DOI: 10.5061/dryad.q83bk3jqv
title: Seasonality in functional connectivity: A case study with the American marten in Forillon National Park

description: Protected areas are essential tools for reducing global biodiversity loss.
 To fulfill their ecological functions, protected areas must be connected
 to their surroundings, a requirement that is difficult to meet in
 landscapes intensively disturbed by anthropogenic activities. Therefore,
 protecting movement corridors at the edges of protected areas is crucial,
 especially for species with broad habitat needs, such as the American
 marten (Martes americana). However, habitat selection and space use
 patterns are dynamic processes, so we could expect that functional
 connectivity would vary temporally in response to changing environmental
 conditions and levels of human activities. In this study, we aimed to
 predict the location of movement corridors for the American marten in
 Forillon National Park and its periphery during two contrasted periods
 (snow-free: May–November; snow-covered: December–April). We used
 “seasonal” resource selection functions to identify core areas
 (interpreted as circuit “nodes”) and CircuitScape to delineate movement
 corridors between them based on the electrical circuit theory. Habitat
 selection patterns differed between periods, with martens avoiding open
 areas, high elevations, and road proximity during the snow-free period,
 while avoiding areas closer to secondary roads but selecting areas closer
 to primary roads and housing structures during the snow-covered period.
 Consequently, the location of movement corridors differed partially
 between periods; functional connectivity was favored by the presence of
 forest patches for both periods, while being constrained by open
 environments, especially during the snow-free period. Our study highlights
 the importance of modeling functional connectivity at fine temporal scales
 to provide movement corridors that fulfill the requirements of a species
 at each period of its annual cycle.
description: We captured American martens from September to December 2020 and
 from October to December 2021 using certified live traps (Tomahawk model
 202, Wisconsin, USA, and Havahart model 1078, Pennsylvania, USA). Captured
 martens were physically restrained using a handling cone and anesthetized
 with an intramuscular injection (rear thigh) of BAM (combination of
 butorphanol, azaperone, and medetomidine; Wildlife Pharmaceuticals, Inc.,
 Windsor, Colorado, USA). Martens weighing ≥ 650 g were fitted with a
 <em>Litetrack 20</em> (Lotek Engineering Inc, Newmarket,
 Ontario, Canada, 21 g) GPS collar programmed to acquire location fixes
 every 6 h for nearly 10 months. From January 2021 to May 2022, we
 collected GPS locations from collared martens through remote data
 download, recaptures, or trapped individuals. We used
 digital maps to obtain habitat covariates that could potentially influence
 the habitat selection and space use patterns of martens.  We also used 1:
 20,000 digital terrain models, and digital maps of all roads, trails, and
 recreational facilities published by the MRNF. We conducted our habitat
 selection analyses using RSF models (Manly et al., 2002) at the
 3<sup>rd</sup> order of selection (<em>sensu
 </em>Johnson, 1980), thus contrasting habitat covariates found at
 the sites used by martens (i.e., GPS locations) with those found at sites
 available in individual home ranges (i.e., random points). To reflect
 seasonal variation in resource selection, we partitioned GPS locations
 into two distinct biological periods: snow-free (from April
 12<sup>th</sup> to November 14<sup>th</sup>) and
 snow-covered (from November 15<sup>th</sup> to April
 11<sup>th</sup>). For each GPS location and random point, we
 extracted a set of habitat covariates, including habitat type, elevation,
 and Euclidean distance to roads and housing structures. We parameterized
 seasonal RSFs using mixed logistic regression models and set individual
 martens as a random factor (i.e., random intercept) to prevent
 pseudoreplication and account for differences in sample sizes (Gillies et
 al., 2006). For both periods, we used the coefficients of the highest
 ranked RSF model to calculate, following Boyce et al. (2002; see equation
 2), and map the relative occurrence probability over the entire study
 area. We used CircuitScape (v. 4.0.5; McRae et al.,
 2008) to conduct our two functional connectivity analyses (one per
 period). CircuitScape needs two rasters to operate: the first indicating
 the core areas to be connected and the second representing landscape
 resistance to animal movement. We used our previously created maps of
 relative occurrence probability to develop our rasters (pixels 50 m × 50
 m) of core areas and landscape resistance. For both periods, core areas
 were determined by selecting the 20% highest ranked habitats (i.e., pixels
 with a relative occurrence probability ≥ 80%) that had a minimum area of
 1 km<sup>2</sup>, to maintain suitable habitat patches as well
 as stepping stones. We thus assumed that pixels with a higher relative
 occurrence probability offered lower costs to travel than those with a
 lower relative occurrence probability (Beier et al., 2008). Following
 Finch et al. (2020), we defined functional connectivity corridors as
 contiguous pixels with a high current density (i.e., upper quartile, ≥75%
 of the maximum value) connecting at least two core areas of suitable
 habitat quality (with a relative occurrence probability ≥ 80%).
description: # Seasonality in functional connectivity: A case study with the American
 marten in Forillon National Park Note: This README file is the same for
 the two datasets that represent different seasonal periods (summer and
 winter). Description of column names: * presence: Dummy variable
 indicating it is a GPS relocation (1) or a random point (0) * ID: Animal
 identifier (GPS collar #) * DATE: GPS location date (randomly attributed
 to random points) * HABITAT: Land cover type under the GPS relocation or
 the random point * CONIFER: Dummy variable for the &gt; 60-year-old
 coniferous stands * MIX: Dummy variable for the &gt; 60-year-old mixed and
 deciduous stands * YOUNG: Dummy variable for the 21–60-year-old
 regenerating stands * OPEN: Dummy variable for the open habitats * LAT:
 Latitude (in decimal degrees) * LONG: Longitude (in decimal degrees) *
 ELEV: Elevation (in m) * ROAD1_eucldist: Euclidian distance to a primary
 road (in m) * ROAD1_decay50: Decay distance to a primary road, alpha value
 of = 50 * ROAD1_decay100: Decay distance to a primary road, alpha value of
 = 100 * ROAD1_decay250: Decay distance to a primary road, alpha value of =
 250 * ROAD1_decay500: Decay distance to a primary road, alpha value of =
 500 * ROAD1_decay1000: Decay distance to a primary road, alpha value of =
 1000 * ROAD1_decay1500: Decay distance to a primary road, alpha value of =
 1500 * ROAD1_decay2000: Decay distance to a primary road, alpha value of =
 2000 * ROAD2_eucldist: Euclidian distance to a secondary road (in m) *
 ROAD2_decay50: Decay distance to a secondary road, alpha value of = 50 *
 ROAD2_decay100: Decay distance to a secondary road, alpha value of = 100 *
 ROAD2_decay250: Decay distance to a secondary road, alpha value of = 250 *
 ROAD2_decay500: Decay distance to a secondary road, alpha value of = 500 *
 ROAD2_decay1000: Decay distance to a secondary road, alpha value of = 1000
 * ROAD2_decay1500: Decay distance to a secondary road, alpha value of =
 1500 * ROAD2_decay2000: Decay distance to a secondary road, alpha value of
 = 2000 * HOUSING_eucldist: Euclidian distance to a housing structure (in
 m) * HOUSING_decay50: Decay distance to a housing structure, alpha value
 of = 50 * HOUSING_decay100: Decay distance to a housing structure, alpha
 value of = 100 * HOUSING_decay250: Decay distance to a housing structure,
 alpha value of = 250 * HOUSING_decay500: Decay distance to a housing
 structure, alpha value of = 500 * HOUSING_decay1000: Decay distance to a
 housing structure, alpha value of = 1000 * HOUSING_decay1500: Decay
 distance to a housing structure, alpha value of = 1500 *
 HOUSING_decay2000: Decay distance to a housing structure, alpha value of =
 2000 * ELEV_km: Elevation (in km) * ROAD1_eucldist_km: Euclidian distance
 to a primary road (in km) * ROAD2_eucldist_km: Euclidian distance to a
 secondary road (in km) * HOUSING_eucldist_km: Euclidian distance to a
 housing structure (in km)