Habitat selection of resident and non-resident gray wolves: implications for habitat connectivity.

Habitat selection studies facilitate assessing and predicting species distributions and habitat connectivity, but habitat selection can vary temporally and among individuals, which is often ignored. We used GPS telemetry data from 96 Gray wolves (Canis lupus) in the western Great Lakes region of the USA to assess differences in habitat selection while wolves exhibited resident (territorial) or non-resident (dispersing or floating) movements and discuss implications for habitat connectivity. We used a step-selection function (SSF) to assess habitat selection by wolves exhibiting resident or non-resident movements, and modeled circuit connectivity throughout the western Great Lakes region. Wolves selected for natural land cover and against areas with high road densities, with no differences in selection among wolves when resident, dispersing, or floating. Similar habitat selection between resident and non-resident wolves may be due to similarity in environmental conditions, when non-resident movements occur largely within established wolf range rather than near the periphery or beyond the species range. Alternatively, non-resident wolves may travel through occupied territories because higher food availability or lower human disturbance outweighs risks posed by conspecifics. Finally, an absence of differences in habitat selection between resident and non-resident wolf movements may be due to other unknown reasons. We recommend considering context-dependency when evaluating differences in movements and habitat use between resident and non-resident individuals. Our results also provide independent validation of a previous species distribution model and connectivity analysis suggesting most potential wolf habitat in the western Great Lakes region is occupied, with limited connectivity to unoccupied habitat.

A chemical prioritization process: Applications to contaminants of emerging concern in freshwater ecosystems (Phase I).

Contaminants of emerging concern (CECs), such as pharmaceuticals, personal care products, and hormones, are frequently found in aquatic ecosystems around the world. Information on sublethal effects from exposure to commonly detected concentrations of CECs is lacking and the limited availability of toxicity data makes it difficult to interpret the biological significance of occurrence data. However, the ability to evaluate the effects of CECs on aquatic ecosystems is growing in importance, as detection frequency increases. The goal of this study was to prioritize the chemical hazards of 117 CECs detected in subsistence species and freshwater ecosystems on the Grand Portage Indian Reservation and adjacent 1854 Ceded Territory in Minnesota, USA. To prioritize CECs for management actions, we adapted Minnesota Pollution Control Agency's Aquatic Toxicity Profiles framework, a tool for the rapid assessment of contaminants to cause adverse effects on aquatic life by incorporating chemical-specific information. This study aimed to 1) perform a rapid-screening assessment and prioritization of detected CECs based on their potential environmental hazard; 2) identify waterbodies in the study region that contain high priority CECs; and 3) inform future monitoring, assessment, and potential remediation in the study region. In water samples alone, 50 CECs were deemed high priority. Twenty-one CECs were high priority among sediment samples and seven CECs were high priority in fish samples. Azithromycin, DEET, diphenhydramine, fluoxetine, miconazole, and verapamil were high priority in all three media. Due to the presence of high priority CECs throughout the study region, we recommend future monitoring of particular CECs based on the prioritization method used here. We present an application of a chemical hazard prioritization process and identify areas where the framework may be adapted to meet the objectives of other management-related assessments.

Anthropogenic factors associated with contaminants of emerging concern detected in inland Minnesota lakes (Phase II).

Contaminants of emerging concern (CECs) include a variety of pharmaceuticals, personal care products, and hormones commonly detected in surface waters. Human activities, such as wastewater treatment and discharge, contribute to the distribution of CECs in water, but other sources and pathways are less frequently examined. This study aimed to identify anthropogenic activities and environmental characteristics associated with the presence of CECs, previously determined to be of high priority for further research and mitigation, in rural inland lakes in northeastern Minnesota, United States. The setting for this study consisted of 21 lakes located within both the Grand Portage Indian Reservation and the 1854 Ceded Territory, where subsistence hunting and fishing are important to the cultural heritage of the indigenous community. We used data pertaining to numbers of buildings, healthcare facilities, wastewater treatment plants, impervious surfaces, and wetlands within defined areas surrounding the lakes as potential predictors of the detection of high priority CECs in water, sediment, and fish. Separate models were run for each contaminant detected in each sample media. We used least absolute shrinkage and selection operator (LASSO) models to account for both predictor selection and parameter estimation for CEC detection. Across contaminants and sample media, the percentage of impervious surface was consistently positively associated with CEC detection. Number of buildings in the surrounding area was often negatively associated with CEC detection, though nonsignificant. Surrounding population, presence of wastewater treatment facilities, and percentage of wetlands in surrounding areas were positively, but inconsistently, associated with CECs, while catchment area and healthcare centers were generally not associated. The results of this study highlight human activities and environmental characteristics associated with CEC presence in a rural area, informing future work regarding specific sources and transport pathways. We also demonstrate the utility of LASSO modeling in the identification of these important relationships.