Disturbance‐mediated changes to boreal mammal spatial networks in industrializing landscapes

Funding: InnoTech Alberta. Grant Number: C2021000986; Alberta Innovates; Alberta Conservation Association; Petroleum Technology Alliance Canada. Grant Numbers: 17-ERPC-02, 18-ERPC-01, 19-ERPC-04; Algar Caribou Habitat Restoration Program. Grant Number: NXC-107980; Oil Sands Monitoring program; Natural Sciences and Engineering Research Council of Canada. Grant Numbers: RGPIN-2018–03958, Canada Research Chairs.Compound effects of anthropogenic disturbances on wildlife emerge through a complex network of direct responses and species interactions. Land‐use changes driven by energy and forestry industries are known to disrupt predator–prey dynamics in boreal ecosystems, yet how these disturbance effects propagate across mammal communities remains uncertain. Using structural equation modeling, we tested disturbance‐mediated pathways governing the spatial structure of multipredator multiprey boreal mammal networks across a landscape‐scale disturbance gradient within Canada's Athabasca oil sands region. Linear disturbances had pervasive direct effects, increasing site use for all focal species, except black bears and threatened caribou, in at least one landscape. Conversely, block (polygonal) disturbance effects were negative but less common. Indirect disturbance effects were widespread and mediated by caribou avoidance of wolves, tracking of primary prey by subordinate predators, and intraguild dependencies among predators and large prey. Context‐dependent responses to linear disturbances were most common among prey and within the landscape with intermediate disturbance. Our research suggests that industrial disturbances directly affect a suite of boreal mammals by altering forage availability and movement, leading to indirect effects across a range of interacting predators and prey, including the keystone snowshoe hare. The complexity of network‐level direct and indirect disturbance effects reinforces calls for increased investment in addressing habitat degradation as the root cause of threatened species declines and broader ecosystem change.Peer reviewe

Effects of scent lure on camera trap detections vary across mammalian predator and prey species.

Camera traps are a unique survey tool used to monitor a wide variety of mammal species. Camera trap (CT) data can be used to estimate animal distribution, density, and behaviour. Attractants, such as scent lures, are often used in an effort to increase CT detections; however, the degree which the effects of attractants vary across species is not well understood. We investigated the effects of scent lure on mammal detections by comparing detection rates between 404 lured and 440 unlured CT stations sampled in Alberta, Canada over 120 day survey periods between February and August in 2015 and 2016. We used zero-inflated negative binomial generalized linear mixed models to test the effect of lure on detection rates for a) all mammals, b) six functional groups (all predator species, all prey, large carnivores, small carnivores, small mammals, ungulates), and c) four varied species of management interest (fisher, Pekania pennanti; gray wolf, Canis lupus; moose, Alces alces; and Richardson's ground squirrel; Urocitellus richardsonii). Mammals were detected at 800 of the 844 CTs, with nearly equal numbers of total detections at CTs with (7110) and without (7530) lure, and variable effects of lure on groups and individual species. Scent lure significantly increased detections of predators as a group, including large and small carnivore sub-groups and fisher specifically, but not of gray wolf. There was no effect of scent lure on detections of prey species, including the small mammal and ungulate sub-groups and moose and Richardson's ground squirrel specifically. We recommend that researchers explicitly consider the variable effects of scent lure on CT detections across species when designing, interpreting, or comparing multi-species surveys. Additional research is needed to further quantify variation in species responses to scent lures and other attractants, and to elucidate the effect of attractants on community-level inferences from camera trap surveys

Fisher Camera Detections

Unmarked fisher camera detection dat

Encounter Data File (EDF)

Individual encounter histories for SCR models

Estimating density for species conservation: Comparing camera trap spatial count models to genetic spatial capture-recapture models

Density estimation is integral to the effective conservation and management of wildlife. Camera traps in conjunction with spatial capture-recapture (SCR) models have been used to accurately and precisely estimate densities of “marked” wildlife populations comprising identifiable individuals. The emergence of spatial count (SC) models holds promise for cost-effective density estimation of “unmarked” wildlife populations when individuals are not identifiable. We evaluated model agreement, precision, and survey costs, between i) a fully marked approach using SCR models fit using non-invasive genetic data, and ii) an unmarked approach using SC models fit using camera trap data, for a recovering population of the mesocarnivore fisher (Pekania pennanti). The SCR density estimates ranged from 2.95 to 3.42 (2.18–5.19 95% BCI) fishers 100 km−2. The SC density estimates were influenced by their priors, ranging from 0.95 (0.65–2.95 95% BCI) fishers 100 km−2 for the uninformative model to 3.60 (2.01–7.55 95% BCI) fishers 100 km−2 for the model informed by prior knowledge of a 16 km2 fisher home range. We caution against using strongly informative priors but instead recommend using a range of unweighted prior knowledge. Thin detection data was problematic for both SCR and SC models, potentially producing biased low estimates. The total cost of the genetic survey ($47 610) was two-thirds of the camera trap survey ($77 080), or comparable ($75 746) if genetic sampling effort was increased to include sex and trap-behaviour covariates in SCR models. Density estimation of unmarked populations continues to be a series of trade-offs but as methods improve and integrate, so will our estimates. Keywords: Bayesian estimation, Camera trap surveys, Cost-effectiveness, Non-invasive genetic sampling, Pekania pennanti, Population monitoring, Wildlife conservatio

Data from: Estimating density for species conservation: comparing camera trap spatial count models to genetic spatial capture-recapture models

Density estimation is integral to the effective conservation and management of wildlife. Camera traps in conjunction with spatial capture-recapture (SCR) models have been used to accurately and precisely estimate densities of “marked” wildlife populations comprising identifiable individuals. The emergence of spatial count (SC) models holds promise for cost-effective density estimation of “unmarked” wildlife populations when individuals are not identifiable. We evaluated model agreement, precision, and survey costs, between i) a fully marked approach using SCR models fit using non-invasive genetic data, and ii) an unmarked approach using SC models fit using camera trap data, for a recovering population of the mesocarnivore fisher (Pekania pennanti). The SCR density estimates ranged from 2.95 to 3.42 (2.18–5.19 95% BCI) fishers 100 km−2. The SC density estimates were influenced by their priors, ranging from 0.95 (0.65–2.95 95% BCI) fishers 100 km−2 for the uninformative model to 3.60 (2.01–7.55 95% BCI) fishers 100 km−2 for the model informed by prior knowledge of a 16 km2 fisher home range. We caution against using strongly informative priors but instead recommend using a range of unweighted prior knowledge. Thin detection data was problematic for both SCR and SC models, potentially producing biased low estimates. The total cost of the genetic survey ($47 610) was two-thirds of the camera trap survey ($77 080), or comparable ($75 746) if genetic sampling effort was increased to include sex and trap-behaviour covariates in SCR models. Density estimation of unmarked populations continues to be a series of trade-offs but as methods improve and integrate, so will our estimates

Data from: Who’s for dinner? High-throughput sequencing reveals bat diet differentiation in a biodiversity hotspot where prey taxonomy is largely undescribed

Effective management and conservation of biodiversity requires understanding of predator–prey relationships to ensure the continued existence of both predator and prey populations. Gathering dietary data from predatory species, such as insectivorous bats, often presents logistical challenges, further exacerbated in biodiversity hot spots because prey items are highly speciose, yet their taxonomy is largely undescribed. We used high-throughput sequencing (HTS) and bioinformatic analyses to phylogenetically group DNA sequences into molecular operational taxonomic units (MOTUs) to examine predator–prey dynamics of three sympatric insectivorous bat species in the biodiversity hotspot of south-western Australia. We could only assign between 4% and 20% of MOTUs to known genera or species, depending on the method used, underscoring the importance of examining dietary diversity irrespective of taxonomic knowledge in areas lacking a comprehensive genetic reference database. MOTU analysis confirmed that resource partitioning occurred, with dietary divergence positively related to the ecomorphological divergence of the three bat species. We predicted that bat species' diets would converge during times of high energetic requirements, that is, the maternity season for females and the mating season for males. There was an interactive effect of season on female, but not male, bat species' diets, although small sample sizes may have limited our findings. Contrary to our predictions, females of two ecomorphologically similar species showed dietary convergence during the mating season rather than the maternity season. HTS-based approaches can help elucidate complex predator–prey relationships in highly speciose regions, which should facilitate the conservation of biodiversity in genetically uncharacterized areas, such as biodiversity hotspots

Data and Code for Curveira-Santos et al. "Disturbance-mediated changes to boreal mammal spatial networks in industrializing landscapes"

Data and model code used in Curveira-Santos et al. “Disturbance-mediated changes to boreal mammal spatial networks in industrializing landscapes”. i. Data: “sem_dat.csv”  Species detection and covariate data from camera-trap surveys conducted across a three-area disturbance gradient (Richardson [low disturbance], Algar [medium disturbance], Christina lake [high disturbance]) within Canada’s Athabasca Oil Sands Region. Rows represent sampling sites (site [camera-trap] by year and by landscape combinations). Columns represent species-specific site use intensity data (independent counts at sampling sites) and covariate values for on-/off-line camera placement (on-line), linear feature density (lf1500), block feature cover (bf1500), and wetland probability (wb500). ii. Model Code: “SEM_Rcode.R” Code written for program R and using the piecewiseSEM and lme4 packages to implement the Structural Equation Model. </p