Of goats and heat, the differential impact of summer temperature on habitat selection and activity patterns in mountain goats (Oreamnos americanus) of different ecotypes

doi:10.21203/rs.3.rs-3398323/v1

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Of goats and heat, the differential impact of summer temperature on habitat selection and activity patterns in mountain goats (Oreamnos americanus) of different ecotypes

https://doi.org/10.21203/rs.3.rs-3398323/v1

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Climate change disproportionately affects northern and alpine environments, with faster rates of warming than the global average. In this context, understanding the effect of projected temperature increase on the species inhabiting these ecosystems is essential. Most alpine and northern ungulates are particularly well adapted to low temperatures and must modify their behaviour when temperatures exceed a critical threshold. We analyzed the influence of summer weather conditions, particularly temperature, on the activity patterns and habitat selection of four populations of a mountain specialist, the mountain goat (Oreamnos americanus), representing the two ecotypes found in this species - coastal and continental. We collected GPS location and activity sensor data during 2010-2019 from 223 mountain goats in four different study areas in both the coastal range of Alaska and the Rocky Mountains of Alberta. Mountain goats of each ecotype used different habitat selection tactics to avoid thermal stress. When temperatures increased, mountain goats of both ecotypes decreased selection for alpine meadows, but goats in coastal environments increased selection for open habitats close to snow, while continental goats increased selection for forest environments. With respect to altitude, mountain goats in continental environments selected higher elevation open habitats only when temperature increased. In coastal environments, goats selected higher elevation habitats at all temperatures, but the selection for high elevations was strongest in warm conditions. Mountain goats of both ecotypes reduced the proportion of time spent active when temperatures increased during the middle of the day. Our study shows that mountain goats use diverse tactics that are likely to mitigate thermal stress. Mountain goats living in different ecosystems use alternative tactics. The observed ecotypic variation highlights the importance of assessing the impact of climate change at the population rather than the species level, a pattern that arises because populations are experiencing different environmental constraints.

Climate change

alpine ecosystems

Mountain goats

Oreamnos americanus

temperature

ungulate

Every species has an optimal temperature range, which has minimal thermoregulatory costs in endotherms or allows for an ideal body temperature in ectotherms (Huey and Tewksbury 2009). This ideal temperature range is one of the main drivers limiting the biogeographic range suitable to animals (du Plessis et al. 2012). Climate change causing warmer temperatures globally alters the distribution of ideal temperature ranges, forcing species to shift their distribution and phenology accordingly (Parmesan 2006, Chen et al. 2011). Long-term upslope range shifts driven by temperature increases have been recorded in many montane species but, in numerous cases, range shifts are lagging behind climate change (Chen et al. 2011). Upslope range shift among montane species is not a viable long-term tactic to avoid thermal stress in a warming world. Using higher elevations decreases the temperature experienced by animals (Minder et al. 2010), but the available habitat becomes more fragmented and scarce while its quality generally decreases with increasing elevation (Cannone et al. 2007, Dirnböck et al. 2011, White et al. 2018). Our understanding of long-term altitudinal range shifts is still limited and can be improved by examining small-scale responses of species and populations to environmental cues.

Animals can use several alternative tactics to avoid heat stress, which may allow certain species to remain within an ideal temperature range without altitudinal range shifts. For example, lowering the proportion of time spent active during hot periods, or shifting the timing of activity to dawn and dusk represent tactics used by several taxa to decrease thermal stress (Korstjens et al. 2010, Bourgoin et al. 2011, McLellan and McLellan 2015). Diurnal altitudinal migrations may also occur in alpine ungulates to avoid heat stress during the warmest periods of the day (Aublet et al. 2009, Bourgoin et al. 2011). Ibex (Capra ibex), for example, have been known to shift their altitude range by up to 600 m between hot and cool days (Bourgoin et al. 2011). Use of shaded areas, such as dense canopy forests, has been observed in many heat-sensitive ungulates (van Beest et al. 2012, Melin et al. 2014, Thompson et al. 2021). Physiological tactics such as increased respiration and heart rates can also be used to avoid heat stress (Sarmento et al. 2019, Thompson et al. 2020, Thompson et al. 2021). Other, more specific heat-avoidance tactics, such as the use of perennial snow patches as bedding sites have also been recorded in alpine ungulates (Sarmento et al. 2019).

Behavioural plasticity has the potential to mitigate and buffer the effects of climate change but may only be a stopgap measure. Heat avoidance tactics may represent a trade-off resulting in individuals decreasing time spent foraging, conducting other vital activities, or spending time foraging in less productive or riskier areas (Korstjens et al. 2010). Thus, such behavioural adaptations likely have their limits and may only be viable within a certain range of warming. Once temperatures exceed critical thresholds, behavioural and physiological plasticity may be insufficient leading to thermal stress and severe energetic, physiological and, ultimately, fitness costs (Cunningham et al. 2021). Although general tactics and trends can be observed among several taxa, every species has a different tolerance and response towards warming temperatures. Furthermore, the response of a species to heat stress is not always uniform across its biogeographic range, especially near the fringes of its distribution where temperatures closer to the tolerance limit of the species are expected (Lowe et al. 2010).

In the current context of climate change, understanding how species react to increasing temperatures at a fine temporal and spatial scale, the tactics they use to mitigate thermal stress, and how habitat selection and activity patterns vary across the distribution of a species have become critical. Our study therefore aims to assess how summer weather influences the fine scale habitat selection and activity budgets of mountain goats (Oreamnos americanus). Mountain goats are an endemic species of the coastal and interior mountains of northwestern North America, inhabiting alpine and subalpine environments from Colorado in the south to southern Alaska in the north. Although they are not currently threatened, population declines have been recorded in large parts of their range (Côté and Hamel 2018), and climate change is expected to negatively impact their population dynamics (White et al. 2018). The biogeographic range of mountain goats encompasses both the coastal and interior climatic regions, which has led to the origin of two distinct ecotypes (Herbert and Turnbull 1977). The coastal mountain goat ecotype occurs in areas characterized by mild and wet winters, whereas mountain goats of the interior ecotype live in environments with cold and dry winters (Herbert and Turnbull 1977). Mountain goats in these two climatic regions experience vastly different environmental conditions and utilize different tactics of habitat use during winter, with coastal goats moving to lower-elevation forests and continental goats staying high on ridges and windward slopes (Taylor et al. 2006, Taylor and Brunt 2007, Poole et al. 2009). Differences in summer habitat ecology of these two ecotypes are less understood and, to our knowledge, have not been formally examined using direct comparative analyses.

We first hypothesized that mountain goats would mitigate thermal stress by avoiding exposure to high temperatures. We thus predicted mountain goats would use cool microsites such as high-elevation areas, forested habitats and shaded aspects when temperatures increase. Secondly, we hypothesized that mountain goats would minimize thermal stress by adjusting activity depending on ambient temperature. We predicted that the proportion of time spent active would remain stable until a threshold is reached around 15 °C – a temperature threshold shown to cause thermal stress in another heat-sensitive boreal species [moose (Alces alces, Renecker and Hudson (1986)] and also used in the study of alpine ibex (Aublet et al. 2009) – after which it would decrease. We also predicted that the proportion of time spent active would not change with temperature during the predicted crepuscular activity peaks of mountain goats. Finally, we hypothesized that because of the different landscapes and climate conditions they face, mountain goats in coastal and continental regions would use different heat avoidance tactics. Because of the differences in mountain characteristics and scale, mountain goats in coastal areas use lower elevations relative to the highest summits available to them in comparison with continental goats who occupy mountain tops during summer. Thus, we predicted that mountain goats in coastal areas would use higher elevations to compensate for increasing temperatures, while goats in continental areas would continue to use similar elevations but increase their use of forested areas and shaded aspects when temperatures increased.

Study sites

We studied mountain goats in four study areas (Fig. 1) located in pairs at each end of a continental-coastal gradient in northwest North America. Two sites in the Rocky Mountains interior were located at Caw Ridge and Jasper (Alberta), and two sites strongly influenced by maritime conditions in southeastern Alaska, Baranof Island and Lynn Canal. Caw Ridge (54°N, 119°W) is a 28 km² area located in the eastern foothills of the Rocky Mountains and composed of alpine tundra and open subalpine forest between 1,750 m and 2,170 m above sea level with the treeline at around 1,900 m. The climate is arctic-subarctic, with annual precipitations averaging 742 mm and a mean July temperature of 9.1°C (Mesinger et al. 2006, Festa-Bianchet and Côté 2008). The Jasper National Park study area (53° N, 118° W) is a 125 km² area ranging from 1,000 to 3,000 m in elevation. The site is located in a dry subarctic climate and humid continental climate transition zone, with yearly precipitations averaging 1,064 mm and an average July temperature of 8.5°C (Mesinger et al. 2006, Richard and Côté 2016). Lynn Canal (59°N, 135°W) is a 600 km² area ranging from sea level to 1,920 m and characterized by a coastal climate, with cool, wet summers and snowy winters (treeline: 900-1000 m). At the mean elevation used by mountain goats (1,000 m), annual precipitation averages 1,844 mm and the mean July temperature is 9.0°C (Mesinger et al. 2006, White and Gregovich 2018). The Baranof Island study area (57°N, 135°W) is composed of 1,360 km² of coastal mountains, with elevations ranging from sea level to 1,610 m and a strong maritime influence. The Baranof study area is characterized by a coastal climate with mean annual precipitations of 1,912 mm and average July temperatures of 8.2°C (Mesinger et al. 2006). Although coastal sites are located 3-4˚ further north, they are also 1,000 m lower approximately which explain why summer temperatures are largely similar.

Species assemblages of predators and other large ungulates vary between sites. In both continental sites, grizzly bears (Ursus arctos), wolves (Canis lupus), cougars (Puma concolor), and black bears (Ursus americanus) are the main predators of mountain goats. Several other large ungulates also share part of the range of continental goats such as bighorn sheep (Ovis canadensis), moose, caribou (Rangifer tarandus), elk, and mule deer (Odocoileus hemionus) (Festa-Bianchet and Côté 2008). In the Lynn Canal study area, brown bears, black bears, and wolves are the main predators, while moose is the only other large ungulate sharing their range with goats. In the Baranof Island study area, brown bears are the only potential predator, and Sitka black-tailed deer (Odocoileus hemionus) is the only other large ungulate (MacDonald and Cook 2007). Mountain goats in all habitats are strongly associated with escape terrain, which is primarily used to avoid predation (Poole and Heard 2003, Festa-Bianchet and Côté 2008, White and Gregovich 2018). This suggests that predation plays an important role in the habitat selection of mountain goats regardless of the predator assemblage.

Mountain goat activity and locations

In the Caw Ridge study area, we captured 15 goats (4 females and 11 males) in box traps between 2011 and 2019 (see Côté et al. (1998) for capture details). For the Jasper study area, we captured 10 goats (5 males and 5 females) in box traps between 2011 and 2013 (see Richard et al. (2014) for capture details). We marked goats from these two sites with GPS collars (GPS PLUS Iridium collars; VECTRONIC Aerospace GmbH, Berlin, Germany). The collars collected location, activity, and temperature data every 3h, during 1 to 6 years. For the Lynn Canal and Baranof Island study areas, we used helicopter darting techniques (White et al. 2021) to capture 157 goats (85 males and 72 females) between 2005 and 2019, and 61 goats (39 males and 22 females) between 2010 and 2018, respectively. We marked goats in Lynn Canal and Baranof Island with Telonics TGW-3590, TGW-4500 and TGW-4590 GPS satellite collars (Telonics, Inc., Mesa, AZ). The collars recorded location, activity and temperature data at 3h (N=42) or 6h (N=162) intervals, and collars were active between 6 months and 5 years. We knew the age of some animals that were previously marked as yearling, otherwise we aged goats at capture from horn annuli (Stevens and Houston 1989). In addition, we used incisor tooth eruption patterns (for animals <4-years old) and/or cementum annuli analysis (when teeth were collected from natural mortalities) to assess individuals’ age. The age of mountain goats at the time of capture ranged from 1 to 15, with an average of 7 years old.

Activity measurements in all collars were recorded over a 15-minute period following each GPS location acquisition attempt. Activity was measured as the number of switches of an accelerometer with one possible switch every second (min = 0, max = 900). A subset of 75 collars recorded activity over 15-minute periods throughout the day. We calibrated activity data to define active or inactive periods using focal observations of collared goats in Lynn Canal and domestic sheep wearing some of the collars previously deployed in the continental study sites (Appendix 1). We analyzed the calibration data using logistic regressions to estimate the probability of being active during every 15-minute periods and classified records as either active or inactive (Appendix 1). We removed activity values with a probability of being active between 15 and 85% (N=3,214 of 73,787 total readings) from all subsequent analyses to avoid introducing noise (i.e., measurements when animals were both active and inactive during the evaluation period) in the analyses (Appendix 1). We only used data collected in the months of June, July and August to assess summer habitat use and activity patterns to exclude the shoulder seasons (spring and fall) when mountain goat behaviour and space use are changing (e.g. due to altitudinal migration).

Weather

To characterize weather conditions in each study area, we extracted the weather data from the North American Regional Reanalysis (NARR) raster database, which covers all of North America at a 3h temporal resolution and a 32 km spatial resolution (Mesinger et al. 2006). To supplement temperature readings from mountain goat collars, we extracted air temperature, daily precipitation and wind speed for the median elevation recorded by the goat collars at each site (1,000 m for Lynn Canal and Baranof Island, 1,900 m for Caw Ridge and 2,200 m for Jasper). We linked GPS locations and activity readings to the closest temporal NARR reading available with a maximum difference of 1h between the GPS and NARR readings. The altitudinal accuracy of the NARR interpolation for mountainous regions has been shown to be reliable and a good representation of in-situ conditions (Trubilowicz et al. 2016), and our own testing showed a good correlation between NARR temperatures and in-situ readings (R²= 0.67, Appendix 2, R²= 0.82, Trubilowicz et al. 2016). Wind speed also showed a good level of agreement between in-situ readings and NARR with R²‘s ranging from 0.33 to 0.88 depending on the site used (Trubilowicz et al. 2016).

Temperature data were also recorded from a temperature logger inside the collar and calibrated using ambient temperature loggers installed throughout the study areas (Appendix 3). We used the temperature recorded by the GPS radio collar for the activity pattern analysis because collar temperature has been shown to be the most accurate estimate of the ambient temperature experienced by the individuals as it takes into account sun and wind exposure, as well as general habitat selection (Allred et al. 2013). Temperature recorded by collars, combined with wind speed and daily precipitation from the NARR, were thus used for the activity pattern analysis, while NARR temperature was used for the habitat selection analysis. NARR temperatures were used for the Resource Selection Function (RSF) analysis (see below) as temperature readings from mountain goat collars are influenced by the habitat selection of mountain goats and would add noise to the results. We needed a temperature reading that was independent from the habitat selection of mountain goats, and NARR temperatures were the best option that was standardized across all sites. We selected one data point and elevation for each of the four sites that was most representative of the site to be used for the entire analysis.

Habitat characteristics

We derived landscape-related explanatory variables in a geographic information system using Program R (R Core Team 2020) and QGIS 3.10 (QGIS Development Team 2021) (See Table 1). We used the most precise digital elevation models (DEM) available for each site, a 5 m resolution DEM for coastal (Carswell 2013), and a 13.5 m resolution DEM for continental sites (Canada Centre for Mapping and Earth Observation 2013). We standardized all DEMs among study areas to 10 m resolution by down sampling (using the “aggregate” function from the “raster” package (Hijmans and Etten 2012) in R) the coastal rasters and resampling (using the “resample” function in QGIS) the continental rasters. We created slope rasters from standardized DEMs using the “terrain” function from the raster package. We then created escape terrain rasters by selecting slopes > 40° (White and Gregovich 2018). We used the “apply” function from the “rgeos” package (Bivand and Rundel 2020) to calculate the distance to escape terrain for both used and available locations. We also created topographical sunshade rasters for every hour of every day for each site. We calculated the position of the sun using the “suncalc” package (Thieurmel and Elmarhraoui 2019). The positions of the sun were projected on the DEMs using the “insol” package in R (Corripio 2019).

Table 1 Descriptions and data sources for terrain characteristics and land cover habitat categories used to predict habitat use of mountain goats for the continental (Jasper and Caw Ridge, Alberta Canada, 2011-2019) and coastal (Baranof Island and Lynn Canal, Alaska, USA, 2005-2019) ecotypes

	Variables	Description	Data source
Terrain	Elevation	Elevation (m)	(Canada Centre for Mapping and Earth Observation 2013, Carswell 2013)
	Distance to escape terrain	Distance to a slope ≥40°
	Topographical shade	Shade index (yes or no) based on the topography and the position of the sun
Landcover	Forest	Forests of all stand types	(Canada Centre for Remote Sensing 2020)
	Alpine meadow	Shrubland, grasslands and mosslands
	Barren meadow	Area covered by at least 50% rock. Used as the reference category in analyses.
	Snow/Ice	Snow and ice

We used 30 m resolution land cover raster from the 2010 Land Cover of North America (Canada Centre for Remote Sensing 2020) to determine the land cover at each location. The original raster contained 19 land cover types, which we reclassified into four ecologically relevant land cover types based on our understanding of mountain goat ecology: alpine meadows (all shrublands, grasslands and moss lands), barren meadows (all habitats with a rock cover > 50%), forests (open and closed forests of all stand types), and snow/ice (glaciers, perennial snowfields and snow patches that last throughout the summer). Water and ocean were excluded because mountain goats do not use lakes, rivers or oceans.

1.4.4 Habitat selection analysis

To characterize mountain goat habitat selection in relation to weather conditions and ecotype, we developed a resource selection functions (RSF) for each ecotype using the “glmer” function (family: binomial, link: logit) from the lme4 package (Bates et al. 2015) according to the methods and recommendations described by Fieberg et al. (2021). We also used a weight of 1 for used points and 5000 for available points as recommended by Fieberg et al. (2021). Specifically, we estimated third-order resource selection, defined as use vs. availability within individual home ranges (Johnson 1980). We used a ratio of used/available locations of 1:5. Available locations were randomly distributed within the summer home range of each mountain goat using the “random_points” function from the “adehabitatHR” package (Calenge 2006). We excluded non-habitat from the available locations such as, lakes rivers and the ocean, as goats do not use these habitats. We estimated home ranges using the 95% minimum convex polygon (MCP) method, with the function “mcp” from the “adehabitatHR” package. To ensure accurate summer home range delineation and subsequent availability of habitat characteristics for each goat, we only included individuals with more than 35 used locations in the analysis (sample size threshold determined via preliminary simulation analyses). Therefore, the average number of used locations per goat for the coastal and continental goats were 332 (minimum = 35, maximum = 1359) and 796 (minimum = 80, maximum = 1674), respectively. We extracted habitat covariates of both used and available locations using the extract function from the “raster” package in Program R.

For the RSF analyses, we only used locations between 9h and 18h to isolate the effect of temperature on habitat selection and minimize the impact of the time of day. We used a total of 17,511 used locations from 22 goats for the continental RSF and 64,080 locations from 193 goats for the coastal RSF. For both ecotypes, we used the simple effect of the distance to escape terrain as a covariate and assessed the effect of temperature by including its interaction with elevation, land cover type, and topographical shade. We used barren meadows as the reference habitat as it was the land cover type which provided the smallest variation between used and available for both ecotypes (Appendix 4). We included individual identity and year as random intercepts. For the coastal model, however, the random intercept of year had a variance estimated at zero. Moreover, we evaluated a potential second-degree polynomial effect of elevation as goats’ selection of elevation may not be linear and they may select for intermediate elevations rather than high or low elevations. The second-degree polynomial model was retained in the coastal model based on a lower Akaike information criterion (AIC) value with a DAIC >10 (Burnham and Anderson 2002). For the continental model, we could not evaluate a second-degree polynomial because the model including this effect did not converge.

It was not possible to add date, age or sex to our models as it would have created several three-way interactions difficult to interpret (Fieberg et al. 2021). Age and sex were balanced in our analysis and not including them in our analysis is unlikely to create biases. To limit the effect of date in the analyses, we used only the months of June, July, and August, during which conditions and mountain goat summer habitat use are fairly similar (Rice, 2008). Still, temperature varied widely throughout the study period. To evaluate the potential for date and temperature to be confounded, we tested linear and quadratic regressions between temperature and Julian date for both ecotypes (Appendix 5). There was a weak quadratic relationship between temperature and date for both ecotypes, with the influence of date explaining less than 14% of the variability in temperature and temperature showing wide variation on each date (Appendix 5). We conclude that there is a low potential for temperature to be confounded by date and that neglecting the influence of date is unlikely to introduce a strong bias in the estimation and interpretation of the temperature effect on habitat selection.

Once the RSF models were fitted, we standardized the results by dividing the relative strength of selection by the relative availability of the habitat type to account for the availability of the different categories, and thereby avoid overestimating selection of very common categories (Fieberg et al. 2021). Assumptions for logistic regressions were met, the dependence of observations was accounted for with the mixed model, collinearity between variables were small [largest generalized variance inflation factor (Fox and Monette 1992) was 2.79 when adjusted for the degrees of freedom], there were no outliers, and all covariates were linear with the logit of the used vs. available response variable.

1.4.5 Activity patterns analysis

To assess how mountain goat activity patterns varied according to changes in environmental conditions, we modelled activity readings (0 inactive, 1 active) using a Generalized Additive Mixed Model (GAMM) with a binomial error and a logit link, using the “bam” function in the “mgcv” R package (Wood 2011). We included individual identity, population, and year nested within population as random intercepts to account for the repeated measures of individuals in the same population each year. We used a total of 70,573 activity readings from 170 individual mountain goats. We used Julian day, age and sex of the goat as covariates and assessed the influence of collar temperature, wind speed, daily precipitation, local time of day and ecotype on goat activity patterns. Age was used as a proxy for the mass of the animals as mass is known to influence the response to temperature in other species (Aublet et al. 2009). Sex was also included because males and females have different activity budgets in several ungulates (Ruckstuhl and Neuhaus 2000, 2002, Li and Jiang 2008).

To examine the interactions between weather conditions and mountain goat individual characteristics on activity, we included two-way smooth interactions (Table 2) of temperature with wind speed, precipitation, ecotype, age and sex. We also included a smooth interaction between time of day and Julian day to account for the varying length of the day throughout the season. We used a three-way interaction between Julian day, time of day and ecotype to account for differences in day lengths between both ecotypes. Finally, we added a three-way smooth interaction between temperature, time of day and ecotype to assess the effect of temperature on activity during different periods of the day for goats in the two ecotypes. Time of day was modelled as a cyclic spline to account for the continuity of time in the data. We selected K values for each smooth, specifying the number of knots to be fitted on the smooth by starting from the smallest K-value and incrementally increasing it for each variable until the value for each smooth was not significant in the “gam.check” function of the “mgcv” package, as described by Wood (2017). All model assumptions were met.

We also explored the effect of temperature on the daily percentage of time spent active by computing the percentage of time spent active over 24h. We only kept day for which we had 93 or more of the 96 possible activity readings to ensure accuracy of our proportions of time active. We used a total of 11,447 daily mean activity readings from 73 individual mountain goats. We then used the temperatures recorded by the mountain goat collars to calculate the mean and maximum temperatures for each 24h period. We used both mean and maximum temperatures to explore respectively the effect of sustained high temperatures on mountain goats as well as more short-term peaks of high temperature. Although, GPS collars are quite accurate to assess local temperatures (Appendix 3), intense solar radiation may increase temperatures recorded by collars. These readings do not represent the actual air temperature experienced by the goat, but they do indicate the goat is exposed to higher temperatures than when solar radiation is weaker. Thus, we performed two GAMMs, one model for the mean and one for the maximum temperature, using the same random effect model structure described for the activity patterns analysis. We included a smooth interaction between temperature and the following covariates: Julian day, ecotype, age and sex of the goat.

For both continental and coastal goats, habitat selection was influenced by temperature in interaction with elevation, topographical shade and habitat categories (Table 2). To visually illustrate and interpret the interaction between temperature and elevation, we examined the relative selection for two reference elevation categories (high and low elevation representing the 85^th and 15^th percentiles of elevation, respectively, for each site). For the coastal ecotype, higher elevations were always selected, over lower elevations, but the strength of selection increased with temperature (Fig. 2). For the continental ecotype, individuals exhibited greater selection for lower elevations than for high elevations when temperatures were low, while higher elevations were selected more than lower elevations at high temperatures (Table 2; Fig. 2). Increasing temperatures also changed selection for habitat categories (Table 2; Fig. 3)

For the coastal ecotype, increasing temperatures decreased the strength of selection for alpine meadows and increased the selection for snow and ice patches, whereas for the continental ecotype, selection for forested areas increased and selection for alpine meadows decreased with increasing temperatures relative to the reference category, barren meadows (Table 2; Fig. 3). In general, coastal and continental goats showed a strong selection for unshaded areas at all temperatures (Table 2; Fig. 4), but selection for shaded areas increased with temperature in the continental ecotype, with goats presenting a relatively similar relative selection for shaded and unshaded areas when temperatures increased over ~18˚C (Table 2; Fig. 4).

Table 2 Estimates and 95% confidence intervals (CI) for variables included in the resource selection functions of the coastal (Baranof Island and Lynn Canal, Alaska, USA, 2005-2019) and continental (Jasper and Caw Ridge, Alberta Canada, 2011-2019) mountain goat ecotypes, for the daytime period (9:00 – 18:00) during summer (1 June – 31 August). For the coastal model, we used 64,080 locations from 193 mountain goats, and for the continental model, we used 17,511 locations from 22 mountain goats. The pseudo R²was calculated using the “summ” function from the “jtools” (Long 2020)

Fixed effects	Estimate	Lower CI	Upper CI
*Coastal*
Temperature	0.18	0.15	0.20
Elevation	1.14	1.12	1.15
Elevation²	-0.66	-0.67	-0.65
Escape terrain	-0.67	-0.68	-0.65
Alpine meadow	0.75	0.73	0.77
Forest	0.38	0.34	0.42
Snow/Ice	-0.58	-0.61	-0.54
Shade	-0.36	-0.38	-0.34
Temperature * Elevation	0.13	0.12	0.15
Temperature * Elevation²	-0.01	-0.02	0.01
Temperature * Alpine meadow	-0.29	-0.31	-0.27
Temperature * Forest	-0.21	-0.25	-0.17
Temperature * Snow/Ice	0.14	0.11	0.17
Temperature * Shade	-0.17	-0.19	-0.15

Random intercepts	Standard Deviation
Individual		0.30
Year		0.00


*Continental*
Temperature	-0.23	-0.27	-0.18
Elevation	0.32	0.30	0.35
Escape terrain	-1.56	-1.61	-1.51
Alpine meadow	-1.01	-1.07	-0.94
Forest	-0.22	-0.27	-0.17
Snow/Ice	-0.73	-0.82	-0.63
Shade	0.34	0.28	0.39
Temperature * Elevation	0.22	0.20	0.24
Temperature * Alpine meadow	0.07	0.01	0.13
Temperature * Forest	0.30	0.25	0.35
Temperature * Snow/Ice	0.25	0.15	0.35
Temperature * Shade	0.09	0.04	0.14

Random intercepts	Standard Deviation
Individual		0.35
Year		0.20

Activity patterns were largely influenced by temperature, time of day and ecotypes (Table 3). Mountain goats in coastal and continental ecotypes exhibited a different daily activity pattern with increasing temperatures (Fig. 5).

For the coastal ecotype, the proportion of time goats spent active declined almost linearly with increasing temperatures, whereas the activity pattern of continental goats showed a bell-shaped pattern with increasing temperatures, with an activity peak near 15°C (Fig. 5). These activity patterns were similar across all hours of the day. The confidence intervals at 4h and 20h are large and encompass the null hypothesis of a constant activity level, suggesting that the response of mountain goats to temperature was strongest at midday (Fig. 5). Daily activity patterns differed between ecotypes (Fig. 6). When projecting results at a set temperature (15˚C), date, and weather conditions (to account for interactions), two activity peaks were observed for coastal goats: one at dawn (ca. 4h) and the other at dusk (ca. 20h), with lower activity during midday and the lowest activity during the night (Fig. 6). Continental goats only showed one activity peak at dusk (ca. 20h), while activity was lower throughout the rest of the day and night (Fig. 6). Wind, precipitation, age and sex, as well as their interactions with temperature, had only small effects on goat activity patterns (Appendix 6). Coastal goats showed a slight tendency to lower their activity when exposed to high temperatures and radiation, whereas continental goats showed a tendency for a slight increase (Fig. 7). Nevertheless, the daily proportion of time spent active with increasing daily mean and maximum temperatures showed wide confidence intervals for both ecotypes (Fig. 7), suggesting the time spent active is mostly unaffected by daily temperatures.

Table 3. Estimates [95% confidence interval CI] and estimated degrees of freedom (EDF) for the variables describing activity patterns of mountain goats of the continental (Jasper and Caw Ridge, Alberta Canada, 2011-2019) and coastal (Baranof Island and Lynn Canal, Alaska, USA, 2005-2019) ecotypes during the summer (1 June – 31 August). Total number of activity readings was 70,573 from 170 mountain goats

Variables	Estimate ± Standard Error			Statistic	P
*Parametric terms*				Z

Intercept (F, Coastal)	0.08	±	0.12	0.67	0.51
Sex (M)	-0.32	±	0.10	-3.29	0.001
Ecotype (Continental)	-0.33	±	0.22	-1.48	0.14

*Smooth terms*	EDF			X²

Temperature	1.00			10.77	0.001
Wind	4.98			29.40	< 0.001
Precipitation	5.69			77.61	< 0.001
Time of day	5.17			19.51	< 0.001
Julian Day	4.45			28.49	< 0.001
Age	6.43			29.05	< 0.001
Temperature * Wind	3.24			3.96	0.53
Temperature * Precipitation	2.83			6.70	0.11
Temperature * Sex (F)	2.86			8.49	0.08
Temperature * Sex (M)	0.98			0.65	0.52
Temperature * Age	5.20			27.64	< 0.001
Temperature * Ecotype (Coastal)	0.0002			0.00	0.50
Temperature * Ecotype (Continental)	3.72			55.42	< 0.001
Julian Day * Ecotype (Coastal)	2.33			9.21	0.03
Julian Day * Ecotype (Continental)	1.00			0.04	0.85
Time of day * Ecotype (Coastal)	4.54			12.84	< 0.001
Time of day * Ecotype (Continental)	4.14			9.99	< 0.001
Temperature * Time of day * Ecotype (Coastal)	15.07			71.62	< 0.001
Temperature * Time of day * Ecotype (Continental)	18.88			83.35	< 0.001
Julian Day * Time of day * Ecotype (Coastal)	28.58			1507.35	< 0.001
Julian Day * Time of day * Ecotype (Continental)	21.65			136.78	< 0.001

*Random intercepts*
Goat ID	151.17			5515.18		< 0.001
Study area	0.95			453.70		0.14
Year (Baranof Island)	6.30			4337.31		< 0.001
Year (Caw Ridge)	2.53			57.55		0.05
Year (Jasper)	2.69			219.41		0.006
Year (Lynn Canal)	6.13			274.07	0.13

Table 4. Estimates [95% confidence interval CI] and estimated degrees of freedom (EDF) for the variables describing the daily activity patterns of mountain goats of the continental (Jasper and Caw Ridge, Alberta Canada, 2011-2019) and coastal (Baranof Island and Lynn Canal, Alaska, USA, 2005-2019) ecotypes during the summer (1 June – 31 August), as a function of mean daily temperature. Total number of days was 11,447 from 73 mountain goats.

Variables	Estimate ± Standard Error			Statistic	P
*Parametric terms*				t

Intercept (F, Coastal)	-0.34	±	0.05	-6.20	< 0.001
Age	-0.001	±	0.005	-0.27	0.79
Sex (M)	-0.08	±	0.04	-1.89	0.06
Ecotype (Continental)	-0.06	±	0.09	-0.67	0.50

*Smooth terms*	EDF			F

Mean daily temperature	1.00			0.68	0.41
Julian Day	15.61			52.25	< 0.001
Mean daily temperature * Sex (F)	1.00			5.29	0.02
Mean daily temperature * Sex (M)	1.15			0.56	0.44
Mean daily temperature * Age	4.11			0.15	0.17
Mean daily temperature * Ecotype (Coastal)	1.06			0.57	0.44
Mean daily temperature * Ecotype (Continental)	2.02			3.83	0.01
Mean daily temperature * Julian Day	2.01			15.17	< 0.001

*Random intercepts*
Goat ID	65.86			136.37	< 0.001
Study area	0.88			1990.42	0.07
Year (Baranof Island)	6.05			828.20	< 0.001
Year (Caw Ridge)	4.84			220.33		< 0.001
Year (Jasper)	6.00			274.91		< 0.001
Year (Lynn Canal)	4.83			194.62		0.003

Table 5. Estimates [95% confidence interval CI] and estimated degrees of freedom (EDF) for the variables describing the daily activity patterns of mountain goats of the continental (Jasper and Caw Ridge, Alberta Canada, 2011-2019) and coastal (Baranof Island and Lynn Canal, Alaska, USA, 2005-2019) ecotypes during the summer (1 June – 31 August), as a function of maximum daily temperature. Total number of days was 11,447 from 73 mountain goats.

Variables	Estimate ± Standard Error			Statistic	P
*Parametric terms*				t

Intercept (F, Coastal)	-0.34	±	0.06	-6.16	< 0.001
Age	-0.002	±	0.005	-0.35	0.73
Sex (M)	-0.08	±	0.04	-1.84	0.07
Ecotype (Continental)	-0.10	±	0.09	-1.18	0.24

*Smooth terms*	EDF			F

Maximum daily temperature	1.00			0.59	0.44
Julian Day	15.78			53.02	< 0.001
Maximum daily temperature * Sex (F)	1.00			17.03	< 0.001
Maximum daily temperature * Sex (M)	0.0003			0.04	1
Maximum daily temperature * Age	13.47			0.72	0.003
Maximum daily temperature * Ecotype (Coastal)	4.31			2.06	0.05
Maximum daily temperature * Ecotype (Continental)	1.00			1.23	0.27
Maximum daily temperature * Julian Day	3.32			6.53	< 0.001

*Random intercepts*
Goat ID	65.83			118.83	< 0.001
Study area	0.92			1101.09	0.07
Year (Baranof Island)	6.01			578.07	< 0.001
Year (Caw Ridge)	4.85			240.01	< 0.001
Year (Jasper)	5.96			255.50	< 0.001
Year (Lynn Canal)	4.85			204.70	< 0.001

We observed that with increasing temperature mountain goats changed their habitat use and selection as well as their level of activity during summer. First, goats altered their habitat selection in relation to ambient temperature, increasing the use of putative thermal refugia such as higher elevations, snow patches, forest areas and shaded areas when temperatures increased. Second, we observed that goats decreased activity, particularly during midday as compared to dawn and dusk, when temperatures increased. Third, mountain goats from the two ecotypes differed both in terms of habitat selection and activity response, suggesting that goats in different ecotypes adopt different heat avoidance tactics.

Although we predicted that only coastal goats would increase selection of high-elevation areas relative to low-elevation areas with increasing temperatures, both ecotypes displayed this selection pattern. The use of higher elevations with increasing temperatures has also been observed in other alpine ungulates, such as mouflon (Ovis gmelini, Bourgoin et al. 2011) and alpine ibex (Aublet et al. 2009, Herfindal et al. 2019), as well as in a previous study on mountain goats in Alaska (Fox 1978). While the use of higher elevation offers the advantage of lowering heat stress, it may also offer higher forage quality for parts of the summer because the green up occurs later at higher elevations (Mysterud et al. 2001). However, because the growing season is shorter, soils are less developed at high elevations, and the area available for plant growth is reduced above a certain elevation, therefore lower forage availability is to be expected overall (Whittaker and Niering 1975, Cannone et al. 2007). Upslope range shift can be an efficient way of decreasing heat stress on the short-term by allowing mountain goats to avoid periods of high temperature, but it is not a viable long-term solution as it decreases the total usable area available. Furthermore, some mountain goat populations, especially in the south of their distribution, already use the highest available habitat under current conditions (DeVoe et al. 2015), making it impossible for these populations to shift their range upwards.

Mountain goats of the two ecotypes exhibited different selection patterns for land cover at all temperatures, which probably stems from the different land cover compositions available to each ecotype (Bjorneraas et al. 2012, Uzal et al. 2013). Mountain goats of both ecotypes selected for land cover types that were most available to them within their home ranges. Coastal goats had the strongest selection for alpine meadows at all temperatures while this category represents >50% of all available land covers (Appendix 4), while continental goats showed strongest selection for barren meadows and forested areas that respectively represent 20% and 50% of available land covers (Appendix 4). Thus, different baseline conditions likely resulted in goats adopting different tactics and responses to cope with warming temperatures in the two ecotypes (Reiner et al. 2021). Perennial snow and ice, for example, are almost completely absent from the two continental sites, representing only 3% of available land covers, while they represent 22% of available land covers for the coastal sites (Appendix 4). This low availability likely explains why continental goats did not select for snow patches when temperature increased and inversely for coastal goats. Still, our analyses were relatively broad and only included perennial snow fields and did not account for seasonal and temporary snow patches (Canada Centre for Remote Sensing 2020). Future analyses involving remote sensing data collected at finer temporal and spatial resolutions could provide further insights about snow patch use dynamics, as finer scale studies have shown that continental goats may select for snow patches as thermal refugia in some instances (Sarmento et al. 2019).

Depending on the ecotype, warmer temperatures resulted in goats using slightly different types of thermal refuges. Indeed, both ecotypes increased selection for higher elevation relative to lower elevations, but coastal goats also selected for snow patches whereas continental goats also selected for forested habitat and shaded areas. This shows that mountain goats use very different tactics to avoid heat across their range; a finding that suggests climate change may impact them in varying ways throughout their range. It also shows that thermal refugia can take several different forms and what represents key habitat may differ across the range of mountain goats. Although both habitat selection tactics allow for reduction of thermal stress, they may also reduce access to forage compared with habitats selected at lower temperatures (Whittaker and Niering 1975, Festa-Bianchet 1988). High elevations are generally associated with lower overall available forage, and snow fields and ice patches offer no forage. Even though forage is absent on snow patches, habitat bordering snow fields can provide higher quality forage throughout the summer (Fox 1991, Apple et al. 2022). As the snow retreats, nearby plants will start growing and will provide higher quality forage compared with plants further away because they are in their first phenological stages (Fox 1991, Apple et al. 2022). Goats may therefore lose foraging opportunities while on snowfields but may be able to balance this loss but utilizing higher quality forage in meadow habitats bordering snowfields (Whittaker and Niering 1975). The snowfield border habitat rapidly decreases in size as snow fields melt and shrink, which ultimately limits the access to this high forage quality but ephemeral habitat (Apple et al. 2022). Forested areas likely offer higher forage quality compared with alpine meadows for alpine ungulates (Zweifel-Schielly et al. 2009), but the abundance of vegetation selected by mountain goats may be lower. For example, slower growth rates were documented in bighorn sheep using lower forested areas instead of alpine meadows (Festa-Bianchet 1988).

The use of forest may also increase predation risk. Forested habitats are visually obstructed, which lowers the ability of ungulates to detect predators compared to open alpine habitat, and can lead to higher predation risk (Bednekoff and Blumstein 2009, Déry 2020). Festa-Bianchet et al. (1994) reported that the majority of predation on mountain goats occurred in open or closed forest, as the principal predators in their Rocky Mountains study area were mainly ambush hunters that capitalized on cover provided by forests. As a consequence of higher predation-risk, mountain goats spent a higher proportion of time vigilant and less time foraging in forested habitats as compared to elsewhere (Déry 2020). Higher predation-risk increases physiological stress levels and can reduce female reproductive rates in mountain goats (Dulude‐de Broin et al. 2020); a relationship that provides further insight into the consequences of temperature driven shifts to risky, forested habitats.

Increased predation exposure for continental goats as a result of higher use of forested areas may partly explain the differences in activity patterns observed between the two ecotypes. The coastal populations showed the expected diurnal peaks of daily activity at dawn and dusk with low activity during the night, a pattern found in most northern ungulates (Bourgoin et al. 2011, Ensing et al. 2014, Borowik et al. 2020). The continental goats only showed one activity peak in the evening and lower activity throughout the rest of the day and night. Hunting and predation have been shown to alter dial activity patterns in other species forcing prey to be active at times when their predators are not (Espinosa and Salvador 2017, Gaynor et al. 2018). Therefore, the observed difference in general activity pattern between coastal and continental populations may be caused by higher predation pressure on continental goats that result from increased use of high-risk forested habitats during warm weather. However, further analysis on the relationship between activity patterns and density of predators would be necessary to definitively address this question.

The overall activity response to temperature also differed between the two ecotypes. Our first hypothesis stated that differences in body size between the two ecotypes may explain variation in temperature responses. Male mountain goats in Alaska are among the heaviest mountain goats and are about 25% heavier than in Alberta (Appendix 7). Females in Alaska are also larger than in Alberta, although the differences are less pronounced compared to males (Appendix 7). The greater surface area : volume ratio for continental goats indicates that they should be more tolerant to heat than coastal goats as their smaller body mass allows for more heat dissipation per unit area, suggesting they can be more active at higher temperatures (Phillips and Heath 1995). Morphological differences between populations of the same species have been shown to influence individual responses to similar environmental conditions corroborating our findings (Aublet et al. 2009, Parker and Robbins 2018). This hypothesis, however, cannot explain why males and females that inhabit the same sites while having much larger mass differences than among ecotypes, show the same general response to temperature in our model (Fig. A6). Thus, mass alone appears unlikely to explain the different activity patterns, and a more complex process is likely operating. Differences in moulting phenology, habitat selection and other factors may also explain the differences in activity patterns in addition to mass differences. For example, continental mountain goats use forested areas more than coastal goats. Increased use of forest allows mountain goats to be in the shade, which may lower the perceived temperature compared to the same temperature in unshaded areas. The higher use of forested and shaded microsites of continental mountain goats compared to coastal goats, may act as a buffer against increasing temperatures and allow them to keep stable activity levels with a certain degree of warming. The threshold at which activity starts decreasing for continental mountain goats (15°C) is still ~6˚C higher than the average July temperature for our study sites, indicating that they have a certain buffer that coastal goats do not have. Forest has been shown to act as a buffer against climate change for another alpine ungulate (Reiner et al., 2021). For example, Reiner et al. (2021) showed that chamois living in forested areas did not follow the same trend of decreasing body mass with warming temperatures other chamois populations exhibited. This suggests that the use of forested areas acted as a buffer against some of the effects of climate change. The ability of forest to act as a buffer against warming temperatures for continental goats may have important implications in their ability to cope with future warming.

Our exploratory analysis on the impact of temperature on mean daily activity suggests that mountain goats can compensate for the decrease in activity related to high midday temperatures by increasing activity at cooler times of the day. Nonetheless, there is a tendency for coastal mountain goats to decrease their daily activity when maximum temperatures are high, suggesting they may not be able to cope with higher changes in temperature. This potential compromise seems unlikely in continental goats because they showed a tendency for a slight increase in mean daily activity with increasing temperatures.

Wind and precipitation showed no impact on mountain goat activity patterns and did not seem to mitigate the effect of high temperatures. Compared with temperature, wind and precipitation are harder to predict from climate models (Trubilowicz et al. 2016) and accurately estimate at a specific location and time. In particular, wind can vary greatly within just a few metres (Ngo and Letchford 2008). Although we could differentiate windy days from calm days with the wind measurements we used, we could not identify with precision the wind conditions truly experienced by mountain goats. Because windchill is expected to reduce perceived temperatures, wind may still have an influence on activity, but more complex models taking location in the landscape and topography into consideration would be necessary to detect such effects.

Whether one ecotype will fare better than the other in light of predicted climate change is not clear, but our results indicate that they will react differently on several levels. Our study highlights the potential impacts of climate change and more specifically the role of increasing temperatures on habitat selection and activity patterns of mountain goats from populations located at different ends of their distribution, and how their local environment affects that response. Although previous studies have assessed interactions between environmental variables and responses to climate change among large mammals (Lowe et al. 2010, Reiner et al. 2021), our study provides a better understanding of the potential impacts of climate change on a species across its range. Such studies are key to further our understanding and potentially predict the medium and long-term effects of climate change on specific populations. Individuals of some populations may be able to adopt tactics allowing them to cope with ongoing climate change or even to thrive in these new conditions, while others may be vulnerable to local extirpation. Studies such as ours can also help identify habitat that is vital to a species ability to adapt to climate change, and these thermal refugia’s may be completely different across the range of a species. Comparing behavioural responses across the range of a species to similar climate stressors represents a powerful approach to understand how climate change can differently affect populations of the same species.

Acknowledgments

Our work was mainly financed by the Natural Sciences and Engineering Research Council of Canada, Alberta Conservation Association, Alberta Fish and Wildlife, Université de Sherbrooke, Sentinelle Nord, Université Laval, Ski Marmot Basin, Jasper National Park, Alaska Department of Fish and Game, Alaska Department of Transportation and Public Facilities, Coeur Alaskawi and Wild Sheep Foundation. We are grateful to all those that have been involved in fieldwork and logistical support over the years, especially K.G. Smith, M. Festa-Bianchet, D. Hobson, M. Russell, J. Wilmshurst, N. Barten, J. Jemison, D. Larsen, P. Mooney, Y. Shakeri and Temsco Helicopters. Also, a special thank you to M. Leclerc for his valuable help during the analysis stage of the project.

Ethical Approval

Capture and handling procedures were reviewed and approved by the Alaska Department of Fish and Game Institutional Animal Care and Use Committee (protocols 05‐11, 2016‐25, 0078‐2018‐68, 0039‐2017‐39), and the Université Laval animal care protocols (#2011040-3, #2011039-1, #2014011-1 and #2018033-1) and followed American Society of Mammalogists guidelines (Sikes et al. 2016).

R. S. Sikes, the Animal Care and Use Committee of the American Society of Mammalogists, 2016 Guidelines of the American Society of Mammalogists for the use of wild mammals in research and education: J. Mammal. 97, 663–688 (2016). doi: 10.1093/jmammal/gyw078

Competing interest

There were no competing interests among the authors

Author contribution

A.M. Designed the study, collected data in the field, conducted analysis, and wrote the main body of the text.

K.S.W. Provided funding, helped with design of the study, collected data in the field, and revised the main body of the text

S.H. Helped with design of the study, collected data in the field, helped with the analysis and revised the main body of the text

J.H.R. Helped with design of the study, collected data in the field, and revised the main body of the text

S.D.C. Provided funding, helped with design of the study, collected data in the field, and revised the main body of the text

Data availability

All data and code are available here ; https://datadryad.org/stash/share/bgZgT39OzTUjnVhGbvoHi1GL9XmhtQXziwHYJk4I7Cs

Funding

Funding for this work was provided by the Alaska Climate Adaptation Science Center, Alaska Department of Fish and Game, Federal Aid in Wildlife Restoration Program grant AKW-10 Project 12.01, Alaska Department of Transportation and Public Facilities, Alaska Division of Geological and Geophysical Surveys, Bureau of Land Management, City of Sitka, Coeur Alaska, Federal Highway Administration, Wild Sheep Foundation, the Natural Sciences and Engineering Research Council of Canada, Alberta Conservation Association, Alberta Fish and Wildlife, Université de Sherbrooke, Sentinelle Nord, Université Laval, Ski Marmot Basin, and Jasper National Park

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Of goats and heat, the differential impact of summer temperature on habitat selection and activity patterns in mountain goats (Oreamnos americanus) of different ecotypes

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Abstract

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Introduction

Methods

Study sites

Mountain goat activity and locations

Habitat characteristics

1.4.4 Habitat selection analysis

1.4.5 Activity patterns analysis

Results

Discussion

Declarations

Acknowledgments

References

Additional Declarations

Supplementary Files

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