DOI: 10.5061/dryad.4b8gthtdc
title: Integrating socio-ecological suitability with human-wildlife conflict risk: Case study for translocation of a large ungulate

description: 1. Translocations are essential for reestablishing wildlife populations.
 As they sometimes fail, it is critical to assess factors that influence
 their success pre-translocation. 2. Socio-ecological suitability models
 (SESMs) integrate social acceptance and ecological suitability to enable
 identification of areas where wildlife populations will expand, which
 makes it likely that SESMs will also be useful for predicting
 translocation success. 3. To inform site-selection for potential elk
 (Cervus canadensis) reintroduction to northeastern Minnesota, USA, we
 developed broad-scale maps of social acceptance from surveys of local
 residents and landowners, animal use equivalence (AUE) from forage
 measured in the field, and empirical conflict risk from geospatial data.
 Resulting SESMs integrated social acceptance favorability scores, AUE, and
 conflict risk, and weighted SESMs showed the relative influences of
 acceptance and conflict. 4. Social acceptance was positive for local
 residents and landowners (mean ≥ 5.4; scale of 1 to 7). AUE (scaled to an
 elk home range) ranged between 1 and 9 elk/16 km2 during winter, and from
 14 to 83 elk/16 km2 during summer. Human-elk conflict risk was low (mean ≤
 0.10; scaled 0 to 1), increasing from north to south. Geographical
 distributions differed for social acceptance, AUE, and conflict risk, and
 weighted SESMs revealed unsuitable areas that were otherwise obscured. 5.
 Synthesis and applications. Integrating human-wildlife conflict risk into
 SESMs shows where social acceptance of translocated species is likely to
 erode, even where viewed favorably pre-translocation, to inform
 translocation planning by highlighting interactions between key factors.
 Such integrated models supplement existing reintroduction biology
 frameworks by supporting decision-making and knowledge development. In
 northeastern Minnesota, natural resource managers who are considering elk
 reintroductions are using SESMs reported here to identify where human-elk
 conflict is likely to result in an isolated elk population and where
 addressing concerns for area residents about conflict risk is essential.
description: 2.1  | 
 Study area We studied elk reintroduction
 to northeastern Minnesota, USA, where managers are deciding whether to
 reintroduce elk to 1 of 3 study areas (Cloquet Valley [CLV], Fond du Lac
 [FDL], and Nemadji [NEM]) that have abundant public land and low road
 density (mean = 0.96 km/km<sup>2</sup>) suitable for elk (Lyon
 1983; Fig. 1A). In the northern lakes and forests ecoregion (Level III
 Region 50), the area was characterized by rolling topography,
 nutrient-poor soils, scattered lakes and rivers (Omernik &
 Griffith, 2014), and mixed-plant vegetative communities. Forests were
 coniferous and northern hardwood (often mixed). Maps we developed buffered
 study areas 20 km (elk
 dispersal distance; Ryckman et al.
 2010). 2.2  | 
 Social acceptance We used local resident
 and landowner surveys to map social acceptance for elk. We mailed 4,000
 local residents a questionnaire (stratified random sampling; stratum
 levels were census blocks, highways, and rivers; see Walberg et al. 2019)
 about attitudes toward translocating elk and measured attitudes using
 returned questionnaires (<i>N</i> = 1,521) scored from very
 unfavorable (1) to very favorable (7). To develop a social acceptance
 model, we mapped mean attitude scores within a circular moving window
 (size needed for continuous surface; 4 townships [372
 km<sup>2</sup>]). We used the same methods
 for landowners (owners of ≥ 4 ha properties located ≤ 8 km from a study
 area). Landowner properties were near likely reintroduction sites (outside
 residential areas and near public lands), making them areas where
 post-reintroduction human-elk interactions were most likely. We used a
 stratified random sampling approach to select 4,500 landowners, where
 stratum was property size (levels: 4-16 ha and &gt;16 ha). Mapped
 scores were from 2,585 returned questionnaires (including 35 from local
 residents who met landowner criteria). 2.3  | 
 Forage We measured trees, shrubs, and
 understory vegetation between 14 June and 8 August 2017 (season 1) and 6
 June and 8 August 2018 (season 2). Sites we sampled were distributed
 throughout the study areas in a Geographic Information System (ESRI 2018,
 R Core Team 2019; Any use of trade, firm, or product names is for
 descriptive purposes only and does not imply endorsement by the U.S.
 Government). During season 1 we randomly distributed points on roads that
 abutted public land, and then randomly sampled 1 point in each vegetated
 landcover type (Rampi, Knight, & Bauer, 2016) within 50 to 500 m
 from the road point (to improve logistics). To achieve sampling that was
 even through space (with respect to roads) and time (date), we
 systematically sampled study areas and 4 rectangular quadrants overlaid on
 them. We repeated this process during season 2 while sampling private
 properties from landowner questionnaire respondents. As elk in eastern
 North America select regenerating aspen (<i>Populus
 tremuloides</i>) as winter forage (D. A. Jenkins et al., 2007) we
 expanded sampling to include randomly selected points in aspen stands
 harvested ≤ 10 years ago (<i>N</i> = 4 plots per study area;
 M.P. Westphal, Carlton County; D. Ryan, US Forest Service; J. Kelash, Pine
 County; and B. Hakala, Saint Louis County;<i> Unpublished
 data</i>). We
 established a circular plot (field plot) centered on each landcover point.
 Field plots consisted of nested circles (Fig. 1B). In the largest circle
 (<i>r</i> =11.3 m) we collected ground cover vegetation from
 10 150 cm × 10 cm quadrats. In each quadrat we clipped, dried, and massed
 woody (height &lt; 15 cm) and herbaceous vegetation (classes: grass,
 forb, sedge, rush, fern, and woody). In 3 medium circles
 (<i>r</i> = 2.8 m) radiating 5.5 m from the plot center (120°
 increments) we used a stepped diameter gauge (Paul et al., 2017) to count
 trees and shrubs with DBH 2.54 cm to 10 cm. In a small circle
 (<i>r</i> = 1.8 m) centered within each medium circle we used
 a stepped gauge to count trees and shrubs (≥ 15 cm tall and &lt; 2.54
 cm diameter at 15 cm height; D15). During season 2 we sampled open
 areas near roads, railroad tracks, and pipelines (right-of-ways; ROWs). We
 used a stratified random sampling approach to select ROWs; strata were
 study area and road class (MNDOT, 2017). Railroad and pipeline ROWs were
 adjacent to tracks and pipeline service roads, and 50 to 500 m (randomly
 selected) from a road intersection (MNDOT, 2015). At each ROW, we
 established a 200 m<sup>2</sup> rectangular plot (ROW plot).
 We measured the distance between the road/railroad edge and the nearest
 tree/shrub line to calculate plot width (maximum of 30 m). Plot length was
 plot width divided by 200. Once we established a ROW plot, we clipped,
 dried, and massed ground cover vegetation from 5 quadrats. We estimated forage biomass
 (non-avoided species) at each field plot during leaf-on (summer) and
 leaf-off (winter) because it correlates positively with elk habitat
 selection across multiple spatial scales (Anderson et al., 2005;
 Hebblewhite et al., 2008; Merrill et al., 2020). Summer forage was
 deciduous shrub and tree leaves, forbs, and grasses (not sedges, rushes,
 and ferns; Schneider et al. 2006, Lupardus et al. 2011). To estimate shrub
 and sapling leaf biomass, we summed woody stem counts at field plots and
 used diameter-specific allometric equations (J. C. Jenkins, Chojnacky,
 Heath, & Birdsey, 2004; Perala & Alban, 1993; Smith
 & Brand, 1983). Winter forage was deciduous twigs (not grasses;
 see Jenkins et al. 2007). We used allometric equations to estimate total
 above ground deciduous shrub and tree biomass (J. C. Jenkins et al., 2004;
 Perala & Alban, 1993; Smith & Brand, 1983) and estimated
 twig biomass as the product of total biomass and the proportion that is
 current year growth consumed by ungulates in Minnesota (0.07; Ohmann et
 al. 1974, 1976). Forage biomass was for shrubs and trees ≤ 2.54 cm in
 diameter (D15), corresponding with mean elk foraging height (1.5 m; Rounds
 2006, Gehring et al. 2008, VanderSchaaf 2013). We used ANOVAs to determine
 if forage (square root transformed) differed by landcover and ownership
 (public or private; stats package in R) and to test for differences in ROW
 type summer forge. Tukey tests followed significant ANOVAs, α was 0.05,
 and the variance inflation factor assessed collinearity. We used random forest (RF) analysis
 to model field plot forage biomass (Breiman, 2001; Cutler et al., 2007).
 RF fits a large number of regression trees, with each tree constructed
 using a random subset of data and predictors. This results in estimates
 that are essentially cross-validated, making it unnecessary to divide data
 into training and test sets (Chen et al., 2017; Prasad, Iverson, &
 Liaw, 2006). RF results in predictions of the dependent variable, and
 measures of accuracy and variable importance (Liaw & Wiener,
 2002). Frequently used in geospatial modeling (Evans & Cushman,
 2009; Rodriguez-Galiano, Ghimire, Rogan, Chica-Olmo, &
 Rigol-Sanchez, 2012), RF models nonlinear relationships and interactions
 without error distribution assumptions (Cutler et al., 2007), and is
 robust to missing data (Rodriguez-Galiano et al., 2012) and overfitting
 (Breiman, 2001), yielding more accurate results than other methods (Chen
 et al., 2017; Prasad et al., 2006). To estimate field plot forage
 biomass, we implemented RF (randomForest package; Liaw and Wiener 2002)
 using biologically relevant predictors extracted from 15 m resolution
 rasters (Table 1). We eliminated correlated independent variables (Millard
 and Richardson 2015;
 Spearman correlation coefficient
 |<i>r<sub>s</sub></i>| &gt; 0.5; stats package
 in R) by keeping only the variable that resulted in greater
 predictive accuracy (Gustafson, Lietz, Wright, & Ecologist, 2003).
 RF models predicted leaf or total biomass by growing 1,000 regression
 trees. Using RF forage estimates (not spatially autocorrelated; Moran’s I test,
 <i>P</i> &gt; 0.25; spdep package in R; Millard and Richardson 2015, Bivand
 and Wong 2018), we estimated summer and winter forage across
 the mapped area (Hijmans, 2019). ROW forage was overlaid after
 assigning mean forb and grass biomass (field measurements).
 Mean pipeline forage was used for unsampled high voltage power lines
 (similarly sized openings and vegetation; Minnesota Geospatial Information
 Office 2016). 2.4  | 
 Animal unit equivalence To estimate AUE we used RF forage
 estimates summed within 16 km<sup>2</sup>
 circular moving windows to match an elk home range (O’Neil & Bump,
 2014), using: AUE =
 (<i>F</i> × <i>C</i>) / (<i>S</i> ×
 <i>M</i> × <i>D</i>); where
 <i>F</i> was forage, <i>S</i> was dry forage (as %
 elk mass) required to sustain 1 elk of mass <i>M</i> for 1 day
 during a season lasting <i>D</i> days, and
 <i>C</i> was a correction factor reflecting how much forage
 elk consume. AUE was for cow elk (<i>M </i>= 250 kg; Bender et
 al. 2006), consuming 2.1% of <i>M</i> daily
 (Christianson & Creel, 2009) during a 200 d winter, and
 2.2% of <i>M</i> daily (Kuzyk & Hudson, 2007) during a
 165 d summer. To account
 for shrubs and trees not consumed within their use areas, we
 estimated that elk consume the same proportion of available forage as do
 moose (<i>Alces</i>; <i>C</i> = 0.03 of forage;
 Peek et al. 1976, Edenius et al. 2002). Resulting maps
 estimated elk supported by the surrounding 16
 km<sup>2</sup>, a scale that is biologically
 significant to elk (home range; (O’Neil & Bump,
 2014). We reported the mean and standard
 deviation (<i>SD</i>) of raster map values in each study area
 but did not develop statistical comparisons as the large number of raster
 cells (CLV: 7,841,931 cells; FDL: 3,402,931 cells;  NEM:
 4,279,849 cells), made
 <i>P</i>-values uninformative (Lin, Lucas, & Shmueli,
 2013). 2.5  | 
 Human-elk conflict risk To estimate
 empirical human-elk conflict risk we developed a surface reflecting the
 proportion of area (16 km<sup>2</sup> circular window) that
 was the sum of row crop, hay/pasture, feedlot, and road.
 Surface values were expected probabilities of elk traversing
 these features. Crop and hay/pasture were from landcover data (Rampi et
 al., 2016). Road surface was centerlines (MNDOT, 2017) buffered by mean
 road widths (field measurements by class). We estimated feedlot area by
 buffering cow, horse, and pig feedlot locations by 0.12
 km<sup>2</sup> (median of 20 random feedlots; MPCA 2007) and
 added all 4 captive cervid operations within the mapped area (Minnesota
 Board of Animal Health, <i>unpublished data</i>). Nearly all
 923 feedlots had areas where elsewhere in elk range livestock-elk contact
 occurs and elk raid forage (98% had open lots and pasture and 84% had
 holding areas; MPCA 2007). 2.6  | 
 Socio-ecological suitability To develop
 integrated socio-ecological suitability maps we calculated mean AUE
 (summer and winter, weighted equally), social acceptance (residents and
 landowners, weighted equally), and empirical conflict risk after
 normalizing map values between 0 and 1 (min-max scalar). We subtracted
 conflict risk from 1 before normalization to ensure all values
 corresponded positively with suitability. The resulting map weighted AUE,
 acceptance, and conflict equally. To assess their relative influence, we
 developed maps that weighted acceptance and conflict (each weighted 2- and
 3-times baseline map value). Using continuous variables enabled us to
 assess an array of interacting conditions, thereby obviating
 potentially-influential thresholds where continuous data are discretized
 into categorical datasets (Behr et al., 2017).