Disturbance type and species life history predict mammal responses to humans

Abstract

Human activity and land use change impact every landscape on Earth, driving declines in many animal species while benefiting others. Species ecological and life history traits may predict success in human‐dominated landscapes such that only species with “winning” combinations of traits will persist in disturbed environments. However, this link between species traits and successful coexistence with humans remains obscured by the complexity of anthropogenic disturbances and variability among study systems. We compiled detection data for 24 mammal species from 61 populations across North America to quantify the effects of (1) the direct presence of people and (2) the human footprint (landscape modification) on mammal occurrence and activity levels. Thirty‐three percent of mammal species exhibited a net negative response (i.e., reduced occurrence or activity) to increasing human presence and/or footprint across populations, whereas 58% of species were positively associated with increasing disturbance. However, apparent benefits of human presence and footprint tended to decrease or disappear at higher disturbance levels, indicative of thresholds in mammal species’ capacity to tolerate disturbance or exploit human‐dominated landscapes. Species ecological and life history traits were strong predictors of their responses to human footprint, with increasing footprint favoring smaller, less carnivorous, faster‐reproducing species. The positive and negative effects of human presence were distributed more randomly with respect to species trait values, with apparent winners and losers across a range of body sizes and dietary guilds. Differential responses by some species to human presence and human footprint highlight the importance of considering these two forms of human disturbance separately when estimating anthropogenic impacts on wildlife. Our approach provides insights into the complex mechanisms through which human activities shape mammal communities globally, revealing the drivers of the loss of larger predators in human‐modified landscapes.

Modelling the distribution and intraguild associations of an understudied mesocarnivore across the contiguous United States

“…Wildlife assemblages across the world are often affected by human development and activity (Crooks et al, 2010;Suraci et al, 2021) and gross primary productivity (Willig et al, 2003), but none of these factors affected the ψ or p of grey fox site use in our analyses using sites from across the contiguous United States. Many studies have reported that anthropogenic development has a negative effect on grey foxes (Kowalski et al, 2015;Lombardi et al, 2017;Nickel et al, 2020;Ordeñana et al, 2010), yet other studies found that they use a range of human development intensities (Harrison, 1997;Kapfer & Kirk, 2012;Riley, 2006;Suraci et al, 2021) and that human activity has a positive effect on grey foxes (Nickel et al, 2020;Rota et al, 2016). These varying effects of anthropogenic development may be due to more complex factors, such as grey foxes using anthropogenic landscapes to avoid coyotes (Lesmeister et al, 2015;Wang et al, 2015).…”

“…Following the recommendations of MacKenzie et al (2017), instead of estimating the proportion of total study area occupied by each species and the detection probability of a species, respectively, we defined the parameters of interest (occupancy and detection probability) as the probability of site use (ψ) and the intensity of site use ( p ; i.e. the probability a species returns to a site, given use), respectively, to control for the potential bias that can come from closely spaced cameras that potentially violate spatial independence (Kery & Royle, 2008; MacKenzie et al, 2002, 2017; Suraci et al, 2021). Multiple covariates were correlated with other predictors (Pearson's r > 0.7).…”

“…States using a single species, single season Bayesian occupancy model (Kery & Royle, 2015;MacKenzie et al, 2017). the probability a species returns to a site, given use), respectively, to control for the potential bias that can come from closely spaced cameras that potentially violate spatial independence (Kery & Royle, 2008;MacKenzie et al, 2002MacKenzie et al, , 2017Suraci et al, 2021). Multiple covariates were correlated with other predictors (Pearson's r > 0.7).…”

“…Before analysis, we standardized each continuous covariate to have a mean = 0 and standard deviation = 1 to improve numerical optimization and allow for direct comparison of beta coefficients (Schielzeth, 2010). We assessed model fit using Bayesian p-values calculated from observed and modelled data (Hobbs & Hooten, 2015;Suraci et al, 2021). We calculated these values using chi-squared discrepancy statistics (Kéry & Shuab, 2012;Suraci et al, 2021).…”

“…We assessed model fit using Bayesian p-values calculated from observed and modelled data (Hobbs & Hooten, 2015;Suraci et al, 2021). We calculated these values using chi-squared discrepancy statistics (Kéry & Shuab, 2012;Suraci et al, 2021).…”