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Article

Habitat Suitability Modeling to Inform Seascape Connectivity Conservation and Management

1
Department of Biological Sciences, University of Alberta, Edmonton, AB T6G 2E9, Canada
2
School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK
3
Seascape Analytics Ltd., Plymouth PL2 1RP, UK
*
Authors to whom correspondence should be addressed.
Academic Editor: Michael Wink
Diversity 2021, 13(10), 465; https://doi.org/10.3390/d13100465
Received: 1 September 2021 / Revised: 18 September 2021 / Accepted: 23 September 2021 / Published: 26 September 2021
(This article belongs to the Special Issue Ecological Connectivity among Tropical Coastal Ecosystems)
Coastal habitats have experienced significant degradation and fragmentation in recent decades under the strain of interacting ecosystem stressors. To maintain biodiversity and ecosystem functioning, coastal managers and restoration practitioners face the urgent tasks of identifying priority areas for protection and developing innovative, scalable approaches to habitat restoration. Facilitating these efforts are models of seascape connectivity, which represent ecological linkages across heterogeneous marine environments by predicting species-specific dispersal between suitable habitat patches. However, defining the suitable habitat patches and migratory pathways required to construct ecologically realistic connectivity models remains challenging. Focusing on two reef-associated fish species of the Florida Keys, United States of America (USA), we compared two methods for constructing species- and life stage-specific spatial models of habitat suitability—penalized logistic regression and maximum entropy (MaxEnt). The goal of the model comparison was to identify the modeling algorithm that produced the most realistic and detailed products for use in subsequent connectivity assessments. Regardless of species, MaxEnt’s ability to distinguish between suitable and unsuitable locations exceeded that of the penalized regressions. Furthermore, MaxEnt’s habitat suitability predictions more closely aligned with the known ecology of the study species, revealing the environmental conditions and spatial patterns that best support each species across the seascape, with implications for predicting connectivity pathways and the distribution of key ecological processes. Our research demonstrates MaxEnt’s promise as a scalable, species-specific, and spatially explicit tool for informing models of seascape connectivity and guiding coastal conservation efforts. View Full-Text
Keywords: habitat suitability modeling; seascape connectivity; seascape ecology; fish migrations; marine spatial planning; habitat restoration habitat suitability modeling; seascape connectivity; seascape ecology; fish migrations; marine spatial planning; habitat restoration
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MDPI and ACS Style

Stuart, C.E.; Wedding, L.M.; Pittman, S.J.; Green, S.J. Habitat Suitability Modeling to Inform Seascape Connectivity Conservation and Management. Diversity 2021, 13, 465. https://doi.org/10.3390/d13100465

AMA Style

Stuart CE, Wedding LM, Pittman SJ, Green SJ. Habitat Suitability Modeling to Inform Seascape Connectivity Conservation and Management. Diversity. 2021; 13(10):465. https://doi.org/10.3390/d13100465

Chicago/Turabian Style

Stuart, Courtney E., Lisa M. Wedding, Simon J. Pittman, and Stephanie J. Green 2021. "Habitat Suitability Modeling to Inform Seascape Connectivity Conservation and Management" Diversity 13, no. 10: 465. https://doi.org/10.3390/d13100465

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