Surviving in steep terrain: a lab-to-field assessment of locomotor costs for wild mountain lions (Puma concolor)

Abstract

Background: Under current scenarios of climate change and habitat loss, many wild animals, especially large predators, are moving into novel energetically challenging environments. Consequently, changes in terrain associated with such moves may heighten energetic costs and effect the decline of populations in new localities. Methods: To examine locomotor costs of a large carnivorous mammal moving in mountainous habitats, the oxygen consumption of captive pumas (Puma concolor) was measured during treadmill locomotion on level and incline (6.8°) surfaces. These data were used to predict energetic costs of locomotor behaviours of free-ranging pumas equipped with GPS/accelerometer collars in California's Santa Cruz Mountains. Results: Incline walking resulted in a 42.0% ± 7.2 SEM increase in the costs of transport compared to level performance. Pumas negotiated steep terrain by traversing across hillsides (mean hill incline 17.2°± 0.3 SEM; mean path incline 7.3°± 0.1 SEM). Pumas also walked more slowly up steeper paths, thereby minimizing the energetic impact of vertical terrains. Estimated daily energy expenditure (DEE) based on GPS-derived speeds of free-ranging pumas was 18.3 MJ day − 1 ± 0.2 SEM. Calculations show that a 20 degree increase in mean steepness of the terrain would increase puma DEE by less than 1% as they only spend a small proportion (10%) of their day travelling. They also avoided elevated costs by utilizing slower speeds and shallower path angles. Conclusions: While many factors influence survival in novel habitats, we illustrate the importance of behaviours which reduce locomotor costs when traversing new, energetically challenging environments, and demonstrate that these behaviours are utilised by pumas in the wild.

Dead-Reckoning Animal Movements in R – A Reappraisal Using Gundog.Tracks

“…Given the approximate linear relationship between DBA [sensu 62] and terrestrial animal speed, DBA estimates can be multiplied by a gradient, m and summed with an intercept, c to derive speed [7,23], which are typically substituted with results from DBA~speed linear regression estimates [23,64,91,92]. The m coefficient should be selected such that behaviour -e.g., in its simplest form, ME could be filled with binary 0's and 1's as governed by a DBA threshold (labelling the ME vector 0 in sleep and resting behaviour).…”

Quantifying energetic costs and defining energy landscapes experienced by grizzly bears

“…Energy is a limiting factor for many species of wildlife (Parker et al, 1984;Shepard et al, 2013). Because daily and seasonal movements are major determinants of energy expenditure and balance, quantifying the landscape of energy use allows us to understand the biological and physical constraints acting on animals occupying different environments or pursuing different life strategies (Dunford et al, 2020;Halsey, 2016;Shepard et al, 2013;Wilson et al, 2012). Three key parameters must be quantified to effectively understand the energy landscape of an animal: (1) the energetic costs of moving at various speeds on differing slopes, (2) the movement behavior of free-ranging animals, using location and activity data, and (3) the physical landscape through which the animal moves (Avgar et al, 2013;Signer et al, 2019;Walker et al, 2015;Wilson et al, 2008Wilson et al, , 2006.…”

“…Whereas measuring the costs of movement in laboratory conditions is a relatively straightforward and well-defined process (Hoyt and Taylor, 1981;Taylor et al, 1982), applying those costs to movements of wild animals and understanding how those animals view and use the environment is more challenging. Animals can continuously alter their movement paths, travel speed and angle moving up and down slopes (Dunford et al, 2020;Halsey and White, 2017;Shine et al, 2015;Wickler et al, 2000).…”

An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers

“…[ 13 ]). Thirdly, an animal’s acceleration changes with pattern and frequency of locomotion, and also the environment in which it moves [ 14 ], thus allowing for the estimation of e.g. fin, wingbeat or stride frequency.…”