Reserve design to optimize functional connectivity and animal density

An Andean bear (Tremarctos ornatus) from a camera trap in Ecuador, one of the species of interest that inspired the research.

The goal of Aichi Biodiversity Target 11, established by the Convention on Biological Diversity, is to conserve at least 17% of terrestrial natural areas by 2020 through the design of well-connected reserve systems. Many reserve design methods approach this task by focusing on spatial attributes like reserve size, shape, and number of patches, without explicitly linking these attributes to ecological processes. 

Two quantities linked to ecological processes, functional connectivity and animal abundance, are potentially stronger reserve design attributes, as they are both important contributors to species persistence. Functional connectivity captures how landscape features affect animal movement during foraging, dispersal and mating, while population density impacts population dynamics. Hence, these measures and the interplay between them influence both short-term survival of individuals and long-term population persistence.

In a recent study, my colleagues and I provide guidance on exactly how to go about designing a connected network of reserves that balances animal abundance and functional connectivity. We compared reserve designs that were created by maximizing 3 different landscape metrics: realized density, potential connectivity, and density-weighted connectivity.

Realized density estimates the distribution of individuals across the landscape, so maximizing this metric emphasizes local animal abundance. Potential connectivity estimates the resistance to movement experienced by individuals of the target species due to landscape features, and thus emphasizes functional connectivity. Density-weighted connectivity captures the capacity of individuals to move through the landscape with respect to their distribution across the area, and thus balances abundance and connectivity.

Reserve designs obtained on two landscapes (one with high simulated habitat fragmentation and one with low fragmentation) by maximizing realized density (RD), potential connectivity (PC) and density-weighted connectivity (DWC).

We found we could protect the largest number of individuals of a target species by designing reserves that maximized realized density. However, these designs had the lowest connectivity, large numbers of patches and low aggregation index values–indicators that such fragmented designs would likely not aid long-term population persistence.

Reserves designed to maximize potential connectivity protected the fewest individuals, despite forming spatially compact and connected reserves. The density-weighted connectivity metric was the “Goldilocks” of reserve design, striking a balance between the two extremes of density-only or connectivity-only. Reserves that maximized density-weighted connectivity covered areas that had high estimated density and high functional connectivity among them.

Maximizing the number of individuals of a target species that are protected by a reserve, as well as the ability of those individuals to access other protected land parcels for resource use, is an ecologically meaningful measure of space use and reserve quality. It also yields reserves with sensible spatial structure that should contribute to population persistence.

References:

Gupta, A. , Dilkina, B. , Morin, D. J., Fuller, A. K., Royle, J. A., Sutherland, C. and Gomes, C. P. 2019. Reserve design to optimize functional connectivity and animal density. Conservation Biology. DOI: 10.1111/cobi.13369.

Convention on Biological Diversity. 2011. Strategic Plan for Biodiversity 2011-2020.

2019-09-03T13:05:29-05:00 August 27th, 2019|

About the Author:

Amrita Gupta
Amrita Gupta is a Ph.D. student in the School of Computational Science and Engineering at the Georgia Institute of Technology. She is advised by Professor Bistra Dilkina, and her research focuses on algorithms and models for efficient resource allocation in biodiversity conservation and urban infrastructure resilience.