Bridging the gap between movement data and connectivity analysis using the time-explicit Step Selection Function (tSSF)

Background Understanding how to connect habitat remnants to facilitate the movement of species is a critical task in an increasingly fragmented world impacted by human activities. The identification of dispersal routes and corridors through connectivity analysis requires measures of landscape resistance but there has been no consensus on how to calculate resistance from habitat characteristics, potentially leading to very different connectivity outcomes. Methods We propose a new model called the time-explicit step selection function (tSSF) that can be directly used for connectivity analysis in the context of the spatial absorbing Markov chain (SAMC) framework without requiring arbitrary transformations. The tSSF model combines a time model with a standard selection function and can provide complementary information regarding how animals use landscapes by separately assessing the drivers of time to traverse the landscape and the drivers of habitat selection. These models are illustrated using GPS-tracking data from giant anteaters ( Myrmecophaga tridactyla ) in the Pantanal wetlands of Brazil. Results The time model revealed that the fastest movements tended to occur between 8 pm and 5 am, suggesting a crepuscular/nocturnal behavior. Giant anteaters moved faster over wetlands while moving much slower over forests and savannas, in comparison to grasslands. We found that wetlands were consistently avoided whereas forest and savannas tended to be selected. Importantly, this model revealed that selection for forest increased with temperature, suggesting that forests may act as important thermal shelters when temperatures are high. Finally, the tSSF results can be used to simulate movement and connectivity within a fragmented landscape, revealing that giant anteaters will often not use the shortest-distance path to the destination patch (because that would require traversing a wetland, an avoided habitat) and that approximately 90% of the individuals will have reached the destination patch after 49 days. Conclusions The approach proposed here can be used to gain a better understanding of how landscape features are perceived by individuals through the decomposition of movement patterns into a time and a habitat selection component. This approach can also help bridge the gap between movement-based models and connectivity analysis, enabling the generation of time-explicit results.