Social network models predict movement and connectivity in ecological landscapes

Abstract

Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.dispersal | graph theory | habitat fragmentation | latent space models | landscape ecology N etwork analysis has recently exploded across scientific disciplines, including the social sciences, physics, cellular biology, and ecology (1-4). Topics as divergent as the stability of the Internet and the structure of metabolic reactions can be depicted through network analysis (1, 3). Such analysis is beneficial because it can facilitate the identification of complex, and often emergent, patterns, and can provide hypotheses for relationships between structure and function in many systems (2, 3). Nonetheless, a growing, widespread concern in the topic of network analysis is the reliability of data used in constructing networks (4-8).In ecology and conservation, network analysis is increasingly being used to assess population connectivity across landscapes (9-13). Because of the importance of connectivity in conservation and its relevance to population and community ecology (14-16), network analysis and the accompanying use of graph theory are often emphasized as powerful approaches that have modest data requirements for assessing connectivity (10,11,13). In this spatial context, resource patches are considered nodes (or vertices) and movements and/or flows between pat...

Network modularity reveals critical scales for connectivity in ecology and evolution

“…Metapopulation capacity is defined as the leading eigenvalue of a 'connectivity matrix', which previously has used Euclidean distance between patches as a proxy for isolation and potential movement (Methods). However, elsewhere we have shown that statistical models developed for social networks reliably predict movements, in terms of model fit and predicting unknown linkages and improve predictions of metapopulation viability for cactus bugs compared with traditional distance-based proxies 21 . We tested for variation in predicted metapopulation capacity by fitting social network models to predict movement rates of cactus bugs, contrasting models that ignore modularity 21 to those that considered the potential for modularity 14 (Methods).…”

“…However, elsewhere we have shown that statistical models developed for social networks reliably predict movements, in terms of model fit and predicting unknown linkages and improve predictions of metapopulation viability for cactus bugs compared with traditional distance-based proxies 21 . We tested for variation in predicted metapopulation capacity by fitting social network models to predict movement rates of cactus bugs, contrasting models that ignore modularity 21 to those that considered the potential for modularity 14 (Methods). This approach allows for altering predictions of connectivity matrices, and thus the metapopulation capacity, based on the potential for modularity in movements.…”

“…We estimated modularity in two mark-recapture datasets that span several orders of spatial magnitude: movements of the cactus bug (Chelinidea vittiger) on patchy Opuntia cactus and breeding-season movements of the Everglades snail kite (Rostrhamus sociabilis plumbeus) among wetlands 21 . We also estimated modularity from genetic data on breeding aggregations of the bullfrog (Rana catesbeiana) 24 and among core populations of black bears (Ursus americanus floridanus) in Florida 25 .…”

“…Although theory suggests that modularity is highly relevant to spatial dynamics, the empirical application of modularity concepts to spatial ecology and evolution has been scarce. Recent algorithms from statistical physics and social sciences may help overcome these challenges while honouring complex dynamics that may arise in assessments of connectivity and critical spatial scales, such as highly directional movement 21 , spatial variation in the resolution of critical scales (that is, non-stationarity 22 ) and effects of scale beyond geographic distance alone 23 .…”

Quantifying the reliability of dispersal paths in connectivity networks

“…For example, pioneering work by Jordán [30] applied some aspects of reliability theory to dispersal and landscape ecology in networks to design more reliable migration corridors (for some other applications of reliability theory to biology, see [31][32][33][34]). Link probabilities have also been used analytically in other ecological contexts [3,[35][36][37], for example, to calculate the likelihood that two patches will be mutually reachable [38,39]. Circuit theory is another engineering principle used to characterize dispersal in landscape ecology, with many useful properties such as explicit consideration of the path redundancies and the relative density of random walkers taking specific routes through a landscape [17,40,41].…”