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      Modelling Dolphin Distribution to Inform Future Spatial Conservation Decisions in a Marine Protected Area

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          Abstract

          As marine predators experience increasing anthropogenic pressures, there is an urgent need to understand their distribution and their drivers to inform spatial conservation planning. We used an ensemble modelling approach to investigate the spatio-temporal distribution of southern Australian bottlenose dolphins ( Tursiops cf. australis) in relation to a variety of ecogeographical and anthropogenic variables in Coffin Bay, Thorny Passage Marine Park, South Australia. Further, we evaluated the overlap between current spatial management measures and important dolphin habitat. Dolphins showed no distinct seasonal shifts in distribution patterns. Models of the entire study area indicate that zones of high probability of dolphin occurrence were located mainly within the inner area of Coffin Bay. In the inner area, zones with high probability of dolphin occurrence were associated with shallow waters (2–4 m and 7–10 m) and located within 1,000 m from land and 2,500 m from oyster farms. The multi-modal response curve of depth in the models likely shows how the different dolphin communities in Coffin Bay occupy different embayments characterized by distinct depth patterns. The majority of areas of high (>0.6) probability of dolphin occurrence are outside sanctuary zones where multiple human activities are allowed. The inner area of Coffin Bay is an important area of year-round habitat suitability for dolphins. Our results can inform future spatial conservation decisions and improve protection of important dolphin habitat.

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          Selecting pseudo-absences for species distribution models: how, where and how many?

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            Predicting species distributions for conservation decisions

            Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of ‘translators’ between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.
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              Absent or undetected? Effects of non-detection of species occurrence on wildlife–habitat models

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                Author and article information

                Contributors
                cecipass8@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                23 October 2018
                23 October 2018
                2018
                : 8
                : 15659
                Affiliations
                [1 ]ISNI 0000 0004 0367 2697, GRID grid.1014.4, Cetacean Ecology, Behaviour and Evolution Lab, , College of Science and Engineering, Flinders University, ; South Australia, 5001 Australia
                [2 ]ISNI 0000 0004 0367 2697, GRID grid.1014.4, Molecular Ecology Lab, College of Science and Engineering, Flinders University, ; South Australia, 5001 Australia
                Author information
                http://orcid.org/0000-0001-8667-8274
                http://orcid.org/0000-0002-7293-5847
                Article
                34095
                10.1038/s41598-018-34095-2
                6199262
                29311619
                73e18ac8-34e2-47b3-8bc7-9d0ef71e6437
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 14 February 2018
                : 11 October 2018
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