A comparison of approaches for including connectivity in systematic conservation planning

Abstract

Plans for expanding protected area systems (prioritizations) often aim to facilitate connectivity. To achieve this, many approaches—based on different assumptions and datasets—have been developed. However, little is known about how such approaches influence prioritizations. We examine eight approaches that aim to promote connectivity in prioritizations. Using Washington State (USA) and its avifauna as a case study, we generated prioritizations that aimed to meet species' representation targets and promote connectivity by (a) maximizing total area; (b) further maximizing species representation; (c) minimizing boundary length; and connecting areas based on (d) minimizing human pressure, (e) minimizing naturalness‐based landscape resistance, (f) minimizing focal species landscape resistance, (g) minimizing habitat heterogeneity and (h) maximizing environmental similarity. We controlled for total expenditure, species' representation, and existing land use policies to enable comparisons among prioritizations. We then used a hierarchical cluster analysis to compare prioritizations, based on which areas they selected. We also evaluated how well each approach facilitated connectivity as measured by the other approaches. We found that different approaches for promoting connectivity can lead to very different or very similar prioritizations, depending on their underlying assumptions. In particular, the boundary length approach—which is widely used in systematic conservation planning—resulted in a prioritization that was highly dissimilar to all other prioritizations. Surprisingly, approaches based on very different underlying assumptions produced similar prioritizations, such as maximizing total area and minimizing focal species landscape resistance approaches. Moreover, when comparing the prioritizations based on the level of connectivity they could facilitate, we found that none of the prioritizations facilitated a high level of connectivity for all eight approaches. Synthesis and applications. We recommend carefully considering the assumptions and limitations that underpin approaches for promoting connectivity. Our findings demonstrate that different connectivity approaches can produce marked differences in priorities and, in turn, produce trade‐offs between different approaches. Indeed, despite the ubiquity of the boundary length approach, practitioners might find that other approaches can better achieve conservation objectives. Practitioners can use our methodology for comparing different connectivity approaches to help to navigate trade‐offs among them.

Balancing land use for conservation, agriculture, and renewable energy

“…Further, the boundary penalty approach can lead to solutions that are highly dissimilar to others and may result in solutions that don't effectively provide functional connectivity (Hanson et al, 2022).…”

Connecting the Dots: Assessing Landscape Connectivity Algorithms for Biodiversity Conservation

“…It is essential to understand these traits in order to implement adaptation options that can help species survive under the new and changing conditions. To predict bottlenecks in the current and future habitat networks, it is important to study the dispersal of species and how it relates to the connectivity of the landscape (Beger et al 2022;Hanson et al 2022).…”

Rapid evaluation of habitat connectivity change to safeguard multispecies persistence in human-transformed landscapes

“…There exists a multitude of indicators for monitoring the structural and functional facets of connectivity (Keeley et al, 2021) but how they compare and what they imply for conservation management is often not easy to interpret (Hanson et al, 2022; Lalechère & Bergès, 2021; Wood et al, 2022). To date, common definitions, goals and standards to measure and evaluate connectivity change have not been well-established and consequently, criteria for critical connectivity thresholds are often not operationalized in spatial conservation planning (Beger et al, 2022; Ward et al, 2020; Wood et al, 2022).…”