Characterizing demographic parameters across environmental gradients: a case study with Ontario moose (Alces alces)

Abstract

Abstract. Population-level demographic characteristics as estimated by standard logistic growth models (i.e., carrying capacity and intrinsic growth rate) should vary with changes in habitat quality and availability of resources. However, few published studies have tested this hypothesis by comparing population growth rates across broad bioclimatic gradients, and fewer still the carrying capacities of those populations. We used time series data on moose (Alces alces) population densities based on aerial census and hunter harvest data for 34 management units across Ontario to estimate local carrying capacities and intrinsic growth rates. These population parameters were then regressed against associated habitat covariates for each management unit to assess how moose demography changes across a broad gradient of productivity, habitat abundance, and timber harvest. Moose carrying capacity was found to increase with increasing forest productivity as measured by DNDVI and the proportion of mixedwood stands in the forest. Both variables are plausibly indicative of high quality forage abundance for moose. Moose carrying capacity decreased with the proportion of forest stands harvested for timber annually, suggesting that immediate removal of forest stands and increased access by hunters temper maximum population size. Maximum rates of population growth by Ontario moose did not vary predictably with any of the landscape covariates tested. These findings contribute to our understanding of changes in demography across broad geographic and bioclimatic gradients and suggest that crude population estimators may be derived based on known habitat preferences and resource availability without a priori knowledge of animal abundance.

Dynamical facilitation of the ideal free distribution in nonideal populations

“…We already have a term for this phenomenon-the carrying capacity-but this indicates that animal behavior and population dynamics interacting to produce the IFD constitute the mechanism by which carrying capacities emerge (Street et al, 2017). The connection between fine-scale behavior and equilibrium densities has been demonstrated based on presumed or correlative relationships between behavior and population dynamics (Boyce & McDonald, 1999;Boyce et al, 2016;Matthiopoulos et al, 2015;Street et al, 2015Street et al, , 2017, but our findings here suggest that the specific interaction between population dynamics and animal…”

“…Although animal space use is decidedly dynamic, with individuals often selecting for seemingly suboptimal habitat at fine spatiotemporal scales, an IFD can emerge from nonideal behavior at both fine (Griffen, ) and broad scales (Street, Rodgers, Avgar, & Fryxell, ; Street, Rodgers, Avgar, Vander Vennen, & Fryxell, ). A necessary next step in understanding this process is to examine how individual movement strategies (e.g., correlated random walks vs. Brownian motion; Turchin, ), and variation among individuals in movement behaviors (e.g., exploratory vs. site‐fidelitous), encourage or inhibit the development of an IFD in a spatially explicit population with variable perception.…”

“…Although animal space use is decidedly dynamic, with individuals often selecting for seemingly suboptimal habitat at fine spatiotemporal scales, an IFD can emerge from nonideal behavior at both fine (Griffen, 2009) and broad scales (Street, Rodgers, Avgar, & Fryxell, 2015;Street, Rodgers, Avgar, Vander Vennen, & Fryxell, 2017). A necessary next step in understanding this process is to examine how individual movement strategies (e.g., correlated random walks vs.…”

Comparing resource selection and demographic models for predicting animal density

“…Therefore, variation unexplained by landscape covariates in carrying capacity (∼54%) and habitat selection (∼85%) may be so high as to obscure the relationship between selection‐based density and observed carrying capacity. Assuming we fitted the appropriate ecological model to the data (i.e., we are not missing critical determinants of habitat selection or carrying capacity, an assumption corroborated by previous findings; Street et al ), this could be overcome by increasing the number and length of time series available for the analysis, and by increased sampling of presence–absence in study sites.…”

“…We used simple linear regression to estimate changes in carrying capacity within management units as a function of environmental characteristics corresponding to habitat availability, specifically proportional coverage by mixedwood stands based on OLC, average ΔNDVI from summer to winter, and the proportion of the landscape harvested per year on average, based on spatially explicit timber harvest polygons available from the Land Information Ontario data warehouse (https://www.javacoeapp.lrc.gov.on.ca/geonetwork/srv/en/main.home, accessed 14 Jan 2014). Street et al () demonstrated that this combination of landscape parameters best described changes in moose carrying capacity. We logit transformed all proportion data.…”