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      Environmental drivers of reef manta ray ( Mobula alfredi) visitation patterns to key aggregation habitats in the Maldives

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          Abstract

          A detailed understanding of the dynamics of small-scale (10s km) habitat use by the reef manta ray ( Mobula alfredi) in the Maldives Archipelago is required to develop an effective national conservation management plan for this wide-ranging species. Here, a combination of photo-ID sightings data and acoustic telemetry were used to investigate both long-term M. alfredi visitation trends and small-scale movement patterns to key habitats on the eastern side of Baa Atoll (Hanifaru Bay feeding area, Dhigu Thila multifunctional site, and Nelivaru Thila cleaning station). All tagged and most of the sighted M. alfredi exhibited high affinity to the eastern side of Baa Atoll, where 99% of detections occurred, and 69% of individuals were re-sighted in multiple years. Sightings data suggests that visitation patterns may be associated with differences in habitat use by sex and maturity status. Boosted regression trees indicated that tag detection probability at Hanifaru Bay increased with increased westerly wind speed (>5ms -1) during the day, close to a new and full moon just after high tide, and when the tidal range was low. Interaction effects between predictors suggest that wind-driven oceanographic processes, such as Langmuir Circulation, maybe working to increase zooplankton concentration at this location. Tag detection probability increased at Dhigu Thila under similar conditions. At Nelivaru Thila, it increased at lower wind speeds (<5ms -1), close to a full moon, three hours after high tide. These results suggest that M. alfredi may utilise cleaning stations during the day when environmental conditions are not suitable for feeding. There was a high level of connectivity between these three locations, which suggests they form part of a network of key habitats that provide essential services to M. alfredi locally. Future conservation efforts should focus on identifying all areas of key habitat use for this species within the Maldives; applying strict protective measures to these sites and any connecting migration corridors which link them.

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          Applied Logistic Regression

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            A working guide to boosted regression trees.

            1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model. Boosted regression trees combine the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance). The final BRT model can be understood as an additive regression model in which individual terms are simple trees, fitted in a forward, stagewise fashion. 3. Boosted regression trees incorporate important advantages of tree-based methods, handling different types of predictor variables and accommodating missing data. They have no need for prior data transformation or elimination of outliers, can fit complex nonlinear relationships, and automatically handle interaction effects between predictors. Fitting multiple trees in BRT overcomes the biggest drawback of single tree models: their relatively poor predictive performance. Although BRT models are complex, they can be summarized in ways that give powerful ecological insight, and their predictive performance is superior to most traditional modelling methods. 4. The unique features of BRT raise a number of practical issues in model fitting. We demonstrate the practicalities and advantages of using BRT through a distributional analysis of the short-finned eel (Anguilla australis Richardson), a native freshwater fish of New Zealand. We use a data set of over 13 000 sites to illustrate effects of several settings, and then fit and interpret a model using a subset of the data. We provide code and a tutorial to enable the wider use of BRT by ecologists.
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              A review of methods for the assessment of prediction errors in conservation presence/absence models

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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                23 June 2021
                2021
                : 16
                : 6
                : e0252470
                Affiliations
                [1 ] The Manta Trust, Corscombe, Dorset, United Kingdom
                [2 ] University of Plymouth, Plymouth, United Kingdom
                Institut de Recherche pour le Developpement, FRANCE
                Author notes

                Competing Interests: The Four Seasons Resort Maldives at Landaa Giraavaru had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript, but provided logistical support Manta Trust researchers working in the Maldives. This does not alter our adherence to PLOS ONE policies on sharing data and materials. The authors have declared that no other competing interests exist.

                Author information
                https://orcid.org/0000-0001-8684-9096
                https://orcid.org/0000-0002-2056-9830
                Article
                PONE-D-21-00356
                10.1371/journal.pone.0252470
                8221513
                34161339
                37d04f59-3c3e-498e-a5d7-14a0801a131c
                © 2021 Harris, Stevens

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 5 January 2021
                : 16 May 2021
                Page count
                Figures: 7, Tables: 5, Pages: 25
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100007665, Save Our Seas Foundation;
                Award ID: SOSF P44_2007_01_13
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100007665, Save Our Seas Foundation;
                Award ID: SOSF P85_2009_03_25
                Award Recipient :
                Author who received the award: G M W Stevens Funder: Save Our Seas Foundation URLs to funds’ websites https://saveourseas.com/ Grant numbers: SOSF P44_2007_01_13 and SOSF P85_2009_03_25 Authors who received salary or other funding from commercial companies: G M W Stevens Full names of commercial companies that sponsored the study or authors: Four Seasons Resort Maldives at Landaa Giraavaru Prodivers Maldives at Kuredu URLs to sponsors’ websites https://www.fourseasons.com/maldiveslg/ https://www.prodivers.com/kuredu-maldives/dive-center/ The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Earth Sciences
                Atmospheric Science
                Meteorology
                Wind
                Earth Sciences
                Geomorphology
                Topography
                Landforms
                Atolls
                Earth Sciences
                Atmospheric Science
                Climatology
                Monsoons
                Physical Sciences
                Physics
                Acoustics
                Earth Sciences
                Geophysics
                Tides
                Physical Sciences
                Physics
                Geophysics
                Tides
                Biology and Life Sciences
                Psychology
                Behavior
                Animal Behavior
                Foraging
                Social Sciences
                Psychology
                Behavior
                Animal Behavior
                Foraging
                Biology and Life Sciences
                Zoology
                Animal Behavior
                Foraging
                Biology and Life Sciences
                Marine Biology
                Coral Reefs
                Earth Sciences
                Marine and Aquatic Sciences
                Marine Biology
                Coral Reefs
                Earth Sciences
                Marine and Aquatic Sciences
                Reefs
                Coral Reefs
                Biology and Life Sciences
                Ecology
                Habitats
                Ecology and Environmental Sciences
                Ecology
                Habitats
                Custom metadata
                All databases containing reef manta ray detection and environmental data used for boosted regression tree modelling is available via figshare at DOI: 10.6084/m9.figshare.13514849. The photographic identification data is available at http://idthemanta-intg.eu-west-2.elasticbeanstalk.com/home#!/home. All R scripts used in this study are available at https://github.com/JBjouffray/ggBRT and https://github.com/rspatial/dismo.

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