Potential paths for male-mediated gene flow to and from an isolated grizzly bear population

Enhanced regional connectivity between western North American national parks will increase persistence of mammal species diversity

Many protected areas worldwide increasingly resemble habitat isolates embedded in human-modified landscapes. However, establishing linkages among protected areas could significantly reduce species-loss rates. Here we present a novel method having broad applicability for assessing enhanced regional connectivity on persistence of mammal diversity. We combine theoretically-derived species relaxation rates for mammal communities with empirically-derived pathways. We assess the value of enhanced regional connectivity for two hypothetical networks of national parks in western North America: the Yellowstone-Glacier network and the Mount Rainier-North Cascades network. Linking the Yellowstone and Glacier park assemblages by eliminating barriers to movement in identified mammal dispersal pathways and by incorporating adjacent wilderness areas and known ungulate migratory routes into a protected area network would greatly enlarge available habitat. This would enhance medium to large mammal species persistence time by factor of 4.3, on average, or ~ 682 generations relative to individual parks. Similarly, linking Mount Rainier and North Cascades park assemblages would enhance mammal species persistence time by a factor of 4.3, on average, or ~305 generations relative to individual parks. Enhancing regional connectivity among western North America parks could serve as an important template for landscape-scale conservation in the 21st century.

Deterministic, random, or in between? Inferring the randomness level of wildlife movements

BACKGROUND

When assessing connectivity, it is crucial to rely on accurate modeling frameworks that consider species movement preferences and patterns. One important aspect is the level of randomness or unpredictability in the route selection. In this respect, traditional approaches (based on least-cost path or circuit theory) consider species movements unrealistically as totally deterministic or as totally random. A recent approach (randomized shortest path) advocates for choosing intermediate levels of randomness through a single parameter. This parameter may be optimized by validating connectivity surfaces developed from different levels of randomness against observed movement data. However, connectivity models are seldom validated, and it is still unclear how to approach this task. To address this knowledge gap, this paper aims at comparing different validation methods to infer the optimal randomness level in connectivity studies. Additionally, we aimed to disentangle the practical consequences of applying traditional connectivity approaches versus using an optimized level of movement randomness when delineating corridors.

METHODS

These objectives were accomplished through the study case of the Iberian lynx, an endangered species whose maintenance and recovery depend on the current connectivity among its population nuclei. We firstly determined a conductance surface based on point selection functions accounting for the behavioral state (territorial or exploratory) of individuals. Secondly, we identified the level of randomness that better fits lynxes' movements with independent GPS locations and different validation techniques. Lastly, we delineated corridors between lynx population nuclei through a) the randomized shortest path approach and the extreme and optimal levels of randomness of each validation method, and b) the traditional connectivity approaches.

RESULTS

According to all used validation methodologies, models with intermediate levels of randomness outperformed those with extreme randomness levels representing totally deterministic or random movements. We found differences in the optimal randomness level among validation methods but similar results in the delineation of corridors. Our results also revealed that models with extreme randomness levels (deterministic and random walk) of the randomized path approach provided equivalent corridor networks to those from traditional approaches. Moreover, these corridor networks calculated with traditional approaches showed notable differences in patterns from the corridor network calculated with an optimized randomness level.

CONCLUSIONS

Here we presented a connectivity model with a solid biological basis that calibrates the level of movement randomness and is supported by comprehensive validation methods. It is thus a step forward in the search and evaluation of connectivity approaches that lead to improved, efficient, and successful management actions.