Moving beyond landscape resistance: considerations for the future of connectivity modelling and conservation science

Abstract

Landscape connectivity, the extent to which a landscape facilitates the flow of ecological processes such as organism movement, has emerged as a central focus of landscape ecology and conservation science. Connectivity modelling now encompasses an enormous body of work across ecological theory and application. The dominant connectivity models in use today are based on the framework of ‘landscape resistance’, which is a way of measuring how landscape structure influences movement patterns. However, the simplistic assumptions and high degree of reductionism inherent to the landscape resistance paradigm severely limits the ability of connectivity algorithms to account for many fundamental aspects of animal movement, and thus greatly reduces the effectiveness and relevance of connectivity models for conservation theory and practice. In this paper, we first provide an overview of the development of connectivity modelling and resistance surfaces. We then discuss several key drivers of animal movement which are absent in resistance-based models, with a focus on spatiotemporal variation, human and interspecies interactions, and other context-dependent effects. We look at a range of empirical studies which highlight the strong impact these effects have on movement and connectivity predictions. But we also provide promising avenues of future research to address this: we discuss newly emerging technologies and interdisciplinary work, and look to developing methodologies, models and conversations which move beyond the limiting framework of landscape resistance, so that connectivity models can better reflect the complexities and richness of animal movement.

Agriculturally developed areas reduce genetic connectivity for a keystone neotropical ungulate

“…While our cost values were based on existing literature, it’s important to acknowledge that they may only partially reflect reality. Future studies should explore alternative parameterization methods, such as ResistanceGA (Peterman, 2018), or incorporate biological movement data from GPS tracking or radio necklace technology, to develop more accurate resistance models reflecting landscape fragmentation costs (Elliot et al, 2014; Unnithan Kumar et al, 2022). These efforts are crucial for mitigating the negative impacts of landscape fragmentation on wildlife populations.…”

Spatial phenotypic variability is higher between island populations than between mainland populations worldwide

“…While spatial isolation is typically a much stronger driver of genetic differentiation between populations due to limits to dispersal and genetic drift (Sexton et al 2014), the effects of geographic distance on spatial patterns of neutral genetic diversity seem globally weak and may be more heavily influenced by organismal life histories combined with environmental conditions, as advanced earlier by Orsini et al (2013) and Lira‐Noriega and Manthey (2014). Gene flow underlying genetic diversity is often not correlated with geographic distance between populations, but instead with the so‐called ‘resistance distance' (Unnithan Kumar et al 2022), meaning that other physical barriers, such as topological features (rivers and mountains) or environment (forest versus open habitat) are much more important. Such features are taxon‐specific and would hard to be measured with the current study design.…”

“…The initial filter resulted in 234 papers which were then redistributed among co-authors for a second round of filtering. In the second filter, we excluded papers that did not provide both population geographic coordinates and population-level quantitative data based on individual measurements, unless data were provided upon contacting the authors or could be obtained from figures using DataThief (Tummers 2006). We visually inspected maps plotted for each study separately and we made minor adjustments to the GPS coordinates when the coordinates placed the focal population off the island or mainland.…”

Digital ecologies: Materialities, encounters, governance

“…The 'minimal animal' ) in these simulations is divorced from observations of actual living animals (Bergman 2005). As Kumar et al (2021) note, models for predicting wildlife movement produce 'minimal ecologies' in which broader ecological relations are obscured (see also Kumar et al 2022). These practices have been critiqued for deepening conservation's biopolitical tendencies towards forms of population management neglectful of individual animals' experiences (see Srinivasan 2014;Braverman 2015;Kumar et al 2022), and digital technologies still require 'ground-truthing' to ensure equitable and responsible conservation outcomes.…”

“…Ecology is conventionally understood as the biological study of interactions between living organisms and their environments. Promisingly, the term has also been adopted and reworked in the social sciences and humanities, notably by STS scholar and philosopher of science Isabelle Stengers (1997; 2005; 2010), precisely because of its focus on relations, assemblages, practices, and connections between more-than-human actants (Hörl and Burton 2017; Latour 2004; Latour et al 2018; Tsing 2015). Inspired by Stengers, scholars have explored ecological frameworks for elucidating the relations between matter, bodies, and environments (Barad 2007; Bennett 2009; Braun and Whatmore 2010; Barua 2014; Lemke 2021)—key themes running throughout this paper.…”