POINT ARENA MOUNTAIN BEAVER (APLODONTIA RUFA NIGRA) SELECTS COOL CLIMATES AT FINE SPATIAL SCALES.
The PAMB has a small, disjunct distribution of approximately 235 [km.sup.2] along the Mendocino Coast in California. Habitat loss and modification as a result of livestock grazing and localized urban development within this limited range is considered one of the species' primary threats (Fig. 1, USFWS 1998). Mountain Beavers are typically found in cool, dark forest understory near riparian areas (Beier 1989; Feldhamer and others 2003). However, PAMBs occupy areas on the coast that are notably different from the species' primary habitat, including coastal scrub and coastal dunes (Zielinski and Mazurek 2006). Although the temperature is moderated by summer fog, these areas have very little canopy cover and tend to be directly exposed to the sun. To persist in these warmer, drier habitats, PAMB may have developed local adaptations. Alternatively, PAMB on the coast may be selecting microclimates within their range that meet the typical needs of the species. Understanding habitat limitations at fine scales will be crucial in managing this species now and into the future.
Species distributions models (SDMs) have increasingly been used to understand the current geographic range and habitat requirements of taxa, particularly rare and sensitive species. Using a variety of statistical approaches, SDMs relate presence and either absence or background/pseudo-absence points to spatially explicit predictor variables to predict a species' distribution (Elith and others 2006). Although generally effective at broad scales, SDMs are often unable to accurately assess fine scale details of distribution (Pearson and Dawson 2003; Guisan and Thuiller 2005). This is likely a result of the spatial mismatch between readily available predictor variables and the scale at which most species experience their environment. This mismatch in scale may be especially important to consider for species with limited mobility (Pearson and Dawson 2003; Guisan and Thuiller 2005, Potter and others 2013). For a species like the Mountain Beaver, whose average home range estimates vary from 0.058 to 4.16 ha, climatic data at the 1-[km.sup.2] or even 900-[m.sup.2] resolution may not adequately capture variation in microhabitats available to them (Zielinski and Mazurek 2006; Arjo and others 2007).
Recent advances in downscaling climate data have allowed for the creation of fine-resolution environmental predictors. Fine-scale (often sub-meter) topographic data measured with aerial Light Detection and Ranging (LiDAR) sensors combined with microclimate data loggers have successfully been used to create temperature models that better correspond to the scale at which a species experiences the environment (George and others 2015; Frey and others 2016). Incorporating climate variables at an appropriate scale can improve predictive ability and provide insight into direct mechanisms by which Mountain Beavers may respond to climate change. For example, in coastal Mendocino County, a 1-[km.sup.2] area may appear to have quite hot maximum summer temperatures. However, within this area, Mountain Beavers will often have access to cooler ravines shaded by a dense redwood canopy. The difference in temperature between a south-facing, open meadow and a north-facing, old riparian forest could be up to 20[degrees]C in summer. Down-scaling topographic-driven climate data from 1-[km.sup.2] to 1-[m.sup.2] scale can provide much more accurate estimates of the climate actually experienced by a Mountain Beaver within the few hectares of its home range. However, owing to the amount of data required in fine scale models, they are generally not feasible for range-wide modeling for most species. As a climatically sensitive subspecies with a limited range, PAMB are an ideal organism for studying range restrictions based on microclimate variables.
Our objective was to determine whether PAMB have adapted to habitats with hotter, drier temperatures than elsewhere in the species' range, or if the sub-species is able to find microclimates within this hotter landscape that match conditions throughout their distribution. We developed fine-scale climate surfaces using temperatures recorded from data loggers and topographic variables calculated from LiDAR data. Occurrence locations for PAMB were obtained from personal surveys and a previous study (Zielinski and others 2015). The fine-scale climate layers were incorporated with PAMB occurrence data in Maxent to model fine-scale habitat suitability throughout their range. We then used an independent data set to evaluate the accuracy of these models. We expected that PAMB were able to persist in apparently hotter, drier areas by taking advantage of microhabitat features that mirrored the cooler, wetter conditions in which the species is normally found. If so, we expected that the fine-scale model of PAMB distribution would identify a negative relationship between PAMB presence and: (1) mean daily maximum temperature during the summer-autumn; (2) mean daily minimum temperature during the summer-autumn; and (3) mean hours above PAMB upper critical temperature in the summer-autumn.
The range of PAMB is currently limited to 235-[km.sup.2] area along the Mendocino County coast in California (Zielinski and others 2015). The area is comprised of a variety of habitat types including coastal dunes, coastal scrub, riparian, and non-native grasslands along the coast, and forests and riparian areas inland (LANDF1RE 2017). Land use varies, with approximately 25% of the range owned by timber companies, with the rest divided among public land, ranches and dairy farms, and other private parcels (ENPLAN 2017). The weather is moderate, with annual mean temperatures between 10 and 12[degrees]C (Fick and Hijmans 2017). Warmer temperatures are typically observed May-November (NOAA 2012). Summers have relatively frequent fog and low-level clouds, particularly along the coast (Torregrosa and others 2016).
In the summer of 2017, we conducted burrow surveys following USFWS protocols to determine patterns of occupancy (USFWS 2017). We also deployed temperature data loggers to develop fine-scale, spatially explicit climate layers. Individual PAMB construct a burrow system with multiple burrow entrances that are distinct and easily identified (Camp 1918; Feldhamer and others 2003; Zielinski and others 2015). Visual surveys for burrows were targeted at riparian areas and in areas where previous surveys had located burrows. Active burrows were classified as having fresh dirt and being clean of vegetation and other debris. PAMB burrow areas tend to have multiple entrances, and we did not attempt to classify individual openings as active or inactive. Instead, if a majority of burrow openings appeared active, we classified the entire area as active. For each active burrow area detected, a data logger was placed near one of the burrow entrances. Using ArcMap (Environmental Systems Research Institute, Inc., Redlands, CA), a grid of points was placed over the range of PAMB, such that each point was 150 m from the nearest neighbor. From this grid, we selected 2 subsets, 1 inland and 1 coastal, of 100 points for PAMB surveys and data logger deployment (Fig. 2). Sites for the coastal grid were randomly selected in R (George and others 2015). Sites were reselected if they were unsuitable for data logger deployment (such as in a river or inaccessible owing to topography or vegetation). Because of accessibility constraints (rugged terrain and landowner permission), points for the inland grid were selected using a stratified approach (Fridley 2009; Frey and others 2016). Inland points were placed along transects that were selected based on accessibility, presence of PAMB, and environmental variables. Histograms of elevation, slope, and aspect for the PAMB range were visually compared to grid points to ensure the range of environmental variables was reflected in the deployment sites. Additionally, as data loggers were placed in the field, the surrounding area was surveyed for any Mountain Beaver sign.
Microclimate data were collected using iButton[R] (Maxim Integrated, San Jose, CA, USA) temperature loggers to create a downscaled microclimate model of PAMB habitat. Each data logger was placed in a shield constructed from PVC and fiberglass screen that allowed for airflow while limiting the effect of direct solar radiation and precipitation. All data loggers were deployed during the summer, checked once in late summer-early autumn, and collected in early winter. Each data logger recorded temperatures on the same 3.5-h cycle to maximize battery life and capture different times of day. This deployment from July-December captured most of the typical warm season in the PAMB range (NOAA 2012).
We then developed a series of topographic predictor variables using LiDAR data and other available GIS spatial data layers to generate a suite of range-wide, fine-scale climatic variables. Specifically, a Digital Terrain Model (DTM), Digital Surface Model (DSM), and Canopy Height Model (CHM) were derived from LiDAR data from NASA's Northern San Andreas Fault Study (NASA 2003). Environmental predictor variables that are generally accepted to affect temperature were calculated using the DTM, DSM, and CHM at 2 spatial grains, 10 and 100 m, to account for the effects of these variables at a local and more widespread scale (Table 1; Frey and others 2016). As a proxy for fog, we calculated distance to coast from a national US shapefile at a 1-m scale (GADM.org). We summarized 3 response variables for each data logger during the entire 6-mo deployment period: minimum, mean, and maximum temperature. We calculated an additional response variable, upper critical temperature ([CT.sub.max]), as the average number of times per day a data logger recorded temperatures above 32[degrees]C, which is the lower temperature limit that causes mortality in Mountain Beavers with prolonged exposure (Johnson 1971).
We used boosted regression trees in R package 'dismo' (Hijmans and others 2017) to analyze the relationship between topographic data at the data logger locations and climate data recorded on the data logger; we projected these climate models onto topographic data throughout PAMB range. Boosted regression trees can identify nonlinear relationships and explore complex interactions, and they can be particularly useful for a large number of predictor variables. Models were fitted using a tree complexity of 3 to 5 and a bag fraction of 0.75 owing to the small sample size (n = 168). All models were initially run with a learning rate of 0.005 and decreased until at least 1000 trees were used to fit the model (Elith and others 2008). Models were evaluated using cross validation, and we then simplified the top model by dropping non-informative predictor variables using methods outlined in Elith and others (2008). As the purpose of simplification is to improve the performance of the model, when the simplified model did not outperform the un-simplified model, the unsimplified model was considered the top model (Elith and others 2008). The models were also tested for correlation between predicted and observed values using 10-fold cross validation. Low correlations between predicted and observed values indicate over-fitting of the model; although there are no accepted target values for correlations, Frey and others (2016) specified that values >0.6 were "high."
Finally, we combined the occurrence data collected for PAMB and points from a previous study of PAMB distribution (Zielinski and others 2015) with the fine-scale climate data to construct local distribution models throughout PAMB range using Maxent in package 'dismo' (Hijmans and others 2017). Methods for distinguishing active from inactive burrow areas and documenting locations from the earlier study were substantially the same as ours. The mean, maximum, minimum, and critical temperature layers were used as predictor variables. Initially we considered all possible combinations of these 4 variables in our candidate set. Next, we filtered the set so that only variables with Pearson correlation coefficient between -0.5 and 0.5 were included in the same model. We evaluated the role of model complexity by varying the "beta" parameter in Maxent from 0.5 to 3.0 in steps of 0.5. The beta multiplier varies the number of different features used to relate habitat suitability to the predictor variables. A higher beta parameter results in a simpler model, and thus fewer total features used. Because of Maxent's tendency to overfit, we used model selection based on AICc to balance model fit with complexity (Warren and Seifert 2011). Accuracy of this model was also assessed using the Continuous Boyce Index (CBI), following the methods used for the broad-scale models (Hirzel and others 2006). Additional presence points provided from a variety of sources and collated by USFWS were used as an independent testing data set but were not used as training data owing to uncertainty in accuracy of location data.
We documented 20 PAMB active burrow areas. We used an additional 35 burrow areas from Zielinski and others (2015) for a total of 55 PAMB occurrences for the suitability models. We deployed a total of 195 iButton data loggers during the summer of 2017 (Fig. 2). Because of theft or loss, we removed 27 data loggers from the analysis, leaving 168 locations for this analysis. Several of the data loggers recorded temperatures above a reasonable limit for the area, likely resulting from solar radiation and heat reflection from the soil. We did choose to include these for analysis, as they likely captured relative patterns of temperature, if not reliable absolute values (Terando and others 2017).
Creation of fine-scale climate layers from the data loggers and LiDAR data resulted in accurate models for mean, minimum, and [CT.sub.max] (Table 2, Fig. 3). Deviance for these 3 layers ranged from 0.023 ([CT.sub.max]) to 2.240 (minimum). However, the maximum temperature model had high deviance. The cross-validation correlation values were moderate (ranging from 0.586 to 0.794), indicating that there was some agreement between training and testing data.
The top model for PAMB distribution included mean temperature and [CT.sub.max] (Table 3, Fig. 4). Maximum temperature was too correlated with minimum temperature (0.71) and [CT.sub.max] (0.90), as were minimum temperature and [CT.sub.max] (0.88), therefore no models that included these variables together were considered. The top 5 models all included mean temperature, 4 included [CT.sub.max] with varying beta values, and 1 incorporated minimum temperature (Table 3). Maximum temperature was not included in any of the best-supported models. In the top model, mean temperature had a greater contribution (94.3%) than [CT.sub.max] (5.7%). Suitability decreased as mean temperature increased above 12.0[degrees]C, and decreased as daily readings above [CT.sub.max] increased (Fig. 5). The model had an AUC of 0.880. The mean CBI value for the top model was 0.86 when the original data were divided into training and testing datasets. The mean CBI value was 0.99 when the additional USFWS points were used as the testing dataset.
In this study, we created species distribution models for PAMB at a scale that reflects how they interact with their environment. A previous PAMB habitat modeling study pointed to the need to assess critical habitats for management and recovery of this taxon (Zielinski and others 2015), and our study presents a novel estimate of habitat suitability driven by climatic variables at a scale appropriate for the species. Such models have long been a tool for conservation and recovery of endangered species (Eads and others 2011; D'Elia and others 2015). Several studies have shown that increased grain size decreases the accuracy of SDMs (Gottschalk and others 2011; Song and others 2013). This does not discount the need for coarser models, as they can often be used in conjunction with fine-scale models to increase understanding of factors limiting a species distribution at multiple scales (Fournier and others 2017). These fine-scale models provide insight into how PAMB is distributed through its range, whereas coarser models may elucidate information on why PAMB is limited to such a small area.
Similar to other climatically sensitive species, PAMBs appear to select habitat at a microclimatic scale (Wilkening and others 2011; Shi and others 2014; Varner and Dearing 2014). The fine-scale SDM showed that PAMB inhabited cooler microclimates within their range. These microclimates may be particularly important at range margins such as the conditions experienced by PAMB (Ray and others 2016). As temperature and moisture are believed to be 2 of the primary environmental variables limiting Mountain Beaver distribution, incorporating fine-scale humidity, fog, and precipitation in future models may help to further elucidate how PAMB select habitats (Nungesser and Pfieffer 1965; Johnson 1971).
A previous study on habitat suitability for PAMB used 25-ha sample units, likely leading to an overestimation of suitability in some areas and an underestimation in others (Zielinski and others 2015). Both models showed a clear affinity for riverine corridors; however, our fine-scale model showed increased connectivity between suitable areas, particularly in the southern portion of their range. The pattern of suitability in the northern portion of the range is more strictly defined by riverine corridors, whereas in the south their distribution appears to be more diffuse. This may be a result of the increased fog in the southern portion of the range, but warrants further research (Torregrosa and others 2016). Maintaining and increasing connectivity is critical for the survival of this species, and these suitability studies provide insight into high-priority areas, such as riverine corridors and the southern portion of the range.
Microclimate appeared to be a driving factor in PAMB habitat selection; however, this does not exclude the possibility of local adaptations, and there are multiple mechanisms in which this adaptation can be realized. Behavioral adaptations, such as shifting foraging activities to cooler parts of the day, have been documented in other climatically sensitive species (for example, Pikas [Ochotona princeps]; Smith 1974). Activity patterns of Mountain Beavers have only been studied in the subspecies californica, which were active at various times throughout the day and night with increased activity and ranging at night. During summer, these Mountain Beavers shifted to more nocturnal activity, potentially to avoid increased temperatures (Ingles 1959). This shift may be more conspicuous in populations living in areas with hotter temperatures, as PAMB do, and may occur seasonally or daily as weather conditions, such as fog, shift. Future studies on differences in active periods between subspecies could provide insight into behavioral adaptations.
Genetic adaptations may also allow PAMB to occupy atypical habitats. Genetic analyses support grouping the 7 subspecies of Mountain Beaver into 5 distinct clades. The Point Arena Mountain Beaver (A. r. nigra), Point Reyes Mountain Beaver (A. r. phaea), and Humboldt Mountain Beaver (A. r. humboldtiana) form a closely related California coastal clade, whereas the remaining 4 subspecies (A. r. olympica, A. r. rufa, A. r. califomica, A. r. pacifica) remain in separate clades (Piaggio and others 2013; Fig. 1). PAMB can be further divided into 3 distinct genetic groupings, 2 of which exist only on the coast (Zielinski and others 2013). The 3rd grouping primarily occupies inland habitats, which more closely resemble other Mountain Beaver habitat. It is possible that the coastal groupings may be more adapted to hotter, drier climates and are therefore critical to preserve in the face of climate change. A study into the physiology of California coastal Mountain Beavers could provide valuable insight into whether this species has adapted to a hotter, drier climate or if microclimate selection is enough to sustain them.
Although this study focused on the impacts of abiotic limits on Mountain Beaver distribution, there are likely biotic variables that also influence this species' distribution. Biotic variables are recognized as important factors in predicting distribution shifts in response to climate change; however, there is debate over their relative importance at broad scales (Pearson and Dawson 2003; Wisz and others 2013). Incorporating biotic factors, such as predation, competition, and dispersal abilities, into SDMs, particularly fine-scale SDMs, for Mountain Beavers may improve our understanding of how this species will respond to climate change (Wisz and others 2013).
This work was partially funded by cooperative agreements with the Bureau of Land Management and the United States Fish and Wildlife Service. We are grateful for the insight on PAMB survey techniques and study design from WJ Zielinski, K Fitts, R Douglas, H Ross, G Schmidt, J Hunter, M Szykman Gunther, and J Szewczak. Thanks also to our field technicians T Yanin and S Scherbinski. This project would not have been possible without access to lands owned or administered by R Douglas and H Ross (Mendocino Redwood Company), T Fuller (California State Parks), W Standley and K Barnitz (US Bureau of Land Management), N Houtz (Mendocino Land Trust), H Newberger (The Conservation Fund), B Morrison (Soper Wheeler Company), F Fedeli, T Shi, B Lotan, N Pinette, K Fusek, and B Heick. Additional thanks to S Cardimona for coordinating field housing.
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JENNIE JONES SCHERBINSKI AND WILLIAM T BEAN
Humboldt State University, 1 Harpst St., Arcata, CA 95521 USA; email@example.com; firstname.lastname@example.org
Submitted 2 January 2019, accepted 24 June 2019. Corresponding Editor: Robert Hoffman.
TABLE 1. Predictor variables and the data scales used to calculate fine-scale temperature models from the Digital Terrain Model (DTM), Digital Surface Model (DSM), and Canopy Height Model (CHM). These variables were selected as they likely affect air temperature (Frey and others 2016) Variable Spatial scale Description Distance to coast 1 m Euclidean distance from coast, proxy for fog Mean cover 10 m/100 m Mean percent cover from DTM and DSM SD cover 10 m/100 m Standard deviation of percent cover from DTM and DSM Mean eastness 10 m/100 m Mean sin (aspect *pi/180), between -1 and 1, DTM Mean elevation 10 m/100 m Mean elevation, DTM SD elevation 10 m/100 m Standard deviation of elevation, DTM Elevation range 10 m/100 m Range of elevation values, DTM Mean northness 10 m/100 m Mean cos(aspect*pi/180), between -1 and 1, DTM Mean slope 10 m/100 m Mean slope (%) from DTM SD slope 10 m/100 m Standard deviation of slope (90 from DTM Topo Index 10 m/100 m Difference in elevation between the point and mean elevation, indicating local low and high points, DTM Mean veg height 10 m/100 m Mean vegetation height, CHM SD veg height 10 m/100 m Standard deviation of vegetation height, CHM TABLE 2. Model settings and performance metrics for the top model for each temperature variable, and whetherthe top model was simplified by removing uninformative predictor variables. Variable Tree Learning No. trees Deviance Dev SE CV corr complex rate Mean 5 0.005 1000 0.517 0.053 0.784 Max 3 0.002 1300 33.258 2.206 0.64 Min 5 0.002 1400 2.24 0.292 0.794 CTmax 5 0.002 1300 0.023 0.004 0.586 Variable CV SE Simplified Mean 0.041 No Max 0.034 No Min 0.029 Yes CTmax 0.101 Yes TABLE 3. Model selection table for PAMB microclimate suitability. Only the top 5 models are shown. Mean = mean daily temperature (July-December); CTmax = mean daily temperature recordings above 32[degrees]C; Min = minimum temperature (July-December). (Note: the beta multiplier determines the number of parameters Maxent uses to relate the listed predictors to habitat suitability; therefore, 2 models with the same predictors will vary with different beta multipliers. In this case, a simpler model IK = 4] was better supported than a more complex model [K = 6] using the same predictors). Model Beta Log K AICc [DELTA]AICc AICc multiplier likelihood weight Mean + CTmax 3.0 -442.2641 4 893.33 0.00 0.65 Mean + CTmax 2.0 -440.9083 6 895.57 2.23 0.21 Mean + CTmax 2.5 -441.5078 6 896.77 3.44 0.12 Mean + CTmax 1.5 -438.1371 10 901.27 7.95 0.01 Mean + Min 3.0 -445.2443 6 904.24 10.91 <0.01
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|Author:||Scherbinski, Jennie Jones; Bean, William T.|
|Publication:||Northwestern Naturalist: A Journal of Vertebrate Biology|
|Date:||Dec 22, 2019|
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