Fine-scale temperature patterns in the southern boreal forest: implications for the cold-adapted moose.
Moose (Alces alces) are adapted to cold environments (Karns 2007)
but conversely, are less tolerant of high ambient temperatures
([T.sub.a]). Renecker and Hudson (1986) estimated the upper critical
temperatures ([T.sub.uc]) of moose as -5[degrees]C in winter and
14[degrees]C in summer, with open mouth panting occurring at 0[degrees]C
and 20[degrees]C, respectively. A recent study estimated a slightly
higher [T.sub.uc] in summer (17[degrees]C; McCann et al. 2013). These
estimates of [T.sub.uc] provide a lower limit of [T.sub.a] at which
moose presumably employ physiological and behavioral mechanisms to
reduce thermal stress.
Moose respond physiologically to high [T.sub.a] by reducing metabolic rate, flattening their pelage, and increasing respiratory rate to expel excess heat, but they cannot sweat (Schwartz and Renecker 2007). They also exhibit behavioral responses including higher use of conifer stands for thermal refuge and nocturnal activity (DeMarchi and Bunnel 1995, Dussault et al. 2004, Broders et al. 2012).
Chronic exposure of moose to high [T.sub.a] has been correlated with reduced weight gain in Norway (van Beest and Milner 2013), lower survival in northeastern Minnesota (Lenarz et al. 2009), population declines in northwestern Minnesota (Murray et al. 2006), and distribution shifts in China (Dou et al. 2013). Fine-scale differences in [T.sub.a] likely exist across space and time at the southern extent of moose range, and individual moose should exploit these differences to mitigate the effects of high [T.sub.a] on body condition and ultimately fitness. Previous studies of moose habitat selection and [T.sub.a] focused on forest cover type as the main driver of thermal conditions across the landscape (e.g., Lowe et al. 2010). Other factors affecting variability in the thermal landscape include elevation, canopy cover, slope and aspect, and position on slope (Reifsnyder et al. 1971, Chen and Franklin 1997, Danielson et al. 1997, Chen et al. 1999, Ellis and Pomeroy 2007). Habitat selection patterns relative to [T.sub.a] cannot be fully understood without a clear understanding of patterns in the thermal environment at different spatial and temporal scales.
[FIGURE 1 OMITTED]
Operative temperature ([T.sub.o]) is an approximation of the convective and radiant heat transfer on the surface of an animal, making it a useful measure to interpret the thermal environment experienced by animals versus [T.sub.a] alone (Dzialowski 2005). For example, animals experience different [T.sub.o] in sunlight, wind, or under forest canopy at the same [T.sub.a]. It is easiest to estimate [T.sub.o] with a black globe thermometer (Vernon 1930, 1932, 1933; Fig. 1) which consists of a matte black painted copper sphere containing a temperature logger that integrates [T.sub.a], mean radiant temperature, and air movements into a single metric (Bedford and Warner 1934).
Our objectives were to identify physical and vegetative factors that influence [T.sub.o], and to characterize the thermal environment experienced by moose across different cover types in Voyageurs National Park (VNP) in northeastern Minnesota.
STUDY AREA
Voyageurs National Park (VNP) is situated on the southern limit of North American moose range, along the Minnesota-Ontario border (Fig. 2). The climate is mid-continental with long cold winters and short cool summers. Mean monthly temperatures range from -15[degrees]C in January to 19[degrees]C in July with an annual mean temperature of 3[degrees]C (NOAA 2010). First snowfall usually occurs in early November and final snowfall in early April. Average annual precipitation is 61 cm, with an average annual snowfall of 183 cm (NOAA 2010).
[FIGURE 2 OMITTED]
We limited the study area to the 329 [km.sup.2] Kabetogama Peninsula as this is where most moose in VNP currently reside (Windels 2014, Fig. 2). Vegetation in the Kabetogama Peninsula is typical of the southern boreal and Laurentian mixed conifer-hardwood regions (Faber-Langendoen et al. 2007). Forest cover is a mosaic of quaking aspen (Populus tremuloides), paper birch (Betula papyrifera), balsam fir (Abies balsamea), and jack (Pinus banksiana), red (P resinosa), and white pine (P strobus). A variety of wetlands including bogs, fens, marshes, and swamps are interspersed across the landscape (Faber-Langendoen et al. 2007). Geological features include thin and sandy topsoil with regions of exposed bedrock (Ojakangas and Matsch 1982).
Moose and white-tailed deer (Odocoileus virginianus) are the only ungulate species in VNP; woodland caribou (Rangifer tarandus) were extirpated by the early 1900s (Cole 1987). Moose density in the Kabetogama Peninsula ranges from 0.14-0.19 moose/[km.sup.2] and has remained stable since the 1990s (Windels 2014). Beaver (Castor canadensis) are abundant and contribute significantly to the spatial heterogeneity of the landscape (Johnston and Naiman 1990).
A variety of human and other natural disturbances have created a diverse mosaic of vegetation in multiple seral stages. Wildfires and extensive logging occurred in the 1920s and 1930s, followed by less intensive logging through the 1960s (Gogan et al. 1997). Major fires occurred throughout VNP in 1923 and 1936. Suppression of fire followed until the late 1980s when the National Park Service implemented a wildland fire management plan, though most prescribed burns have been relatively small (National Park Service, unpublished data).
METHODS
To measure [T.sub.o] across the Kabetogama Peninsula, we stratified our sampling design by landscape and vegetation characteristics identified within a 30-m x 30-m pixel matrix that matches with LandSat imagery. We derived slope and aspect for each pixel using 30 m resolution Shuttle Radar Topography Mission data (version 1) (Rabus 2003, Rodriguez et al. 2005, 2006). We estimated slope using the slope function from ERDAS Imagine (ERDAS Inc. 2010). We categorized slope as either <10% (5.7[degrees]) or [greater than or equal to] 10% (5.7[degrees]). We calculated aspect using the aspect function of ERDAS Imagine (ERDAS Inc. 2010) and classified each pixel into 1 of 2 categories: aspects between 315-45[degrees] (i.e., north) and aspects between 45-315[degrees] (i.e., east/south/west). We assumed that solar inputs would be lower on north facing slopes compared to east, south, and west facing slopes. Therefore, we combined slope and aspect into flat, slopes facing north, and slopes facing east/south/west for analysis. Detectable variation in [T.sub.a] as a function of elevation was not expected in VNP as most local relief is <30 m and maximum relief within the Peninsula is only 81 m. Therefore, elevation was not considered in our sampling design or subsequent modeling.
We developed a canopy cover model using the methodology outlined in the Great Lakes Inventory and Monitoring Network's Landscape Dynamics protocol (Kennedy and Kirschbaum 2010, Kennedy et al. 2010). Percent cover of trees, shrub, and ground layer was estimated at 30-m pixels in ArcMap (ESRI 2011) using high-resolution air photos taken in the spring (leaf-off) and summer (leaf-on) of 2008 with 0.15 and 1 m resolutions, respectively (Kirschbaum and Gafvert 2010). Estimates of canopy cover in each pixel were related to the normalized burn ratio (van Wagtendonk et al. 2004) calculated from the Landsat image corresponding to that time period to create a regression model of canopy cover. We categorized canopy cover as open (i.e., no or few canopy-forming trees), variable cover (i.e., non-forested, discontinuous canopy), <70% forest cover, 70-80% forest cover, and >80% forest cover. We classified vegetation cover type using the National Vegetation Classification System Subclass Level (deciduous, evergreen, mixed, woodland, shrub, or herbaceous) developed for VNP (Faber-Langendoen et al. 2007).
We developed a sampling matrix using the 3 sets of variables (vegetation cover type, canopy cover, slope/aspect). Each unique combination of the 3 variables was given an identifying code using ERDAS Imagine. A 30-m pixel raster map was created and converted to a polygon shapefile. Areas for each polygon were calculated and polygons <1.07 ha (12 pixels) were deleted to avoid sampling very small patches. We sampled at an intensity of 1 temperature logger for every 333 ha in the study area. We randomly selected polygons to sample from each of 38 unique combinations of vegetation cover type, canopy cover class, and slope/aspect class. We located the sample point near the centroid of the polygon to allow a sufficient buffer between adjacent polygons. We used alternate sites if the selected polygon centroid was <30 m (1 pixel) from the edge, field reconnaissance found that site characteristics were different from remotely-sensed data, or sites were otherwise inaccessible (e.g., flooding).
Black globe temperature loggers consisted of a data-logging thermocouple (Onset Computer Corporation, Bourne, Massachusetts, USA) inserted into a copper toilet tank float painted matte black (Fig. 1). We calibrated loggers for a minimum of 96 h to verify accuracy and resolution as compared to the stated equipment specifications of the logger ([+ or -] 0.21[degrees]C from 0[degrees]-50[degrees]C). All loggers were synchronized and programmed to record temperatures every 15 min for 1 year. At each sample point, we hung loggers 0.75 m above the ground and 15 cm from the trunk. Loggers were placed on the northeast side of trees to minimize direct solar radiation during the warmest time of day (Fig. 1). We used handheld field computers with GPS to verify that logger placement in the field was consistent with identified cover type and location within the cover type polygon. We used real-time GIS and measurements in the field to ensure we were within the identified cover type, canopy cover class, and slope/aspect category before deploying loggers. Loggers were deployed from June 2010 to July 2011, with periodic downloads to reduce risk of data loss. Data were screened to remove biased or failed measurements (e.g., faulty logger, damaged globe or logger, and snow-covered loggers).
We deployed an additional set of loggers from August 2011 to January 2012 to test for differences in position on slope. We randomly deployed 3 loggers in each of 9 combinations of cover type (deciduous, evergreen, and mixed), canopy cover class (<70%, 70-80%, >80% forested canopy), and slope position (top, mid-slope, and base); slopes ranged from 17-47%.
We analyzed data by season with full factorial repeated-measures ANOVA with type III sums of squares using SPSS 20 (IBM Corporation 2011). We defined seasons as spring (1 March-31 May), summer (1 June-31 August), fall (1 September-30 November), and winter (1 December-28 February). Each 15 min logger interval was treated as the response variable but controlled for repeated measures. Post-hoc pairwise comparisons were made using the Bonferroni method. Significance levels were set to P = 0.05 for all tests. Subsamples of the dataset were made to compare differences during the 3 warmest hours of the day (1300-1600 hr) and the 3 coldest hours of the day (0300-0600 hr). We used a similar statistical approach to test for differences in slope position for summer and winter only.
Based on model results, we assessed the availability of potential thermal refugia to moose across the Kabetogama Peninsula. We simulated moose home ranges by creating 25 random points within the study area and then buffering those points to approximate mean annual moose home ranges in VNP (48 [km.sup.2] [+ or -] 33.5 SD) as reported by Cobb et al. (2004). Home ranges varied in size due to the highly variable shoreline of the Kabetogama Peninsula. Within each simulated home range, we estimated the proportion of each habitat type (vegetation cover type, canopy cover class, slope/aspect) and used summary statistics to highlight availability of selected habitat features.
RESULTS
Open habitat types (shrub and herbaceous) were significantly warmer than forested habitat types during the summer (maximum difference = 3.38[degrees]C; Fig. 3) with the greatest difference occurring in the afternoon (maximum difference = 8.10[degrees]C; Table 1, Fig. 3). Open habitat types were also the coolest at night (maximum difference = 2.66[degrees]C; Table 1, Fig. 3). Mean [T.sub.o] during the summer did not differ among forested cover types for any of the time periods (Daily, Hot, Cold). Herbaceous cover types were warmer than forested cover types in the afternoon during the fall (maximum difference = 7.43[degrees]C; Table 1, Fig. 3). Temperatures in shrub cover types were intermediate between herbaceous and forested cover types. The amount of canopy influenced [T.sub.o] within forested cover types. Areas with >80% canopy coverage were cooler than those with <70% cover (Table 2, Fig. 4); [T.sub.o] in the 70-80% cover class was intermediate to these.
[FIGURE 3 OMITTED]
Slope/aspect influenced [T.sub.o] only during the 3 coldest hours of day in summer. Flat areas were cooler than East/South/West facing slopes (difference = 1.14[degrees]C; Table 3). North-facing slopes were intermediate between flat areas and East/South/West facing slopes. Slope/aspect had no influence on [T.sub.o] during the fall months. Winter temperature was only differentiated by slope/aspect during the afternoon with northern facing slopes cooler than both flat and east/south/west categories (Table 3, Fig. 5). Spring temperatures varied across slope/aspect categories during the afternoon hours. North-facing slopes were cooler than flat areas (Table 3, Fig. 5). [T.sub.o] was not different among slope positions in summer ([F.sub.2, 21] = 0.287, P = 0.755) or winter ([F.sub.2, 21] = 0.606, P = 0.556).
The majority of the Kabetogama Peninsula consists of forested cover types with >70 percent canopy cover and flat topography (Table 4, Fig. 6, 7). Simulated home ranges varied in size from 23-57 [km.sup.2] with a mean of 46 [km.sup.2] (SD = 10.1 [km.sup.2]). Potential summer refugia, such as high-canopy cover forests, were found in about 40% of simulated home ranges (Table 4). However, north-facing slopes are relatively limited in the study area and the percentage of north-facing slopes in simulated home ranges was <10% (Table 4, Fig. 7).
DISCUSSION
Vegetation cover type, percent canopy cover, and slope/aspect all influenced [T.sub.o], although differently depending on season and time of day. Vegetation cover type had the strongest influence on [T.sub.o] during summer months and fall afternoons. This was largely driven by open versus forested habitats, although we detected small but significant differences in [T.sub.o] within closed forest habitats, similar to other studies (e.g., McGraw et al. 2012). Amount of canopy cover significantly affected [T.sub.o] only during afternoons in the summer months. Forest type and the amount of canopy cover effectively combine to reduce the amount of solar radiation that reaches the forest floor and therefore can reduce heat loading from direct solar radiation (Demarchi and Bunnel 1993). Vegetation volume, hence canopy cover, is greatest in summer months, likewise solar angle is most direct during summer afternoons. Areas with thick vegetation and dense canopy cover may serve as ideal thermal refuge for moose during the day (DeMarchi and Bunnell 1995, Dussault et al. 2004, van Beest et al. 2012). Although some variation exists in the amount of forested habitat types with high canopy cover within our simulated home ranges, these habitat types do not seem limited in the study area.
[FIGURE 4 OMITTED]
[FIGURE 5 OMITTED]
[FIGURE 6 OMITTED]
Open cover types were cooler than forested cover types during the 3 coldest hours of the day. Dense vegetation and canopy cover actually retain heat within forested cover types while open cover types release more heat at night (Chen et al. 1993). Moose in central Norway use open habitat types at night and older forested stands during daytime (Bjorneraas et al. 2011). The availability of open cover types may be limited for some moose in the Kabetogama Peninsula.
[FIGURE 7 OMITTED]
We defined 4 equal seasons in our models based on calendar months rather than the timing of leaf phenology which varies annually in response to weather events, disease, drought, and other factors (Lechowicz 1984). As a consequence, our ability to detect significant differences may have been diminished for some variables, specifically canopy cover. Future studies of [T.sub.o] should consider incorporating important predictor variables that may change at relatively fine time scales. Also, canopy cover estimates were based on the leaf-on period, and may not accurately reflect the true amount of canopy cover during leaf-off periods for all cover types with a deciduous tree component.
The majority of the study area was flat and cooler at night than east/south/west facing slopes during summer, likely due to differences in radiant heat loss. Slope/aspect was the only significant influence on [T.sub.o] during winter months as well as spring afternoons. Slope/aspect may have a stronger effect on [T.sub.o] during winter months when the solar angle is at its lowest. These environments may serve as thermal refugia on warm days in winter and early spring as topographic exposure can influence maximum daily temperatures (Bolstad et al. 1998). Additionally, radiation received on flat and south facing slopes may be reflected to the body by the high reflectivity of snow in winter. More northerly locations may realize increased effect of slope/aspect, as well as areas with greater topographic relief. Moose strongly selected north-facing slopes in southwestern Alberta due to increased shade and browse availability (Telfer 1988). North-facing slopes make up less than 5 percent of our study area and across most simulated home ranges, suggesting this type of seasonal thermal refugia may be limited for moose in our study area.
We did not detect an effect of slope position on [T.sub.o], presumably because of the low topographic relief in the study area (Danielson et al. 1997). Areas with larger elevational gradients than our study area should include elevation as a variable due to the adiabatic lapse or rate change in temperature of an air mass as it changes with altitude (American Meteorological Society 2000). In certain studies elevation was the single strongest driver of temperature difference (e.g., Lookingbill and Urban 2003).
Moose use aquatic habitats for a variety of reasons including foraging, sodium acquisition, insect relief, and thermoregulation (Peek 2007). Aquatic habitats in our study area contain little to no canopy cover and related [T.sub.o] regimes are likely similar to that of open habitat types. Although moose using shallow, aquatic habitats during daytime may be exposed to direct solar radiation, they could mitigate heat loading by submerging in water.
Thermal variability exists at relatively fine scales across our study area due primarily to the fine mosaic of vegetation cover types, canopy coverage, and site aspect (Fig. 6, 7). We detected maximum differences in mean [T.sub.o] of [less than or equal to] 9[degrees]C across all habitat types during the warmest parts of summer days. Within forested habitat types, there was >2[degrees]C difference across canopy cover categories in summer. Slope/aspect accounted for as much as a 4[degrees]C difference in [T.sub.o] during winter and spring. Even small differences in the thermal environment may be relevant for achieving individual heat balance (Renecker and Hudson 1990).
The availability of thermal refugia will be of greater importance at the southern edge of moose range as mean annual temperature continues to rise with climate change (IPCC 2007). Behavioral responses to high [T.sub.a] include specific microhabitat use and activity shifts in other parts of moose range (Dussault et al. 2004, Broders et al. 2012, van Beest et al. 2012). To mitigate the effects of increasing [T.sub.a], managers should promote a variety of habitat types to provide adequate thermal refugia within a typical home range while meeting other life history requirements of moose (Peek 2007).
ACKNOWLEDGEMENTS
We thank B. Severud, J. Warmbold, C. Eckman, D. Morris, and N. Walker for assistance with field work and A. Kirschbaum for remote sensing support. This project was funded by Voyageurs National Park, a grant from the USGS-NPS Natural Resource Preservation Program, a grant from the U.S. National Park Service's Great Lake Research and Education Center, Bemidji State University, the Natural Resources Research Institute at the University of Minnesota Duluth, and the Environment and Natural Resources Trust Fund.
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Bryce Olson (1, 2), Steve K. Windels (1), Mark Fulton (2), and Ron Moen (3)
(1) National Park Service, Voyageurs National Park, 360 Highway 11 E, International Falls, Minnesota 56649; (2) Bemidji State University, 1500 Birchmont Drive NE, Bemidji, Minnesota 56601; (3) Natural Resources Research Institute, University of Minnesota, 5013 Miller Trunk Highway, Duluth, Minnesota 55811
Moose respond physiologically to high [T.sub.a] by reducing metabolic rate, flattening their pelage, and increasing respiratory rate to expel excess heat, but they cannot sweat (Schwartz and Renecker 2007). They also exhibit behavioral responses including higher use of conifer stands for thermal refuge and nocturnal activity (DeMarchi and Bunnel 1995, Dussault et al. 2004, Broders et al. 2012).
Chronic exposure of moose to high [T.sub.a] has been correlated with reduced weight gain in Norway (van Beest and Milner 2013), lower survival in northeastern Minnesota (Lenarz et al. 2009), population declines in northwestern Minnesota (Murray et al. 2006), and distribution shifts in China (Dou et al. 2013). Fine-scale differences in [T.sub.a] likely exist across space and time at the southern extent of moose range, and individual moose should exploit these differences to mitigate the effects of high [T.sub.a] on body condition and ultimately fitness. Previous studies of moose habitat selection and [T.sub.a] focused on forest cover type as the main driver of thermal conditions across the landscape (e.g., Lowe et al. 2010). Other factors affecting variability in the thermal landscape include elevation, canopy cover, slope and aspect, and position on slope (Reifsnyder et al. 1971, Chen and Franklin 1997, Danielson et al. 1997, Chen et al. 1999, Ellis and Pomeroy 2007). Habitat selection patterns relative to [T.sub.a] cannot be fully understood without a clear understanding of patterns in the thermal environment at different spatial and temporal scales.
[FIGURE 1 OMITTED]
Operative temperature ([T.sub.o]) is an approximation of the convective and radiant heat transfer on the surface of an animal, making it a useful measure to interpret the thermal environment experienced by animals versus [T.sub.a] alone (Dzialowski 2005). For example, animals experience different [T.sub.o] in sunlight, wind, or under forest canopy at the same [T.sub.a]. It is easiest to estimate [T.sub.o] with a black globe thermometer (Vernon 1930, 1932, 1933; Fig. 1) which consists of a matte black painted copper sphere containing a temperature logger that integrates [T.sub.a], mean radiant temperature, and air movements into a single metric (Bedford and Warner 1934).
Our objectives were to identify physical and vegetative factors that influence [T.sub.o], and to characterize the thermal environment experienced by moose across different cover types in Voyageurs National Park (VNP) in northeastern Minnesota.
STUDY AREA
Voyageurs National Park (VNP) is situated on the southern limit of North American moose range, along the Minnesota-Ontario border (Fig. 2). The climate is mid-continental with long cold winters and short cool summers. Mean monthly temperatures range from -15[degrees]C in January to 19[degrees]C in July with an annual mean temperature of 3[degrees]C (NOAA 2010). First snowfall usually occurs in early November and final snowfall in early April. Average annual precipitation is 61 cm, with an average annual snowfall of 183 cm (NOAA 2010).
[FIGURE 2 OMITTED]
We limited the study area to the 329 [km.sup.2] Kabetogama Peninsula as this is where most moose in VNP currently reside (Windels 2014, Fig. 2). Vegetation in the Kabetogama Peninsula is typical of the southern boreal and Laurentian mixed conifer-hardwood regions (Faber-Langendoen et al. 2007). Forest cover is a mosaic of quaking aspen (Populus tremuloides), paper birch (Betula papyrifera), balsam fir (Abies balsamea), and jack (Pinus banksiana), red (P resinosa), and white pine (P strobus). A variety of wetlands including bogs, fens, marshes, and swamps are interspersed across the landscape (Faber-Langendoen et al. 2007). Geological features include thin and sandy topsoil with regions of exposed bedrock (Ojakangas and Matsch 1982).
Moose and white-tailed deer (Odocoileus virginianus) are the only ungulate species in VNP; woodland caribou (Rangifer tarandus) were extirpated by the early 1900s (Cole 1987). Moose density in the Kabetogama Peninsula ranges from 0.14-0.19 moose/[km.sup.2] and has remained stable since the 1990s (Windels 2014). Beaver (Castor canadensis) are abundant and contribute significantly to the spatial heterogeneity of the landscape (Johnston and Naiman 1990).
A variety of human and other natural disturbances have created a diverse mosaic of vegetation in multiple seral stages. Wildfires and extensive logging occurred in the 1920s and 1930s, followed by less intensive logging through the 1960s (Gogan et al. 1997). Major fires occurred throughout VNP in 1923 and 1936. Suppression of fire followed until the late 1980s when the National Park Service implemented a wildland fire management plan, though most prescribed burns have been relatively small (National Park Service, unpublished data).
METHODS
To measure [T.sub.o] across the Kabetogama Peninsula, we stratified our sampling design by landscape and vegetation characteristics identified within a 30-m x 30-m pixel matrix that matches with LandSat imagery. We derived slope and aspect for each pixel using 30 m resolution Shuttle Radar Topography Mission data (version 1) (Rabus 2003, Rodriguez et al. 2005, 2006). We estimated slope using the slope function from ERDAS Imagine (ERDAS Inc. 2010). We categorized slope as either <10% (5.7[degrees]) or [greater than or equal to] 10% (5.7[degrees]). We calculated aspect using the aspect function of ERDAS Imagine (ERDAS Inc. 2010) and classified each pixel into 1 of 2 categories: aspects between 315-45[degrees] (i.e., north) and aspects between 45-315[degrees] (i.e., east/south/west). We assumed that solar inputs would be lower on north facing slopes compared to east, south, and west facing slopes. Therefore, we combined slope and aspect into flat, slopes facing north, and slopes facing east/south/west for analysis. Detectable variation in [T.sub.a] as a function of elevation was not expected in VNP as most local relief is <30 m and maximum relief within the Peninsula is only 81 m. Therefore, elevation was not considered in our sampling design or subsequent modeling.
We developed a canopy cover model using the methodology outlined in the Great Lakes Inventory and Monitoring Network's Landscape Dynamics protocol (Kennedy and Kirschbaum 2010, Kennedy et al. 2010). Percent cover of trees, shrub, and ground layer was estimated at 30-m pixels in ArcMap (ESRI 2011) using high-resolution air photos taken in the spring (leaf-off) and summer (leaf-on) of 2008 with 0.15 and 1 m resolutions, respectively (Kirschbaum and Gafvert 2010). Estimates of canopy cover in each pixel were related to the normalized burn ratio (van Wagtendonk et al. 2004) calculated from the Landsat image corresponding to that time period to create a regression model of canopy cover. We categorized canopy cover as open (i.e., no or few canopy-forming trees), variable cover (i.e., non-forested, discontinuous canopy), <70% forest cover, 70-80% forest cover, and >80% forest cover. We classified vegetation cover type using the National Vegetation Classification System Subclass Level (deciduous, evergreen, mixed, woodland, shrub, or herbaceous) developed for VNP (Faber-Langendoen et al. 2007).
We developed a sampling matrix using the 3 sets of variables (vegetation cover type, canopy cover, slope/aspect). Each unique combination of the 3 variables was given an identifying code using ERDAS Imagine. A 30-m pixel raster map was created and converted to a polygon shapefile. Areas for each polygon were calculated and polygons <1.07 ha (12 pixels) were deleted to avoid sampling very small patches. We sampled at an intensity of 1 temperature logger for every 333 ha in the study area. We randomly selected polygons to sample from each of 38 unique combinations of vegetation cover type, canopy cover class, and slope/aspect class. We located the sample point near the centroid of the polygon to allow a sufficient buffer between adjacent polygons. We used alternate sites if the selected polygon centroid was <30 m (1 pixel) from the edge, field reconnaissance found that site characteristics were different from remotely-sensed data, or sites were otherwise inaccessible (e.g., flooding).
Black globe temperature loggers consisted of a data-logging thermocouple (Onset Computer Corporation, Bourne, Massachusetts, USA) inserted into a copper toilet tank float painted matte black (Fig. 1). We calibrated loggers for a minimum of 96 h to verify accuracy and resolution as compared to the stated equipment specifications of the logger ([+ or -] 0.21[degrees]C from 0[degrees]-50[degrees]C). All loggers were synchronized and programmed to record temperatures every 15 min for 1 year. At each sample point, we hung loggers 0.75 m above the ground and 15 cm from the trunk. Loggers were placed on the northeast side of trees to minimize direct solar radiation during the warmest time of day (Fig. 1). We used handheld field computers with GPS to verify that logger placement in the field was consistent with identified cover type and location within the cover type polygon. We used real-time GIS and measurements in the field to ensure we were within the identified cover type, canopy cover class, and slope/aspect category before deploying loggers. Loggers were deployed from June 2010 to July 2011, with periodic downloads to reduce risk of data loss. Data were screened to remove biased or failed measurements (e.g., faulty logger, damaged globe or logger, and snow-covered loggers).
We deployed an additional set of loggers from August 2011 to January 2012 to test for differences in position on slope. We randomly deployed 3 loggers in each of 9 combinations of cover type (deciduous, evergreen, and mixed), canopy cover class (<70%, 70-80%, >80% forested canopy), and slope position (top, mid-slope, and base); slopes ranged from 17-47%.
We analyzed data by season with full factorial repeated-measures ANOVA with type III sums of squares using SPSS 20 (IBM Corporation 2011). We defined seasons as spring (1 March-31 May), summer (1 June-31 August), fall (1 September-30 November), and winter (1 December-28 February). Each 15 min logger interval was treated as the response variable but controlled for repeated measures. Post-hoc pairwise comparisons were made using the Bonferroni method. Significance levels were set to P = 0.05 for all tests. Subsamples of the dataset were made to compare differences during the 3 warmest hours of the day (1300-1600 hr) and the 3 coldest hours of the day (0300-0600 hr). We used a similar statistical approach to test for differences in slope position for summer and winter only.
Based on model results, we assessed the availability of potential thermal refugia to moose across the Kabetogama Peninsula. We simulated moose home ranges by creating 25 random points within the study area and then buffering those points to approximate mean annual moose home ranges in VNP (48 [km.sup.2] [+ or -] 33.5 SD) as reported by Cobb et al. (2004). Home ranges varied in size due to the highly variable shoreline of the Kabetogama Peninsula. Within each simulated home range, we estimated the proportion of each habitat type (vegetation cover type, canopy cover class, slope/aspect) and used summary statistics to highlight availability of selected habitat features.
RESULTS
Open habitat types (shrub and herbaceous) were significantly warmer than forested habitat types during the summer (maximum difference = 3.38[degrees]C; Fig. 3) with the greatest difference occurring in the afternoon (maximum difference = 8.10[degrees]C; Table 1, Fig. 3). Open habitat types were also the coolest at night (maximum difference = 2.66[degrees]C; Table 1, Fig. 3). Mean [T.sub.o] during the summer did not differ among forested cover types for any of the time periods (Daily, Hot, Cold). Herbaceous cover types were warmer than forested cover types in the afternoon during the fall (maximum difference = 7.43[degrees]C; Table 1, Fig. 3). Temperatures in shrub cover types were intermediate between herbaceous and forested cover types. The amount of canopy influenced [T.sub.o] within forested cover types. Areas with >80% canopy coverage were cooler than those with <70% cover (Table 2, Fig. 4); [T.sub.o] in the 70-80% cover class was intermediate to these.
[FIGURE 3 OMITTED]
Slope/aspect influenced [T.sub.o] only during the 3 coldest hours of day in summer. Flat areas were cooler than East/South/West facing slopes (difference = 1.14[degrees]C; Table 3). North-facing slopes were intermediate between flat areas and East/South/West facing slopes. Slope/aspect had no influence on [T.sub.o] during the fall months. Winter temperature was only differentiated by slope/aspect during the afternoon with northern facing slopes cooler than both flat and east/south/west categories (Table 3, Fig. 5). Spring temperatures varied across slope/aspect categories during the afternoon hours. North-facing slopes were cooler than flat areas (Table 3, Fig. 5). [T.sub.o] was not different among slope positions in summer ([F.sub.2, 21] = 0.287, P = 0.755) or winter ([F.sub.2, 21] = 0.606, P = 0.556).
The majority of the Kabetogama Peninsula consists of forested cover types with >70 percent canopy cover and flat topography (Table 4, Fig. 6, 7). Simulated home ranges varied in size from 23-57 [km.sup.2] with a mean of 46 [km.sup.2] (SD = 10.1 [km.sup.2]). Potential summer refugia, such as high-canopy cover forests, were found in about 40% of simulated home ranges (Table 4). However, north-facing slopes are relatively limited in the study area and the percentage of north-facing slopes in simulated home ranges was <10% (Table 4, Fig. 7).
DISCUSSION
Vegetation cover type, percent canopy cover, and slope/aspect all influenced [T.sub.o], although differently depending on season and time of day. Vegetation cover type had the strongest influence on [T.sub.o] during summer months and fall afternoons. This was largely driven by open versus forested habitats, although we detected small but significant differences in [T.sub.o] within closed forest habitats, similar to other studies (e.g., McGraw et al. 2012). Amount of canopy cover significantly affected [T.sub.o] only during afternoons in the summer months. Forest type and the amount of canopy cover effectively combine to reduce the amount of solar radiation that reaches the forest floor and therefore can reduce heat loading from direct solar radiation (Demarchi and Bunnel 1993). Vegetation volume, hence canopy cover, is greatest in summer months, likewise solar angle is most direct during summer afternoons. Areas with thick vegetation and dense canopy cover may serve as ideal thermal refuge for moose during the day (DeMarchi and Bunnell 1995, Dussault et al. 2004, van Beest et al. 2012). Although some variation exists in the amount of forested habitat types with high canopy cover within our simulated home ranges, these habitat types do not seem limited in the study area.
[FIGURE 4 OMITTED]
[FIGURE 5 OMITTED]
[FIGURE 6 OMITTED]
Open cover types were cooler than forested cover types during the 3 coldest hours of the day. Dense vegetation and canopy cover actually retain heat within forested cover types while open cover types release more heat at night (Chen et al. 1993). Moose in central Norway use open habitat types at night and older forested stands during daytime (Bjorneraas et al. 2011). The availability of open cover types may be limited for some moose in the Kabetogama Peninsula.
[FIGURE 7 OMITTED]
We defined 4 equal seasons in our models based on calendar months rather than the timing of leaf phenology which varies annually in response to weather events, disease, drought, and other factors (Lechowicz 1984). As a consequence, our ability to detect significant differences may have been diminished for some variables, specifically canopy cover. Future studies of [T.sub.o] should consider incorporating important predictor variables that may change at relatively fine time scales. Also, canopy cover estimates were based on the leaf-on period, and may not accurately reflect the true amount of canopy cover during leaf-off periods for all cover types with a deciduous tree component.
The majority of the study area was flat and cooler at night than east/south/west facing slopes during summer, likely due to differences in radiant heat loss. Slope/aspect was the only significant influence on [T.sub.o] during winter months as well as spring afternoons. Slope/aspect may have a stronger effect on [T.sub.o] during winter months when the solar angle is at its lowest. These environments may serve as thermal refugia on warm days in winter and early spring as topographic exposure can influence maximum daily temperatures (Bolstad et al. 1998). Additionally, radiation received on flat and south facing slopes may be reflected to the body by the high reflectivity of snow in winter. More northerly locations may realize increased effect of slope/aspect, as well as areas with greater topographic relief. Moose strongly selected north-facing slopes in southwestern Alberta due to increased shade and browse availability (Telfer 1988). North-facing slopes make up less than 5 percent of our study area and across most simulated home ranges, suggesting this type of seasonal thermal refugia may be limited for moose in our study area.
We did not detect an effect of slope position on [T.sub.o], presumably because of the low topographic relief in the study area (Danielson et al. 1997). Areas with larger elevational gradients than our study area should include elevation as a variable due to the adiabatic lapse or rate change in temperature of an air mass as it changes with altitude (American Meteorological Society 2000). In certain studies elevation was the single strongest driver of temperature difference (e.g., Lookingbill and Urban 2003).
Moose use aquatic habitats for a variety of reasons including foraging, sodium acquisition, insect relief, and thermoregulation (Peek 2007). Aquatic habitats in our study area contain little to no canopy cover and related [T.sub.o] regimes are likely similar to that of open habitat types. Although moose using shallow, aquatic habitats during daytime may be exposed to direct solar radiation, they could mitigate heat loading by submerging in water.
Thermal variability exists at relatively fine scales across our study area due primarily to the fine mosaic of vegetation cover types, canopy coverage, and site aspect (Fig. 6, 7). We detected maximum differences in mean [T.sub.o] of [less than or equal to] 9[degrees]C across all habitat types during the warmest parts of summer days. Within forested habitat types, there was >2[degrees]C difference across canopy cover categories in summer. Slope/aspect accounted for as much as a 4[degrees]C difference in [T.sub.o] during winter and spring. Even small differences in the thermal environment may be relevant for achieving individual heat balance (Renecker and Hudson 1990).
The availability of thermal refugia will be of greater importance at the southern edge of moose range as mean annual temperature continues to rise with climate change (IPCC 2007). Behavioral responses to high [T.sub.a] include specific microhabitat use and activity shifts in other parts of moose range (Dussault et al. 2004, Broders et al. 2012, van Beest et al. 2012). To mitigate the effects of increasing [T.sub.a], managers should promote a variety of habitat types to provide adequate thermal refugia within a typical home range while meeting other life history requirements of moose (Peek 2007).
ACKNOWLEDGEMENTS
We thank B. Severud, J. Warmbold, C. Eckman, D. Morris, and N. Walker for assistance with field work and A. Kirschbaum for remote sensing support. This project was funded by Voyageurs National Park, a grant from the USGS-NPS Natural Resource Preservation Program, a grant from the U.S. National Park Service's Great Lake Research and Education Center, Bemidji State University, the Natural Resources Research Institute at the University of Minnesota Duluth, and the Environment and Natural Resources Trust Fund.
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Bryce Olson (1, 2), Steve K. Windels (1), Mark Fulton (2), and Ron Moen (3)
(1) National Park Service, Voyageurs National Park, 360 Highway 11 E, International Falls, Minnesota 56649; (2) Bemidji State University, 1500 Birchmont Drive NE, Bemidji, Minnesota 56601; (3) Natural Resources Research Institute, University of Minnesota, 5013 Miller Trunk Highway, Duluth, Minnesota 55811
Table 1. Mean daily (24-hour mean) operative temperatures
([degrees]C) across vegetation cover types in spring (1 March-31
May 2011), summer (1 June-31 August 2010), fall (1 September-30
November 2010), and winter (1 December 2010-28 February 2011),
Voyageurs National Park, Minnesota, USA. Mean operative
temperature for the 3 warmest (Hot) and 3 coldest (Cold) hours of
the day are also shown. Means followed by the same letter within
a row are not significantly different from each other.
Season Vegetation Cover Type
Deciduous Evergreen
Sprint Daily 3.97 [+ or -] 0.74 ab 2.89 [+ or -] 0.78 a
Hot 10.47 [+ or -] 0.90 a 7.95 [+ or -] 0.95 b
Cold -1.45 [+ or -] 0.65 a -1.71 [+ or -] 0.68 a
Summer Daily 18.32 [+ or -] 0.33 a 18.76 [+ or -] 0.35 a
Hot 23.03 [+ or -] 0.57 a 23.66 [+ or -] 0.60 a
Cold 13.76 [+ or -] 0.37 a 13.97 [+ or -] 0.39 ab
Fall Daily 5.56 [+ or -] 0.65 a 5.56 [+ or -] 0.69 a
Hot 9.97 [+ or -] 0.82 ab 9.26 [+ or -] 0.87 ab
Cold 2.48 [+ or -] 0.57 a 2.78 [+ or -] 0.60 a
Winter Daily -13.37 [+ or -] 0.24 a -12.99 [+ or -] 0.25 a
Hot -9.20 [+ or -] 0.40 ab -9.68 [+ or -] 0.43 a
Cold -16.17 [+ or -] 0.33 a -15.59 [+ or -] 0.35 a
Season Vegetation Cover Type
Mixed Woodland
Sprint Daily 4.28 [+ or -] 0.73 ab 3.53 [+ or -] 1.81 ab
Hot 10.29 [+ or -] 0.90 ab 9.23 [+ or -] 2.21 abc
Cold -1.25 [+ or -] 0.64 a -1.71 [+ or -] 1.59 a
Summer Daily 18.42 [+ or -] 0.33 a 19.33 [+ or -] 0.60 ab
Hot 23.13 [+ or -] 0.57 a 24.52 [+ or -] 1.04 a
Cold 13.54 [+ or -] 0.37 ab 14.68 [+ or -] 0.67 b
Fall Daily 5.66 [+ or -] 0.67 a 5.99 [+ or -] 1.18 a
Hot 9.77 [+ or -] 0.85 ab 10.48 [+ or -] 1.49 ab
Cold 2.63 [+ or -] 0.59 a 2.93 [+ or -] 1.03 a
Winter Daily -13.41 [+ or -] 0.25 a -13.25 [+ or -] 0.44 a
Hot -9.72 [+ or -] 0.42 a -9.15 [+ or -] 0.73 ab
Cold -16.02 [+ or -] 0.34 a -16.10 [+ or -] 0.60 a
Season Vegetation Cover Type
Shrub Herbaceous
Sprint Daily 5.26 [+ or -] 1.48 ab 6.70 [+ or -] 1.15 b
Hot 14.19 [+ or -] 1.80 ac 16.63 [+ or -] 1.40 c
Cold -1.64 [+ or -] 1.01 a -2.39 [+ or -] 1.30 a
Summer Daily 21.25 [+ or -] 0.60 bc 21.70 [+ or -] 0.42 c
Hot 29.84 [+ or -] 1.04 b 31.13 [+ or -] 0.73 b
Cold 12.88 [+ or -] 0.47 ab 12.02 [+ or -] 0.67 a
Fall Daily 6.38 [+ or -] 1.18 a 7.49 [+ or -] 0.83 a
Hot 14.24 [+ or -] 1.49 ac 16.69 [+ or -] 1.05 c
Cold 2.23 [+ or -] 0.73 a 1.52 [+ or -] 1.03 a
Winter Daily -14.58 [+ or -] 0.44 a -14.20 [+ or -] 0.36 a
Hot -9.08 [+ or -] 0.73 ab -6.95 [+ or -] 0.60 b
Cold -17.22 [+ or -] 0.49 a -17.30 [+ or -] 0.60 a
Season Significance
[F.sub.5,85] P Value
Sprint Daily 0.86 0.496
Hot 2.59 0.051
Cold 0.14 0.934
Summer Daily 2.93 0.029
Hot 7.10 < 0.001
Cold 2.86 0.045
Fall Daily 0.32 0.862
Hot 3.00 0.026
Cold 0.35 0.786
Winter Daily 1.70 0.166
Hot 2.14 0.091
Cold 1.19 0.324
Table 2. Mean daily (24-hour mean) operative temperatures
([degrees]C) across varying amounts of canopy cover by season in
spring (1 March-31 May 2011), summer (1 June-31 August 2010),
fall (1 September-30 November 2010), and winter (1 December 2010-
28 February 2011), Voyageurs National Park, Minnesota, USA. Mean
operative temperature for the 3 warmest (Hot) and 3 coldest
(Cold) hours of the day are also shown. Means followed by the
same letter within a row are not significantly different from
each other.
Season Canopy Cover
Open Variable
Spring Daily 6.70 [+ or -] 1.15 a 4.40 [+ or -] 1.17 a
Hot 16.46 [+ or -] 1.35 a 11.67 [+ or -] 1.38 a
Cold -1.644 [+ or -] 1.01 a -2.05 [+ or -] 1.03 a
Summer Daily 21.70 [+ or -] 0.42 a 20.29 [+ or -] 0.42 a
Hot 30.93 [+ or -] 0.67 a 27.09 [+ or -] 0.67 b
Cold 12.88 [+ or -] 0.47 a 13.35 [+ or -] 0.47 a
Fall Daily 7.48 [+ or -] 0.83 a 6.18 [+ or -] 0.83 a
Hot 16.39 [+ or -] 1.01 a 12.36 [+ or -] 1.01 a
Cold 2.23 [+ or -] 0.73 a 2.23 [+ or -] 0.73 a
Winter Daily -14.20 [+ or -] 0.36 a -13.92 [+ or -] 0.31 a
Hot -7.11 [+ or -] 0.53 a -8.82 [+ or -] 0.46 a
Cold -17.22 [+ or -] 0.49 a -16.70 [+ or -] 0.42 a
Season Canopy Cover
< 70% 70-80%
Spring Daily 4.47 [+ or -] 0.75 a 3.14 [+ or -] 0.77 a
Hot 10.99 [+ or -] 0.92 ab 8.66 [+ or -] 0.94 bc
Cold -1.27 [+ or -] 0.66 a -1.87 [+ or -] 0.68 a
Summer Daily 19.00 [+ or -] 0.35 b 18.54 [+ or -] 0.34 bc
Hot 24.39 [+ or -] 0.61 c 23.24 [+ or -] 0.59 cd
Cold 13.49 [+ or -] 0.39 a 13.95 [+ or -] 0.38 a
Fall Daily 5.57 [+ or -] 0.69 a 5.73 [+ or -] 0.67 a
Hot 10.25 [+ or -] 0.88 b 9.79 [+ or -] 0.85 b
Cold 2.40 [+ or -] 0.61 a 2.69 [+ or -] 0.59 a
Winter Daily -13.45 [+ or -] 0.26 a -13.28 [+ or -] 0.25 a
Hot -8.94 [+ or -] 0.43 a -9.70 [+ or -] 0.42 b
Cold -16.29 [+ or -] 0.35 a -15.98 [+ or -] 0.34 a
Season Canopy Cover Significance
> 80% [F.sub.4,85] P Value
Spring Daily 3.47 [+ or -] 0.72 a 0.59 0.561
Hot 9.00 [+ or -] 0.88 bc 1.38 0.265
Cold -1.31 [+ or -] 0.64 a 0.25 0.783
Summer Daily 17.96 [+ or -] 0.31 c 2.48 0.093
Hot 22.18 [+ or -] 0.54 d 3.71 0.031
Cold 13.83 [+ or -] 0.35 a 0.38 0.688
Fall Daily 5.48 [+ or -] 0.65 a 0.04 0.965
Hot 8.95 [+ or -] 0.81 b 0.62 0.541
Cold 2.81 [+ or -] 0.56 a 0.13 0.879
Winter Daily -13.07 [+ or -] 0.24 a 0.57 0.569
Hot -10.02 [+ or -] 0.40 b 1.75 0.184
Cold -15.50 [+ or -] 0.33 a 1.39 0.259
Table 3. Mean daily (24-hour mean) operative temperatures
([degrees]C) across slope-aspect categories in spring (1 March-
31 May 2011), summer (1 June-31 August 2010), fall (1 September-
30 November 2010), and winter (1 December 2010-28 February 2011),
Voyageurs National Park, Minnesota, USA. Mean operative
temperature for the 3 warmest (Hot) and 3 coldest (Cold) hours of
the day are also shown. Means followed by the same letter within
a row are not significantly different from each other.
Season Slope/Aspect
Flat East/South/West
Spring Daily 4.15 [+ or -] 0.39 a 4.48 [+ or -] 0.88 a
Hot 11.14 [+ or -] 0.48 a 11.05 [+ or -] 1.07 ab
Cold -1.79 [+ or -] 0.35 a -1.07 [+ or -] 0.77 a
Summer Daily 18.79 [+ or -] 0.16 a 19.01 [+ or -] 0.37 a
Hot 24.73 [+ or -] 0.28 a 24.03 [+ or -] 0.65 ab
Cold 13.03 [+ or -] 0.18 a 14.17 [+ or -] 0.42 b
Fall Daily 5.32 [+ or -] 0.33 a 6.34 [+ or -] 0.76 a
Hot 10.60 [+ or -] 0.41 a 10.92 [+ or -] 0.96 a
Cold 1.86 [+ or -] 0.29 a 3.08 [+ or -] 0.67 b
Winter Daily -13.41 [+ or -] 0.12 a -12.84 [+ or -] 0.28 a
Hot -8.89 [+ or -] 0.21 a -8.35 [+ or -] 0.47 a
Cold -16.18 [+ or -] 0.17 a -15.84 [+ or -] 0.39 a
Season Slope/Aspect Significance
North [F.sub.2,85] P Value
Spring Daily 2.87 [+ or -] 0.91 a 0.61 0.551
Hot 7.27 [+ or -] 1.10 b 3.36 0.045
Cold -1.57 [+ or -] 0.80 a 0.20 0.816
Summer Daily 18.35 [+ or -] 0.40 a 2.16 0.125
Hot 22.30 [+ or -] 0.69 b 1.76 0.181
Cold 14.11 [+ or -] 0.45 ab 4.92 0.011
Fall Daily 5.56 [+ or -] 0.79 a 1.54 0.225
Hot 8.55 [+ or -] 0.99 a 1.50 0.233
Cold 3.07 [+ or -] 0.69 ab 2.60 0.084
Winter Daily -13.74 [+ or -] 0.29 a 2.45 0.097
Hot -11.22 [+ or -] 0.49 b 10.24 < 0.001
Cold -15.99 [+ or -] 0.40 a 0.041 0.959
Table 4. Mean, standard deviation (SD), minimum (Min), and
maximum (Max) percentage of vegetation cover type, canopy cover,
and slope/aspect categories in 25 simulated moose home ranges in
Voyageurs National Park, Minnesota, USA.
Variable Simulated Home Range
Mean SD Min Max
Vegetation Evergreen 13 5 6 23
Cover Type Deciduous 34 11 20 58
Mixed 27 7 14 48
Woodland 4 2 1 7
Shrub 6 4 2 16
Herbaceous 16 8 8 36
Canopy High 41 6 29 52
Cover Class Med 23 6 13 34
Low 10 4 4 15
Variable 10 3 5 16
Open 16 8 8 36
Slope/Aspect East/South/West 8 4 2 13
Flat 87 5 79 96
North 5 2 2 8
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| Author: | Olson, Bryce; Windels, Steve K.; Fulton, Mark; Moen, Ron |
|---|---|
| Publication: | Alces |
| Article Type: | Statistical table |
| Geographic Code: | 1U4MN |
| Date: | Jan 1, 2014 |
| Words: | 6497 |
| Previous Article: | Pre-parturition movement patterns and birth site characteristics of moose in Northeast Minnesota. |
| Next Article: | Diversity and abundance of terrestrial gastropods in voyageurs national park, MN: implications for the risk of moose becoming infected with... |
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