Habitat suitability of<scp><i>Rhincodon typus</i></scp>in three localities of the Gulf of California: Environmental drivers of seasonal aggregations

Petatán‐Ramírez, David; Whitehead, Darren A.; Guerrero-Izquierdo, Triana P.; Ojeda-Ruíz, Miguel Ángel; Becerril‐García, Edgar E.

doi:10.1111/jfb.14496

Cited by 13 publications

(9 citation statements)

References 39 publications

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“…The aggregation of essential prey species is also an important consideration for resource managers. White sharks, Carcharodon carcharias (Weng et al, 2007), whale sharks, Rhincodon typus (Petatán‐Ramírez et al, 2020), bonnetheads, Sphyrna tiburo (Driggers III et al, 2014) and many other species aggregate in response to elevated prey availability. For example, Fakarava, French Polynesia supports extremely high abundances of sharks, including endangered grey reef sharks, Carcharhinus amblyrhynchos (Mourier et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

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Comparison of two machine learning frameworks for predicting aggregatory behaviour of sharks

Wade

Fisher

Matich

2022

Journal of Applied Ecology

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Ecological monitoring is critical for conservation efforts, particularly in marine ecosystems. Recently, greater emphasis on analyses of data resultant from long‐term ecological research has allowed for broader questions pertaining to climate change, species range shifts and reproductive phenology to be assessed, which may improve ecosystem management. Data collected via ecological monitoring, however, often feature strong class imbalances, complicating the development of models to predict such events. Here, we propose two modelling frameworks—a boosted regression tree (BRT) model and an artificial neural network (ANN)—for predicting exceptionally rare aggregatory behaviour of bull, Carcharhinus leucas and blacktip sharks, C. limbatus along the Gulf coast of Texas. In tandem with aggressive techniques for handling zero‐inflated data, both methods produced accurate predictions of aggregations of three or more individuals in one survey event, with the BRT outperforming the ANN in minimizing type I error. Additionally, both models maintained relatively high area under the receiver operating characteristic (ROC AUC) values when the threshold for defining aggregative behaviour was raised, though there was a measurable drop‐off in the precision‐recall (PR) AUC at each successive threshold increase. These results provide support for both modelling approaches as highly viable in generating predictions based on monitoring data, even in situations where negative cases outweigh positive cases by more than 10‐fold. This is promising for conservation and management of species that exhibit biologically and ecologically significant, but rare, behaviours like aggregations, and species that are rare in abundance and thus vulnerable to future declines. More accurately predicting aggregation events provides the information necessary to improve the protection of species that gather during key life‐history events (e.g. mating, parturition, migration), and assess the spatiotemporal consistency of such events, thereby improving the efficacy of adaptive management. Synthesis and applications. Conservation and management programmes serve a critical role in maintaining the health of ecosystems, and regularly make use of ecological monitoring to collect data relevant to population studies. In marine systems, particularly, monitoring is often expensive and time‐consuming, which can result in incomplete or sparse datasets. Especially where an ecological event of interest is rare, subsequent analyses are particularly affected by the many limitations of zero‐inflated datasets. When training predictive models to classify these events, special care must be taken to avoid simply predicting negative events in every case. Here, we propose and compare two machine learning approaches: gradient boosting and the artificial neural network (ANN), to predict aggregatory behaviour of two shark species: bull, Carcharhinus leucas and blacktip sharks, C. limbatus in the Gulf of Mexico. Applied to a large dataset considering many spatial and environ...

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Comparison of two machine learning frameworks for predicting aggregatory behaviour of sharks

Wade

Fisher

Matich

2022

Journal of Applied Ecology

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“…This species has a circumglobal distribution with seasonal aggregations occurring in all tropical and warm temperate seas, except for the Mediterranean (Compagno et al ., 2005; Norman, 2002; Pierce et al ., 2010; Rowat & Brooks, 2012). Such aggregations are associated with high productivity areas providing a foraging ground for this mainly planktivorous species (Norman, 2002; Petatán‐Ramírez et al ., 2020). Whale sharks are yolk‐sac viviparous species, with size at maturity ranging between 7 and 9 m L T for males and 9 m L T for females (Compagno et al ., 2005; Meekan et al ., 2015; Norman & Stevens, 2007).…”

Section: Figurementioning

confidence: 99%

General descriptions of the dermis structure of a juvenile whale shark Rhincodon typus from the Gulf of California

Becerril‐García

Pancaldi

Cruz-Villacorta

et al. 2021

Journal of Fish Biology

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The aim of this study is to provide preliminary observations on the microanatomy of Rhincodon typus skin using histology and electron microscopy analyses. Skin biopsies were obtained from a deceased juvenile male shark (548 cm total length) stranded in La Paz, Mexico, during February 2018. The results of this study evidenced the basic structure of the dermal denticles in the epidermis of the trunk of the shark, as well as the composition of the connective tissue in the hypodermis. Histological images of the hypodermis showed a high concentration of collagen fibres, formed by a large number of fine and wavy fibres of compact shape and little intercellular substance.

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“…The whale shark (Rhincodon typus, Smith 1828) is an endangered species of filter-feeding shark and has a worldwide distribution area in both tropical and warm seas (Pierce and Norman 2016). A greater presence of individuals is found in high seas waters (Clingham et al 2016;Ryan et al 2017;Petatán-Ramírez et al 2020). In contrast, immature males dominate coastal areas and congregate seasonally (Donati et al 2016;Pajuelo et al 2018).…”

Section: Introductionmentioning

confidence: 96%

presence of whale sharks based on oceanographic variations in Cenderawasih Bay National Park, Papua, Indonesia

et al. 2021

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Abstract. Manuhutu JF, Wiadnya DGR, Sambah AB, Herawati EY. 2021. The presence of whale sharks based on oceanographic variations in Cenderawasih Bay National Park, Papua, Indonesia. Biodiversitas 22: 4948-4955. Oceanographic factors have an important role in the study of estimating the distribution of fish resource habitats. Sea Surface Temperature (SST), Sea Surface Chlorophyll (SSC), Current Speed, Sea Surface Height (SSH), and bathymetry are important parameters that have been used in estimating the habitat of certain species. Through analyzing these environmental factors, research on the appearance and distribution of whale shark habitats in the Cenderawasih Bay National Park in Papua, Indonesia, utilized field observation data and satellite imagery. This study applied data on the presence of whale sharks and oceanographic parameter data from satellite recordings during 2019 – 2020. Analysis of the relationship and habitat modeling between the monthly presence of whale sharks and environmental parameters was carried out through the Generalized Additive Model (GAM) statistical approach and spatial analysis through the Geographic Information System approach. In the GAM analysis, data on the presence of whale sharks was used as a response variable. In contrast, the predictor variables consisted of SST, SSC, current speed, SSH, and bathymetric data. The results showed that the highest frequency of whale shark presence occurred in the transitional monsoon, with an average presence of 31.77 ± 4.00 %. The study also showed that all predictors showed a highly significant relationship (P < 0.001) to the number of whale sharks present. SST values range from 30.3 – 31.3 °C, SSC of 0.39 – 0.86 mg/L, the current speed of 0.46 – 0.65 m/s, SSH showed 0.63 to 1.00 cm, and bathymetry between 40 – 50 m . In the GAM model, the SST and SSC parameters were the two most important parameters that affect the presence of whale sharks, followed by SSHD, depth (bathymetry), and current parameters.

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Habitat suitability ofRhincodon typusin three localities of the Gulf of California: Environmental drivers of seasonal aggregations

Cited by 13 publications

References 39 publications

Comparison of two machine learning frameworks for predicting aggregatory behaviour of sharks

Comparison of two machine learning frameworks for predicting aggregatory behaviour of sharks

General descriptions of the dermis structure of a juvenile whale shark Rhincodon typus from the Gulf of California

presence of whale sharks based on oceanographic variations in Cenderawasih Bay National Park, Papua, Indonesia

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