The forage maturation hypothesis (FMH) proposes that ungulate migration is driven by selection for high forage quality. Because quality declines with plant maturation, but intake declines at low biomass, ungulates are predicted to select for intermediate forage biomass to maximize energy intake by following phenological gradients during the growing season. We tested the FMH in the Canadian Rocky Mountains by comparing forage availability and selection by both migrant and nonmigratory resident elk (Cervus elaphus) during three growing seasons from 2002–2004. First, we confirmed that the expected trade‐off between forage quality and quantity occurred across vegetation communities. Next, we modeled forage biomass and phenology during the growing season by combining ground and remote‐sensing approaches. The growing season started 2.2 days earlier every 1 km east of the continental divide, was delayed by 50 days for every 1000‐m increase in elevation, and occurred 8 days earlier on south aspects. Migrant and resident selection for forage biomass was then compared across three spatial scales (across the study area, within summer home ranges, and along movement paths) using VHF and GPS telemetry locations from 119 female elk. Migrant home ranges occurred closer to the continental divide in areas of higher topographical diversity, resulting in migrants consistently selecting for intermediate biomass at the two largest scales, but not at the finest scale along movement paths. In contrast, residents selected maximum forage biomass across all spatial scales. To evaluate the consequences of selection, we compared exposure at telemetry locations of migrant and resident elk to expected forage biomass and digestibility. The expected digestibility for migrant elk in summer was 6.5% higher than for residents, which was corroborated with higher fecal nitrogen levels for migrants. The observed differences in digestibility should increase migrant elk body mass, pregnancy rates, and adult and calf survival rates. Whether bottom‐up effects of improved forage quality are realized will ultimately depend on trade‐offs between forage and predation. Nevertheless, this study provides comprehensive evidence that montane ungulate migration leads to greater access to higher‐quality forage relative to nonmigratory congeners, as predicted by the forage maturation hypothesis, resulting primarily from large‐scale selection patterns.

Modern advances in cloud computing and machine-leaning algorithms are shifting the manner in which Earth-observation (EO) data are used for environmental monitoring, particularly as we settle into the era of free, open-access satellite data streams. Wetland delineation represents a particularly worthy application of this emerging research trend, since wetlands are an ecologically important yet chronically under-represented component of contemporary mapping and monitoring programs, particularly at the regional and national levels. Exploiting Google Earth Engine and R Statistical software, we developed a workflow for predicting the probability of wetland occurrence using a boosted regression tree machine-learning framework applied to digital topographic and EO data. Working in a 13,700 km² study area in northern Alberta, our best models produced excellent results, with AUC (area under the receiver-operator characteristic curve) values of 0.898 and explained-deviance values of 0.708. Our results demonstrate the central role of high-quality topographic variables for modeling wetland distribution at regional scales. Including optical and/or radar variables into the workflow substantially improved model performance, though optical data performed slightly better. Converting our wetland probability-of-occurrence model into a binary Wet-Dry classification yielded an overall accuracy of 85%, which is virtually identical to that derived from the Alberta Merged Wetland Inventory (AMWI): the contemporary inventory used by the Government of Alberta. However, our workflow contains several key advantages over that used to produce the AMWI, and provides a scalable foundation for province-wide monitoring initiatives.

Multi‐scale resource selection modeling is used to identify factors that limit species distributions across scales of space and time. This multi‐scale nature of habitat suitability complicates the translation of inferences to single, spatial depictions of habitat required for conservation of species. We estimated resource selection functions (RSFs) across three scales for a threatened ungulate, woodland caribou (Rangifer tarandus caribou), with two objectives: (1) to infer the relative effects of two forms of anthropogenic disturbance (forestry and linear features) on woodland caribou distributions at multiple scales and (2) to estimate scale‐integrated resource selection functions (SRSFs) that synthesize results across scales for management‐oriented habitat suitability mapping. We found a previously undocumented scale‐specific switch in woodland caribou response to two forms of anthropogenic disturbance. Caribou avoided forestry cut‐blocks at broad scales according to first‐ and second‐order RSFs and avoided linear features at fine scales according to third‐order RSFs, corroborating predictions developed according to predator‐mediated effects of each disturbance type. Additionally, a single SRSF validated as well as each of three single‐scale RSFs when estimating habitat suitability across three different spatial scales of prediction. We demonstrate that a single SRSF can be applied to predict relative habitat suitability at both local and landscape scales in support of critical habitat identification and species recovery.