From dispersal constraints to landscape connectivity: lessons from species distribution modeling. Issue 10 (29th January 2015)

• Record Type: Journal Article
• Authors: Vasudev, Divya; Fletcher, Robert J.; Goswami, Varun R.; Krishnadas, Meghna
• Is Part Of: Ecography. Volume 38:Issue 10(2015:Oct.)
• Page Start: 967
• Page End: 978
• Publication Date: 2015-01-29
• Subjects: Ecology -- Periodicals; Biodiversity -- Periodicals; 574.5
• DOI: 10.1111/ecog.01306
• Languages: English
• ISSNs: 0906-7590

Abstract

Abstract : Connectivity plays a crucial role in determining the spread, viability, and persistence of populations across space. Dispersal across landscapes, or the movement of individuals or genes among resource patches, is critical for functional connectivity. Yet current connectivity modelling typically uses information on species location or habitat preference rather than movement, which unfortunately may not capture key dispersal limitations. We argue that recent developments in species distribution modelling provide insightful lessons for addressing this gap and advancing our understanding of connectivity. We suggest shifting the focus of connectivity modelling from locating where animals potentially disperse to a process‐based approach directed towards understanding and mapping factors that limit successful dispersal. To do so, we propose defining species dispersal requirements through identifying spatial, environmental and intrinsic constraints to successful dispersal, analogous to identifying environmental dimensions that define niches. We discuss the benefits of this constraint‐based framework for understanding the distribution of species, predicting species responses to climate change, and connectivity conservation practice. We illustrate how the framework can aid in identifying potential detrimental effects of human activities on connectivity and species persistence, and can spur the implementation of innovative conservation strategies. The proposed framework clarifies the validity and contextual utility of objectives and measures in existing connectivity models, and identifies gaps that may impede our understanding of connectivity and its integration into successful conservation strategies. We expect that this framework will facilitate a mechanistic approach to understanding and conserving connectivity, which will aid in effectively predicting and mitigating effects of ongoing environmental change.