Population‐level inference for home‐range areas

Abstract

Home‐range estimates are a common product of animal tracking data, as each range represents the area needed by a given individual. Population‐level inference of home‐range areas—where multiple individual home ranges are considered to be sampled from a population—is also important to evaluate changes over time, space or covariates such as habitat quality or fragmentation, and for comparative analyses of species averages. Population‐level home‐range parameters have traditionally been estimated by first assuming that the input tracking data were sampled independently when calculating home ranges via conventional kernel density estimation (KDE) or minimal convex polygon (MCP) methods, and then assuming that those individual home ranges were measured exactly when calculating the population‐level estimates. This conventional approach does not account for the temporal autocorrelation that is inherent in modern tracking data, nor for the uncertainties of each individual home‐range estimate, which are often large and heterogeneous. Here, we introduce a statistically and computationally efficient framework for the population‐level analysis of home‐range areas, based on autocorrelated kernel density estimation (AKDE), that can account for variable temporal autocorrelation and estimation uncertainty. We apply our method to empirical examples on lowland tapir Tapirus terrestris, kinkajou Potos flavus, white‐nosed coati Nasua narica, white‐faced capuchin monkey Cebus capucinus and spider monkey Ateles geoffroyi, and quantify differences between species, environments and sexes. Our approach allows researchers to more accurately compare different populations with different movement behaviours or sampling schedules while retaining statistical precision and power when individual home‐range uncertainties vary. Finally, we emphasize the estimation of effect sizes when comparing populations, rather than mere significance tests.

Foraging shifts in a Neotropical Turkey Vulture (Cathartes aura ruficollis) in the presence and absence of a northern migrant (C. a. meridionalis)

“…When meridionalis migrants were absent, marked ru fi collis foraged in both savanna and forest, but during the dry season when there were large numbers of meridionalis in the area, they foraged almost exclusively in forest and semi-open land cover types. Although our sample size was limited, the combination of patagial-tag and radio-tracking data together with robust methods for analyzing animal movements with small sample sizes (e.g., Fleming et al 2022) partially confirmed a shift in use of land cover types by ru fi collis .…”

“…We quantified the home range of patagially-marked and radio-tagged birds exhibiting home-ranging behavior ( n = 6). We compared space-use of ru fi collis Turkey Vultures during the period of sympatry versus allopatry with meridionalis Turkey Vultures using the population-level analysis of home range areas developed by Fleming et al (2022). Further, we used MODIS/Terra Vegetation Continuous Fields at a spatial resolution of 250 m (Dimiceli et al 2015) to calculate the percent of tree cover (forest vegetation) and non-tree cover (open and semi-open vegetation) within these home ranges.…”

The Statistical Building Blocks of Animal Movement Simulations

“…The challenges associated with Goal 6, listed in the Introduction, should not be underestimated. SDE methods have typically been used to estimate macro level quantities emerging from the movement behavior of individuals and populations, such as home range, speed, and distance travelled [73, 74]. To keep things simple, however, these methods avoid the complexities of dynamic background environments, which are known to greatly influence movement behavior [75].…”

“…The challenge should not be underestimated when it comes to demonstrating how a bottom-up StaME approach to constructing and analyzing movement track structures, when combined with fitting auto-regressive models (AR(p) where p is the depth of the time delay dependence), can be used to fit models to movement patterns at the subdiel and diel scales. SDE methods have typically been used to estimate macro level quantities emerging from the movement behavior of individuals and populations, such as home range, speed, and distance travelled [68, 69]. To keep things simple, however, these methods avoid the complexities of dynamic background environments, which are known to greatly influence movement behavior [70].…”

How do landscape and life history traits contribute to the threat context of Brazilian primates?

“…both restricted and wide‐ranging species are threatened) and (ii) persistence data (e.g. the minimum area required for maintaining viable populations and home ranges) are not available for all species (Henriquez et al ., 2021; Soto‐Saravia et al ., 2021; Fleming et al ., 2022; Lees et al ., 2022; Pérez‐Pereira et al ., 2022). Therefore, analyses that support species hierarchization (also called priority‐setting tools) are necessary, and should be used to improve protection lists and for set species conservation targets for selected conservation areas (Dunn, Hussell, & Welsh, 1999; Le Berre et al ., 2019).…”