Abstract
Species distributional estimates are an essential tool to improve and implement effective conservation and management measures. Nevertheless, obtaining accurate distributional estimates remains a challenge in many cases, especially when looking at the marine environment, mainly due to the species mobility and habitat dynamism. Ecosystems surrounding oceanic islands are highly dynamic and constitute a key actor on pelagic habitats, congregating biodiversity in their vicinity. The main objective of this study was to obtain accurate fine-scale spatio-temporal distributional estimates of cetaceans in oceanic islands, such as the Madeira archipelago, using a long-term opportunistically collected dataset. Ecological Niche Models (ENM) were built using cetacean occurrence data collected on-board commercial whale watching activities and environmental data from 2003 to 2018 for 10 species with a diverse range of habitat associations. Models were built using two different datasets of environmental variables with different temporal and spatial resolutions for comparison purposes. State-of-the-art techniques were used to iterate, build and evaluate the MAXENT models constructed. Models built using the long-term opportunistic dataset successfully described distribution patterns throughout the study area for the species considered. Final models were used to produce spatial grids of species average and standard deviation suitability monthly estimates. Results provide the first fine-scale (both in the temporal and spatial dimension) cetacean distributional estimates for the Madeira archipelago and reveal seasonal/annual distributional patterns, thus providing novel insights on species ecology and quantitative data to implement better dynamic management actions.
| Original language | English |
|---|---|
| Article number | 688248 |
| Number of pages | 22 |
| Journal | Frontiers in Marine Science |
| Volume | 8 |
| Issue number | 688248 |
| DOIs | |
| Publication status | Published - 8 Jul 2021 |
Bibliographical note
Funding Information:We wish to thank to all the people and organizations involved in the collection of data over the years. We thank the whale-watching operators Ventura | Nature Emotions, Lobosonda and Seaborn (especially to Miguel Fernandes). Funding. This study was supported by: (i) INTERTAGUA, MAC2/1.1.a/385 funded by MAC INTERREG 2014-2020, (ii) Oceanic Observatory of Madeira throughout the project M1420-01-0145-FEDER-000001-OOM, and (iii) Funda??o para a Ci?ncia e Tecnologia (FCT), Portugal, through the strategic project UID/MAR/04292/2020 granted to MARE UI&I. AD and FA have grants funded by ARDITI?Madeira?s Regional Agency for the Development of Research, Technology and Innovation, throughout the project M1420-09-5369-FSE-000002. RF was partially supported by a FCT doctoral grant (SFRH/BD/147225/2019).
Funding Information:
This study was supported by: (i) INTERTAGUA, MAC2/1.1.a/385 funded by MAC INTERREG 2014-2020, (ii) Oceanic Observatory of Madeira throughout the project M1420-01-0145-FEDER-000001-OOM, and (iii) Fundação para a Ciência e Tecnologia (FCT), Portugal, through the strategic project UID/MAR/04292/2020 granted to MARE UI&I. AD and FA have grants funded by ARDITI—Madeira’s Regional Agency for the Development of Research, Technology and Innovation, throughout the project M1420-09-5369-FSE-000002. RF was partially supported by a FCT doctoral grant (SFRH/BD/147225/2019).
Publisher Copyright:
© Copyright © 2021 Fernandez, Alves, Ferreira, Fischer, Thake, Nunes, Caldeira and Dinis.
Keywords
- whales, dolphins, pelagic, ecological niche modeling, opportunistic data, whale watching
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This is the final published version of the article (version of record). It first appeared online via Frontiers Media at 10.3389/fmars.2021.688248. Please refer to any applicable terms of use of the publisher.