Individual Variation in Temporal Dynamics of Post-release Habitat Selection

Abstract

Translocated animals undergo a phase of behavioral adjustment after being released in a novel environment, initially prioritizing exploration and gradually shifting toward resource exploitation. This transition has been termed post-release behavioral modification. Post-release behavioral modification may also manifest as changes in habitat selection through time, and these temporal dynamics may differ between individuals. We aimed to evaluate how post-release behavioral modification is reflected in temporal dynamics of habitat selection and its variability across individuals using a population of translocated female greater sage-grouse as a case study. Sage-grouse were translocated from Wyoming to North Dakota (USA) during the summers of 2018–2020. We analyzed individual habitat selection as a function of sagebrush cover, herbaceous cover, slope, and distance to roads. Herbaceous cover is a key foraging resource for sage-grouse during summer; thus, we expected a shift from exploration to exploitation to manifest as temporally-varying selection for herbaceous cover. For each individual sage-grouse (N = 26), we tested two competing models: a null model with no time-dependence and a model with time-dependent selection for herbaceous cover. We performed model selection at the individual level using an information-theoretic approach. Time-dependence was supported for five individuals, unsupported for seven, and the two models were indistinguishable based on AICc for the remaining fourteen. We found no association between the top-ranked model and individual reproductive status (brood-rearing or not). We showed that temporal dynamics of post-release habitat selection may emerge in some individuals but not in others, and that failing to account for time-dependence may hinder the detection of steady-state habitat selection patterns. These findings demonstrate the need to consider both temporal dynamics and individual variability in habitat selection when conducting post-release monitoring to inform translocation protocols.

Time-varying habitat selection analysis: a model and applications for studying diel, seasonal and post-release changes

“…Most commonly, an a prior i discretization of time periods of apparent biological relevance is made, although this discretization can sometimes be difficult to justify, let alone to validate. The alternative approach of simply integrating time as predictor in a RSF has limitations (see discussion in Picardi et al (2021)), and more statistically complex approaches (Hooten et al 2014) are unlikely to be broadly used. In this work, we propose a new approach based on dynamic logistic models (Fahrmeir 1992) to easily estimate temporal changes in habitat selection, in a framework consistent with RSF.…”

“…This may be of particular importance, for instance, in the study of inter-individual variability, as the timing of change can be one of the differencing 13 variables, as evidenced in our post-release study case. As recognized by Picardi et al (2021), time-varying HSA opens a new avenue to broaden the scope of the studies of inter-individual differences in space use, which has so far focused on movement characteristics or habitat selection strength. More generally, even when the relevance of an a priori discretization of time is easier to ascertain, such as when comparing daytime to night-time habitat selection, timevarying HSA allows one to immediately identify unusual periods (e.g.…”

“…This again makes discretization of time difficult, or even irrelevant if one is interested in the dynamics of the change itself. This issue has been recognized before and various suggestions have been offered, from using a combination of movement metrics and habitat use information to define periods (but without estimating habitat selection) (Basille et al 2013), to integrating time directly into the RSF predictor (but with a constraint on the shape of the time-dependence) (Picardi et al 2021) or using continuous-time movement models (but with a complex implementation) (Hooten et al 2014). Generally, we feel that ecologists are still lacking an easy and flexible tool to answer the question ‘when is an animal’s habitat selection changing?’, learning from the data with no assumption about when changes should happen.…”

Memory drives the formation of animal home ranges: Evidence from a reintroduction

“…However, finer-grained habitat conditions not shared between the source area and the translocation area would likely require temporally dynamic learning by the released individuals. This could be accommodated in the proposed model by allowing for time-dependent selection coefficients (Picardi et al, 2021) and would be an interesting avenue for future analyses.…”

Using piecewise regression to identify biological phenomena in biotelemetry datasets

“…Most researchers have sought to quantify effects on vital rates, such as survival and fecundity (Casas et al, 2015; DelGiudice et al, 2005; Lameris & Kleyheeg, 2017) or short‐term ethological responses such as changes in the time spent grooming (Kölzsch et al, 2016; Rachlow et al, 2014). Because capture and handling may result in a short‐term period of altered movement behaviour (Picardi et al, 2021), it is common to remove data from the first week or two post‐capture, although often without biological or empirical justification for the threshold used to filter the data.…”