability in explaining the higher abundance of dolphins in the region in summer vs. winter across years. A higher abundance of prey was captured in the summer (mean of two summer seasons 12,080 ± 160) than in the winter (mean of two winter seasons = 7358 ± 343) using the same number of gear sets in each season and year. In contrast, higher biomass and higher energy rich prey were captured during winters than during summers, when fewer dolphins are present in the area. Variability was significant between season and region for the gillnet (p < 0.01), and seine (p < 0.01). The interaction of season and region was also significant for the calorific content captured by the traps (p < 0.03), and between the seasons for biomass of the trap catch (p < 0.02). The dolphin mother and calf pairs that remain in the Estuary and Bay year round may be sustained by the higher quality, and generally larger, if lesser abundant, prey in the winter months. Furthermore, factors such as predator avoidance and mating opportunities are likely to influence patterns of local dolphin abundance. This study provides insights into the complex dynamics of predator-prey interactions, and highlights the importance for a better understanding of prey abundance, distribution and calorific content in explaining the spatial ecology of large apex predators.

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“…Studies of predator-prey relationships on spatial and temporal scales finer than the present study may benefit from concurrent observations of both predator and prey organisms, although Torres et al (2008) showed that predictions of bottlenose dolphin distributions in a heterogeneous coastal habitat can be made without relying on prey data as explanatory variables. On larger scales, prey data from ocean ecosystem models promises to be useful in future cetacean prediction models (Lambert et al, 2014).…”

Section: Do the Models Make Ecological Sense?mentioning

confidence: 99%

Prediction of Large Whale Distributions: A Comparison of Presence–Absence and Presence-Only Modeling Techniques

et al. 2018

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Species distribution models that predict species occurrence or density by quantifying relationships with environmental variables are used for a variety of scientific investigations and management applications. For endangered species, such as large whales, models help to understand the ecological factors influencing variability in distributions and to assess potential risk from shipping, fishing, and other human activities. Systematic surveys record species presence and absence, as well as the associated search effort, but are very expensive. Presence-only data consisting only of sightings can increase sample size, but may be biased in both geographical and niche space. We built generalized additive models (GAMs) using presence-absence sightings data and maximum entropy models (Maxent) using the same presence-absence sightings data, and also using presence-only sightings data, for four large whale species in the eastern tropical Pacific Ocean: humpback (Megaptera novaeangliae), blue (Balaenoptera musculus), Bryde's (Balaenoptera edeni), and sperm whales (Physeter macrocephalus). Environmental variables were surface temperature, surface salinity, thermocline depth, stratification index, and seafloor depth. We compared predicted distributions from each of the two model types. Maxent and GAM model predictions based on systematic survey data are very similar, when Maxent absences are selected from the survey trackline data. However, we show that spatial bias in presence-only Maxent predictions can be caused by using pseudo-absences instead of observed absences and by the sampling biases of both opportunistic data and stratified systematic survey data with uneven coverage between strata. Predictions of uncommon large whale distributions from Maxent or other presence-only techniques may be useful for science or management, but only if spatial bias in the observations is addressed in the derivation and interpretation of model predictions.

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Predicting Cetacean Habitats from Their Energetic Needs and the Distribution of Their Prey in Two Contrasted Tropical Regions

Cited by 56 publications

References 67 publications

Moving Towards Dynamic Ocean Management: How Well Do Modeled Ocean Products Predict Species Distributions?

Moving Towards Dynamic Ocean Management: How Well Do Modeled Ocean Products Predict Species Distributions?

Dolphin Prey Availability and Calorific Value in an Estuarine and Coastal Environment

Prediction of Large Whale Distributions: A Comparison of Presence–Absence and Presence-Only Modeling Techniques

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