Local and Landscape-Scale Influences on the Occurrence and Density of Dicamptodon Aterrimus, the Idaho Giant Salamander

Local and Landscape-Scale Influences on the Occurrence and Density of Dicamptodon aterrimus, the Idaho Giant Salamander

1 ADAM J. SEPULVEDA AND WINSOR H. LOWE

Division of Biological Sciences, University of Montana, 32 Campus Drive #4824, Missoula, Montana 59812 USA

ABSTRACT.—Species distribution and abundance depend on a balance between local and landscape-scale processes. To successfully manage populations in regions with anthropogenic disturbances and habitat fragmentation, an understanding of important processes at each of these spatial scales is important. We used a model selection approach to identify an effective spatial scale to manage the Idaho Giant Salamander, Dicamptodon aterrimus. We used data from field surveys to compare support for local and landscape-scale models that explain D. aterrimus occurrence and density in 40 streams distributed throughout the Lochsa River basin, Idaho. Local-scale models included covariates that reflect patch quality. Landscape-scale models included variables that reflect predictions from metapopulation theory about the importance of patch size, connectivity, and fragmentation. Our results suggest that landscape-scale processes are important controls on D. aterrimus occurrence and that this species has broad habitat requirements within streams. Specifically, we found that probability of D. aterrimus occurrence was highest in roadless drainages and lowest in spatially isolated streams and in drainages with high old-growth forest density. Surprisingly, we found that D. aterrimus density was greatest in streams with a high proportion of embedded substrate and fine sediment. The positive association with embedded substrate may reflect adaptation to a high frequency of natural disturbances, such as landslides, in our study area. We suggest that management and conservation efforts for this species focus on protecting roadless areas and restoring stream connectivity in human-impacted areas, rather than on only improving habitat quality within streams.

Species distribution and abundance depend amphibian populations, which are suffering on a balance between local and landscape-scale global declines (Stuart et al., 2004). Landscape- processes (Ricklefs, 1987; Lawton, 1999). Local scale processes are often given priority because processes are generally viewed as controls on amphibians are frequently assumed to form survival and reproduction associated with metapopulations, where dispersal among local abiotic and biotic conditions within patches populations promotes persistence (reviewed in (MacArthur and Levins, 1964; Shurin and Allen, Alford and Richards, 1999; Storfer, 2003; Smith 2001). Landscape-scale processes are related to and Green, 2005). Management to maintain the spatial arrangement of habitat patches and metapopulations might favor protecting large the movement of individuals among those areas with multiple patches and connectivity patches (Dunning et al., 1992; Hanski and among these patches. However, recent reviews Gilpin, 1997). Landscape-scale processes drive indicate that the assumption that landscape- the physical dynamics of a system (Benda et al., scale processes are most critical to amphibian 2004) and influence population persistence persistence is often inaccurate (Marsh and through resource availability and use (Wiens, Trenham, 2001; Storfer, 2003; Smith and Green, 1989; Dunning et al., 1992), colonization of new 2005), and numerous studies have demonstrat- patches and recolonization of previously occu- ed that amphibian distribution and abundance pied patches (Levins, 1970; Brown and Kodric- are best predicted by local factors (Bradford et Brown, 1977), and can subsidize patches with al., 2003; Knapp et al., 2003) or a mix of local and declining populations (e.g., Pulliam, 1988; landscape-scale factors (Sjogren-Gulve, 1994; Lowe, 2003). To successfully manage popula- Lowe and Bolger, 2002). In cases where local tions in regions with anthropogenic disturbance conditions determine persistence, management and habitat fragmentation, an understanding of might focus on local environmental factors that important processes at each spatial scale is influence patch quality, such as substrate critical. composition, so that local sites can support Identifying an effective scale of management self-sustaining populations (Petranka and Hol- is especially important for the conservation of brook, 2006). These contrasting views on an effective spatial scale for managing amphibian populations underscore the need for studies 1 Corresponding Author. Email: adam.sepulveda@ assessing controls on amphibian distribution mso.umt.edu and abundance at multiple spatial scales. 470 A. J. SEPULVEDA AND W. H. LOWE

Management at an incorrect spatial scale can stream amphibians will help in developing result in ineffective conservation (e.g., Novinger effective conservation strategies. and Rahel, 2003). Here, we used model selection to identify an There is a general lack of information on the effective spatial scale to manage a stream spatial structure and conservation status of salamander species, Dicamptodon aterrimus (Ida- stream amphibian populations, even though ho Giant Salamander), which is listed as a these species can make up over 95% of stream species of concern in the northern Rocky vertebrate biomass (Hawkins et al., 1983; Peter- Mountains. Little is known about D. aterrimus, man et al., 2008) and are thought to play a key but available information suggests that its role in ecosystem dynamics (Davic and Welsh, distribution is patchy across and within stream 2004). Prior studies on how spatial structure drainages (Carstens et al., 2005). In the region (i.e., the number and location of patches in a where D. aterrimus occurs, many stream drain- landscape) influences local population dynam- ages have undergone timber harvest and road ics have focused primarily on pond-breeding building, anthropogenic disturbances that can amphibians (Sjogren-Gulve, 1994; Trenham et reduce habitat quality within streams and al., 2001), whereas most studies on stream reduce population connectivity among streams amphibians have focused on habitat associa- (Carr and Fahrig, 2001; Fagan, 2002). Addition- tions at multiple spatial scales (e.g., Welsh and ally, these drainages experience natural distur- Ollivier, 1998; Lowe and Bolger, 2002; Stoddard bances such as fire, floods, and landslides, and Hayes, 2005) and biotic interactions at fine which can create local extinctions of stream spatial scales (Hairston, 1987; Sih et al., 1992). organisms in suitable habitat patches (e.g., The influence of spatial structure on local Welsh and Ollivier, 1998; Dunham and Rieman, population dynamics has rarely been consid- 1999; Pilliod et al., 2003). We used data from ered for stream amphibians (but see Lowe, 2003; field surveys to compare support for local and Lowe et al., 2006), even though they occupy a landscape-scale models that describe D. aterri- naturally fragmented and complex mosaic of mus occurrence in 40 streams in one northern stream habitats that have become fragmented Idaho river basin. To increase knowledge of D. by human-related disturbances. aterrimus natural history, we also compared support for local-scale models explaining vari- The size, quality, and configuration of patches ation in D. aterrimus density within streams. are landscape-scale variables known to affect Local-scale models include covariates that re- local population persistence and occurrence flect patch quality, whereas landscape-scale across taxa (Dunning et al., 1992; Fahrig and models include covariates that reflect predic- Merriam, 1994; Dunham and Rieman, 1999). In tions from metapopulation and landscape ecol- amphibians, population persistence is correlat- ogy theory about the importance of patch size, ed with patch size and connectivity and connectivity, and fragmentation. We had three negatively correlated with habitat fragmenta- objectives: (1) to identify an effective spatial tion and habitat alteration (reviewed in Cush- scale to manage D. aterrimus; (2) to identify D. man, 2006). Large patches often have greater aterrimus habitat associations at each spatial resistance and resilience to disturbance because scale; and (3) to explore important correlates of populations are larger (Marsh and Pearman, D. aterrimus density at the local scale. 1997), there is a broader diversity of habitat conditions and resources within patches (Schlosser, 1995), and the scale of a given MATERIALS AND METHODS disturbance relative to the size of the patch is Study Species and Site.—Dicamptodon aterrimus smaller (Stoddard and Hayes, 2005). Patch is a large salamander (up to 220 mm snout–vent connectivity decreases extinction risk by in- length) found in or near streams and rivers in creasing demographic and genetic input from the Rocky Mountains of northern and central immigrants, by increasing the chance of recol- Idaho and extreme western Montana (Stebbins, onization after extinction and by increasing 2003). This species exhibits facultative paedo- opportunities for resource supplementation morphosis, a polymorphism that results in the and complementation among patches (reviewed coexistence of gilled and fully aquatic paedo- in Dunning et al., 1992; Hastings and Harrison, morphic adults and terrestrial metamorphic 1994). Finally, habitat fragmentation and alter- adults in the same populations. No studies ation of the intervening habitats can effectively have addressed the natural history and habitat increase patch isolation by reducing rates of associations of D. aterrimus, but its similarity to movement among patches (reviewed in Saun- the Pacific Giant Salamander (Dicamptodon ders et al., 1991). Greater understanding of how tenebrosus) would suggest that D. aterrimus has patch size, patch connectivity, and fragmenta- narrow physiological tolerances and requires tion and alteration of intervening habitat affect specific local habitat conditions. Past studies SALAMANDER OCCURRENCE AND DENSITY IN HEADWATER STREAMS 471

FIG. 1. Map of Lochsa River basin, northwestern Idaho showing stream catchments that flow into the mainstem of the Lochsa River (bold). indicate that aquatic individuals of D. tenebrosus Fires and landslides are common disturbances occur in cold-shaded headwater streams with in this region. Fire regimes are mixed: infrequent, unembedded cobble substrate (Bury et al., 1991; patchy stand-replacement fires dominate in Roni, 2002) and that terrestrial adults inhabit upper-elevation forests and frequent, lower- moist coniferous forests (Nussbaum et al., 1983). severity, surface fires dominate at lower eleva- We conducted this study in 40 headwater tions (Brown et al., 1994). The combination of streams within the Lochsa River basin of the fire-affected soils, weathering granitic rocks, Clearwater River, Idaho County, Idaho (Fig. 1). steep slopes, and rain-on-snow events (defined The Lochsa is an eighth-order river (drainage as rain that falls on existing snow cover; area <3,000 km2) with an elevation range of MacDonald and Hoffman, 1995) has produced 425–2,630 m. Annual precipitation is approxi- landslides throughout the basin. As a result, mately 100 cm, and summers are hot and dry. total sediment volume in streams can exceed Basin geology is predominantly granitic Idaho water quality standards for salmonids, even in batholith. The majority of the land in the basin is the roadless Selway-Bitterroot Wilderness Area part of the Clearwater National Forest or owned (McClelland et al., 1997; Jones, 1999). by the Plum Creek Timber Company; portions Spatial Scale Definitions.—A goal of this of the headwaters are also in the Selway- research was to identify local and landscape- Bitterroot Wilderness Area. Six percent of the scale variables that predict the occurrence and Clearwater National forest and timber company density of D. aterrimus within patches of lands have been logged, but the wilderness area potentially suitable habitat. We assumed that has not been logged. Together, the Clearwater individual headwater stream drainages satisfied National Forest and Plum Creek Timber Com- the Hanksi and Gilpin (1997) definition of a pany lands have an average road density of 0.80 patch: a continuous area of space with all road km per km2 (Jones, 1999). necessary resources for the persistence of a Lower-elevation streams in the Lochsa River local population. We have found D. aterrimus in basin are dominated by Pacific maritime forests, larger streams, but preliminary surveys and which include western red cedar (Thuja plicata) discussions with freshwater managers in the and Douglas-fir (Pseudotsuga menziesii). Higher- region suggested that occurrence and local elevation streams are set in subalpine forests densities are higher in headwater streams (J. containing lodgepole pine (Pinus contorta) and Sauder, Idaho Fish and Game, pers. comm.; B. Engelmann spruce (Pinus engelmannii). The A. Maxell, Montana Natural Heritage Program, streams and rivers have multiple fish species, pers. comm.). Previous research on D. tenebrosus including bull trout (Salvelinus confluentus), supports this assumption: both occurrence and steelhead and rainbow trout (Oncorhynchus density of D. tenebrosus are higher in first- and mykiss), westslope cutthroat trout (Oncorhynchus second-order streams than in larger streams and clarkii lewisi), and sculpin (Cottus sp.). Amphib- rivers (e.g., Grialou et al., 2000; Johnston and ian species include D. aterrimus and Rocky Frid, 2002; Roni, 2002), and telemetry data Mountain Tailed Frogs (Ascaphus montanus). suggest that most individuals move less than 472 A. J. SEPULVEDA AND W. H. LOWE

FIG. 2. Schematic of multiscale sampling design used in the Lochsa River basin, Idaho. Shaded areas show a stream reach (A), a stream drainage (B), and a stream catchment (C). Landscape-scale variables were measured within drainages. Local-scale variables were measured within stream reaches.

30 m along streams over a season (Johnston and reach began 25 m upstream of the confluence Frid, 2002). The headwater streams we sampled with a similar or higher-order stream (Fig. 2A). were fed by drainages of 1–30 km2 (Fig. 2B), We used a backpack electrofisher (Smith and and multiple drainages were aggregated within Root, LR 24, pulsed DC with 400–520 volts) to catchments of 25–200 km2 (Fig. 2C). search for salamanders within the stream. Survey Methods.—Within each catchment, we Comparisons with light-touch surveys in five randomly selected 2–3 headwater drainages and of our sampled streams suggest that electro- sampled the main stream in each drainage. shocking is an effective tool for assessing D. Within each stream, we surveyed two 50-m aterrimus occurrence and density. We found reaches separated by 200 m for D. aterrimus more than 10 D. aterrimus per stream when occurrence and density (Fig. 2A). The lower using an electroshocker and found 0–2 D. SALAMANDER OCCURRENCE AND DENSITY IN HEADWATER STREAMS 473 aterrimus in these same plots when using light- We assessed local variables within each touch surveys. We did not survey for D. stream reach. We recorded mesohabitat as aterrimus terrestrial adults in the riparian area percent occurrence of pools, riffles, and cas- because they are extremely cryptic and believed cades over each 50-m reach (Hawkins et al., to be rare (J. Sauder, Idaho Fish and Game, pers. 1993). We measured substrate composition and comm.); therefore, our inferences pertain to the stream width at four random, 1-m wide occurrence and density of aquatic D. aterrimus. transects that extended between bank-full chan- To determine detection probability, all reach- nel edges within each reach. At six random es were surveyed three times within one season points along each transect, we recorded the (MacKenzie and Royle, 2005). Two of these proportion of substrate in four categories (Lane, surveys were conducted within a 6-h period, 1947): boulder-bedrock (.256.0 mm), cobble and the third survey was conducted 20–30 days (64.0–256.0 mm), gravel-pebble (2.0–64 mm), later. We recorded the number of salamanders and fines (,2.0 mm). We also recorded the captured during each survey and noted the proportion of substrate that was embedded, presence of stream fishes. All surveys were defined as having visible vertical surfaces done at base flow conditions in July and August buried in either silt or sand (Welsh et al., 1997; 2007. To test the independence of surveys Lowe and Bolger, 2002). Stream reach aspect (0– conducted within a 6-h period, we clipped tails 360u) was measured at the most downstream of all IGS captured in the first survey and noted transect of each reach. Abiotic variables were individuals recaptured in the second survey of averaged across the two study reaches within the 6-h period. We used the exact binomial test each headwater stream. Occurrence of salmonid of goodness of fit to evaluate independence fishes was recorded as presence or absence between captured and recaptured D. aterrimus within each headwater stream. To test the in the first and second surveys. assumption that D. aterrimus capture probability We used D. aterrimus occurrence (presence/ was independent of local habitat conditions, we absence) and density within a stream as marked all salamanders captured in the first of response variables. Dicamptodon aterrimus were our two surveys within a 6-h period, and then declared present in a headwater stream if they tested the correlation between proportion re- were detected in at least one 50-m reach in at captured in the second survey and local least one of the three surveys. Compared to variables. other studies that assessed salamander detec- To reduce the likelihood of missing important tion probability (e.g., Bailey et al., 2004), we had variables other than those we hypothesized very high detection probability: 39 of 40 study were important, we also collected data on the reaches had D. aterrimus always present or elevation, stream gradient, stream temperature, always absent in all three surveys. Consequent- and acidity (pH) of all sampled stream reaches. ly, we assumed a detection probability of 1.0 for Elevation was recorded at the most downstream all sites. We calculated density for each head- transect of each reach with a global positioning water stream as the mean number of salaman- system (eTrex, Garmin, Kansas City, KS). ders per m2 captured in the lower and upper Stream gradient was estimated as the difference stream reaches over the three surveys. in elevation between the upstream and down- Habitat Assessment, Local Scale.—We measured stream ends of each plot divided by the distance only local abiotic and biotic variables that have between these points. Stream temperature at the been shown to be important for stream sala- farthest upstream transect of each reach was manders, particularly Dicamptodon species. Abi- recorded hourly from June 20 though Septem- otic variables included mesohabitat type (e.g., ber 1 (Thermochron iButton, Dallas Semicon- pool, riffle, or cascade; Welsh and Lind, 2002), ductor, Dallas, TX). We included the overall substrate size and composition (Parker, 1991; mean summer temperature (T) and maximum Welsh and Ollivier, 1998), stream width (Stod- summer temperature (Tmax) in analyses. We dard and Hayes, 2005), canopy cover (Welsh recorded pH at the downstream end of each and Lind, 2002), and aspect (Stoddard and reach with a portable pH meter (Oakton, Inc., Hayes, 2005). Biotic variables included fish Vernon Hills, IL). Elevation, gradient, T, Tmax, presence (Sih et al., 1992). We recognize that and pH measurements were averaged for the other local variables can influence stream two study reaches within each headwater salamander occurrence and density; however, stream. predictive models with a large number of Habitat Assessment, Landscape Scale.—We de- variables are difficult for managers to embrace rived indices of patch size, patch connectivity, (Stoddard and Hayes, 2005) and have reduced habitat fragmentation, and habitat alteration statistical power in studies like ours, when within each drainage using the most recent sample size is logistically constrained (Burnham GIS coverages in the Clearwater National and Anderson, 2002). Forest GIS library (http://www.fs.fed.us/r1/ 474 A. J. SEPULVEDA AND W. H. LOWE clearwater/gis/library.htm). We defined patch 2005). We used the density of old-growth trees size using two covariates: total stream distance (trees per km2) derived from the 2004 Forest in the drainage and link magnitude (number of Inventory Vegetation Data. headwater streams in the drainage; Rich et al., Statistical Analysis, Approach.—We used mod- 2003). We defined patch connectivity as the el selection to identify the most plausible distance to the nearest stream in a neighboring statistical models for predicting D. aterrimus headwater drainage, but logistical constraints probability of occurrence and density when did not allow us to determine the occupancy present. Prior to this analysis, we identified status of these nearest neighbor streams. In our highly correlated pairs of variables (those with model, patch connectivity can potentially rep- r $ 0.7; Welsh and Lind, 2002) and used resent: (1) a metapopulation effect of coloniza- principal components analysis (PCA) to reduce tion/recolonization (Hanski and Gilpin, 1997); collinearity among these variables. We then (2) source-sink dynamics (Pulliam, 1988); (3) a used combinations of these variables to predict ‘‘Moran effect’’ that describes correlated envi- (1) D. aterrimus occurrence as a function of local- ronments; and (4) habitat complementation or or landscape-scale variables and (2) D. aterrimus neighborhood effects that describes resource density as a function of local-scale variables. use and availability (Dunning et al., 1992). The With the exception of a global model, we did occupancy status of the nearest neighbor stream not include models that mixed local- and is not critical to our test of landscape versus landscape-scale variables because our primary local-scale predictors of D. aterrimus occurrence objective was to determine which of the two because patch occupancy and patch suitability spatial scales better describes patterns of D. are expected to change over time in a dynamic aterrimus occurrence. Finally, we used Akaike’s landscape that has a high frequency of natural Information Criterion (AIC)–based methods to disturbance, such as the Lochsa River basin. select the best models of D. aterrimus occurrence Unoccupied patches with suitable habitat are and density from sets of candidate models generally predicted to be critical to species (Burnham and Anderson, 2002). We used log, persistence in metapopulation models (Hanksi square-root, and arcsine-square-root transfor- and Gilpin, 1997). mations on predictor variables where necessary. Because D. aterrimus can move within the Statistical Analysis, Local-Scale Models.—Four stream channel or overland, we measured categories of variables defined local-scale con- distance to the nearest stream along the channel ditions: mesohabitat type, substrate composi- network (i.e., minimum distance along the tion, aspect, and fish presence. Proportions of stream corridor from the mouth of the sampled riffles and cascades were positively correlated headwater drainage to the mouth of the nearest with the proportion of gravel-pebble, cobble, headwater drainage) and by the Euclidean and boulder-bedrock substrate. We used PCA to distance to this nearest stream (i.e., straight-line produce one axis that accounted for 58% of the topographic distance from any stream segment variation in these measures (coefficients of the within a sampled headwater drainage to any first eigenvector: riffles 520.53, cascades 5 stream segment within a neighboring headwa- 0.52, gravel-pebble 5 0.20, cobble 520.47, ter drainage; Dunham and Rieman, 1999; boulder-bedrock 5 0.43). Therefore, high values Gresswell and Torgersen, 2006). We character- reflect streams dominated by cascades, gravel- ized habitat fragmentation and alteration by pebble, and boulder-bedrock substrate. Propor- road density and forest structure. Roads can tions of embedded and fine substrate were lead to direct mortality of stream amphibians positively correlated; thus, we used PCA to (Trombulak and Frissell, 2000), act as barriers to produce one axis that accounted for 75% of this dispersal (deMaynadier and Hunter, 2000), and variation (coefficients of the first eigenvector: increase sedimentation within streams (Welsh embedded substrate 5 0.71, proportion of fines and Ollivier, 1998). We quantified road density substrate 5 0.71). These procedures yielded six as length of roads standardized by headwater local variables (Table 1): (1) proportion of drainage area (km of road per km2) using the stream with cascades and gravel-pebble/boul- 2001 Inventoried Roadless Area data layer for der-bedrock substrate; (2) proportion of pools the Clearwater National Forest. We described per stream; (3) proportion of fine-embedded forest structure by the density of old-growth substrate per stream; (4) stream width; (5) trees based on the northern Idaho old growth aspect; and (6) fish presence. These local-scale standards (Green et al., 1992), which takes into variables were not correlated with any of the account tree age and size. Previous studies have landscape-scale variables. found that occurrence of stream amphibians is We only considered local-scale models that related to conditions in surrounding forests, had four or fewer variables because complex including density, age, and size of forests models with many variables are of limited (Welsh and Lind, 2002; Stoddard and Hayes, practical value for management, and including SALAMANDER OCCURRENCE AND DENSITY IN HEADWATER STREAMS 475

TABLE 1. Abbreviations and definitions of local and landscape-scale variables used in competing models to predict Dicamptodon aterrimus occupancy and density in headwater streams within the Lochsa River basin, Idaho.

Variable Abbreviation Definition Local Pools P Average proportion of pools per stream Embedded substrate ES First axis from a PCA of average proportions of fine substrate and embedded substrate Stream type ST First axis from a PCA on average proportion of gravel-pebble, cobble, boulder-bedrock substrate and average proportion of cascades and riffles per stream Aspect A Downstream orientation of the stream, 0–360u Stream width SW Average width (m) of bank-full channel edges at high-flow conditions Fish presence FP Occupancy (presence/absence) of salmonid fish Landscape Patch connectivity PC First axis from a PCA on distance (km) along the stream corridor to the nearest drainage and Euclidean distances (km). Patch size PS First axis from a PCA on stream length (km) and link magnitude 2 Road density RD Length of roads (km) per drainage km 2 Old growth density OG Mean number of old growth trees per 0.004 km additional variables inflates the number of Statistical Analysis, Model Selection.—To pre- potential models beyond the number that can dict D. aterrimus occurrence, we compared the be reliably analyzed given our sample size relative likelihood of the global model (all local (Burnham and Anderson, 2002; Stoddard and and landscape-scale variables), the 56 local Hayes, 2005). By restricting models to those models, the 16 landscape models, one land- with no more than four variables, we were left scape-core model (all four landscape variables), with 56 possible local models. one local-core model (all six local variables), and Statistical Analysis, Landscape-Scale Models.— one local-abiotic model that included all five Four categories of variables defined landscape- abiotic variables. Logistic regression was used scale conditions: stream size (total stream length to determine the relative likelihood of each and link magnitude); stream isolation (mini- candidate model given the data. We evaluated mum distance along the stream channel and the strength of support for alternative models Euclidean distance to the stream); habitat using AICc, a bias-corrected version of AIC for fragmentation; and habitat alteration (road small sample size (Burnham and Anderson, density and old-growth tree density; Table 1). 2002). To ensure that AICc was the correct Total stream length and link magnitude were criterion to use, we tested for overdispersion of correlated; thus, they were subjected to PCA to the variance (cˆ) by using the observed chi- produce a single axis that accounted for 86% of squared goodness-of-fit statistic (x2) from the the variation (coefficients of the first eigenvec- global model divided by its degrees of freedom. tor: total stream length 5 0.71, link magnitude Values of cˆ .1 indicate overdispersion, but all 5 0.71). Therefore, high values reflect longer of our values were approximately 1; thus, we streams with many links. Minimum stream used AICc (Mackenzie et al., 2005). channel distance and Euclidean distance were To predict D. aterrimus density, we compared correlated, so they were subjected to PCA to the relative likelihood of the global model (all six produce a single axis that accounted for 98% of local variables), the 56 local models, and one local- the variation (coefficients of the first eigenvec- abiotic model that included all five abiotic tor: minimum stream channel distance 5 0.71, variables. We used multiple linear regression to Euclidean distance 5 0.71). Therefore, high determine the relative likelihood of each candi- values reflect increasing isolation (low connec- date model given the data. We evaluated the tivity). Road density and old-growth density strength of support for alternative models using were not correlated. We considered linear AICc and tested for overdispersion of the variance. models with all possible combinations of these We determined strength of support for the variables, which yielded 16 possible landscape model using DAICc values, AICc weights (v), models, including the landscape core model and evidence ratios (Burnham and Anderson, that included all landscape variables. 2002). Models with DAICc # 4 for small sample 476 A. J. SEPULVEDA AND W. H. LOWE

TABLE 2. Model selection results (AIC values corrected for small sample size) for the (A) occupancy and of Dicamptodon aterrimus in headwater streams in the Lochsa River basin, Idaho. K refers to the numberof parameters in each model, including the intercept. Boldface type indicates the best model and all those with an Akaike weight (w) . 0.1. The evidence ratio (wj:wi) indicates the multiplicative probability by which the best model (wj) is more likely than competing models (wi), given the set of candidate models and the data. Covariate abbreviations are listed in Table 1.

Model name Log-likelihood K D AICc w Evidence ratio Occupancy Landscape: PC, RD, OG 219.65 4 0.00 0.23 1.00 Landscape: RD 223.44 2 2.04 0.08 2.88 Landscape: PC, RD 222.07 3 2.04 0.08 2.88 Landscape: PC, PS, RD, OG 219.57 5 2.48 0.07 3.29 Landscape: RD, OG 222.52 3 2.83 0.06 3.83 Local: A 224.23 2 3.33 0.04 5.75 Landscape: PC 224.55 2 3.84 0.03 7.67 Global: all local and landscape 217.86 11 18.88 0.00 – Landscape core: PC, PS, RD, OG 219.57 5 2.48 0.07 3.29 Local core: P, ES, ST, A, SW, FP 224.09 7 16.35 0.00 – size (n/K , 40; where n 5 sample size and K 5 tremely high. Capture history of 17 of the 18 number of parameters) have empirical support streams with D. aterrimus present was 1-1-1, as being plausible (Burnham and Anderson, whereas one stream had a capture history of 0- 2002). We only present models with DAICc # 4 1-1. Capture history of the 22 streams with D. because n/K , 40 for all models. To assess the aterrimus absence was always 0-0-0. Surveys importance of individual parameters within the that occurred twice within a 6-h period were presented models, we calculated importance independent of one another. The probability of weights by summing v values of all models in detecting D. aterrimus in the second survey was which the parameter occurs (Burnham and not influenced by the first survey (exact Anderson, 2002). Parameters with importance binomial test of independence: P , 0.0001). weights . 0.20 are considered to be significant We captured 201 individuals in the first survey (Stoddard and Hayes, 2005). Finally, coefficients and 147 individuals in the second survey, 40 of (b) of local and landscape habitat covariates for which were recaptures from the first survey. D. aterrimus occurrence and relative density The probability of recapture was not correlated where obtained by averaging across all models with any local-scale variables ( r2 , 0.10, P . weighted by v (i.e., model averaging; Burnham 0.05). and Anderson, 2002). Odds ratios were calculated Support for landscape-scale influences on D. from D. aterrimus occurrence coefficient estimates aterrimus occurrence was greater than support as exp(b). An odds ratio of 1.0 indicates no for local-scale influences (Table 2). The most difference between the proportion of sample plausible model for D. aterrimus occurrence points with or without salamanders, whereas included roads, patch isolation, and old-growth odds ratios close to zero or substantially greater tree density; its evidence ratio was six times than 1.0 indicate a large difference. Odds ratios greater than the most supported local model. less than 1.0 indicate a negative effect, whereas Probability of D. aterrimus occurrence was ratios greater than 1.0 indicate a positive effect highest in headwater stream drainages with (Keating and Cherry, 2004). low road and old-growth tree density and low Additional Variables.—To ensure that stream patch isolation (Fig. 3). Road density and patch occurrence patterns were not influenced by isolation had the highest importance weights physiochemical variables, we used t-tests to and had odds ratios ,1.0, which indicate that compare the average elevation, gradient, T, T , max increases in these variables had a negative and acidity between streams with and without D. effect on probability of D. aterrimus occurrence aterrimus. All statistical analyses were performed (Table 3). The odds ratio for old-growth tree in JMP 7.0 (SAS Institute, Cary, NC, 2007). density was extremely low because of the large standard error. The only local-scale model with RESULTS DAICc , 4 included aspect. Dicamptodon aterri- We found D. aterrimus presence in 18 of 40 mus occurrence was associated with a western sampled headwater streams, with densities (200–300u) patch aspect. There was little evi- ranging from 0.01–0.24 individuals per m2. dence for other local-scale influences on D. Detection probability of D. aterrimus was ex- aterrimus occurrence. Results were the same SALAMANDER OCCURRENCE AND DENSITY IN HEADWATER STREAMS 477

FIG. 3. Relationships between Dicamptodon aterrimus occurrence (solid diamonds 5 present, open diamonds 5 absence) and road density, patch isolation (top), and old growth tree density (bottom). Principal components analysis (PCA) was used to reduce multicollinearity among variables describing patch isolation. High values reflect increasing isolation. An X 5 1 transformation was necessary to display road density values of zero. when we used program PRESENCE (http:// density (Table 5). Surprisingly, D. aterrimus www.mbr-pwrc.usgs.gov/software/doc/presence/ density was greatest in patches with a high presence.zhtml), which incorporates variation proportion of embedded substrate. Less plausi- in detection probability, to assess occurrence ble models suggest that D. aterrimus relative models. density was greatest in western aspects (200– The most plausible model of D. aterrimus 300u) and fishless patches. density included only embedded substrate Physiochemical characteristics did not ex- (Fig. 4), and all models with a DAICc ,4 plain D. aterrimus occurrence patterns. There included this variable (Table 4). Parameter was no difference between streams with and importance weights indicated that embedded without D. aterrimus in elevation (t38 5 1.07, P 5 substrate was the best predictor of D. aterrimus 0.29), gradient (t38 520.47, P 5 0.28), average 478 A. J. SEPULVEDA AND W. H. LOWE

2 FIG. 4. Relationship between Dicamptodon aterrimus density (individual/m ) and a principle component axis representing the proportion of embedded and fine substrates. High values reflect a high proportion of embedded and fine substrate. summer temperature (t38 520.11, P 5 0.92), DISCUSSION maximum summer temperature (t38 520.12, P 5 0.91), and pH (t38 5 0.77, P 5 0.94). Across To identify an effective scale of management all streams, elevation ranged from 530–1,600 m, for D. aterrimus in the Lochsa River basin, we gradient ranged from 0.25–9.50%, average sum- compared model support for local and land- mer temperature ranged from 8.3–15.1uC, max- scape-scale predictors of D. aterrimus occurrence imum summer temperature ranged from 14.0– in 40 headwater streams. Our results suggest 20.0uC, and pH ranged from 5.5–8.8. that management and conservation efforts for

TABLE 3. Importance weights, coefficients (range), 95% confidence intervals and odds ratios of local and landscape habitat covariates predicting Dicamptodon aterrimus occupancy obtained by averaging across all models weighted by the Akaike weights (Burnham and Anderson, 2002). The intercept is not shown. Boldface indicates estimates for which the 95% confidence intervals did not overlap zero.

Scale Importance weight Coefficient estimate 95% C.I. Odds ratio (A) Occupancy Local P 0.07 20.03 0.06 0.93 (20.04 to 20.03) ES 0.08 20.08 0.05 0.92 (20.20 to 0.00) ST 0.07 20.04 0.12 1.04 (20.07 to 0.24) A 0.16 0.03 0.07 1.03 (0.02 to 0.11) SW 0.07 20.09 0.54 0.91 (20.80 to 20.02) FP 0.07 20.01 0.18 1.01 (20.02 to 0.00) Landscape PI 0.46 20.55 0.26 0.58 (20.41 to 20.07) PS 0.17 20.04 0.04 0.96 (20.09 to 20.02) RD 0.60 20.40 0.14 0.67 (20.52 to 20.34) OG 0.42 270.54 26.30 2.32E-31 (238.47 to 281.12) SALAMANDER OCCURRENCE AND DENSITY IN HEADWATER STREAMS 479 this species would be more effective at the of road density may affect the movement of landscape scale than at the local scale. The five individuals between patches to make use of most supported models of D. aterrimus occur- nonsubstitutable and substitutable resources rence were composed of only landscape-scale (Dunning et al., 2002), rather than affecting predictors, and the importance weights of all dispersal, colonization, recolonization, and res- landscape-scale predictors were greater than cue dynamics. Similarly, patch connectivity local-scale predictors. Specifically, we found may be correlated with environmental and that D. aterrimus occurrence was highest in geographic attributes such that a site near an unfragmented headwater drainages with few occupied site may tend to have similar charac- roads, lowest in spatially isolated streams, and teristics to those of an occupied site (Bradford et insensitive to patch size and local-scale vari- al., 2003). Discrimination between metapopula- ables that influence stream habitat quality. Our tion dynamics and landscape physiognomy and research suggests that patch spatial structure in composition is necessary to understand mech- the surrounding landscape influences D. aterri- anistically the role of patch spatial structure on mus occurrence and that developing effective D. aterrimus occurrence. forest management rules that minimize road The importance of road density and spatial establishment and habitat fragmentation are connectivity in predicting D. aterrimus occur- essential for protecting populations of this rence could be related to the ability of D. stream amphibian. aterrimus individuals to rescue and recolonize In a similar analysis of pond amphibian declining or locally extinct populations. Roads occurrence and density, Van Buskirk (2005) also may impede salamander movement between found greater model support for landscape- streams by altering the intervening terrestrial scale covariates than for local-scale covariates. habitat, or they may impede movement along He suggested that his results could be inter- streams with the presence of road crossings and preted in two ways: (1) rejection of the focus on culverts. Previous studies have shown that local processes because results require explana- roads limit overland movement because of tion at the level of the metapopulation; and (2) salamander physiological restrictions and pred- rejection of the focus on amphibian metapopu- ator-avoidance behavior (Marsh et al., 2005; lation dynamics because landscape-scale pro- Semlitsch et al., 2007). Road crossings of streams cesses reflect the availability and condition of and the associated culverts may also limit local habitat. Although we did not rigorously movements of organisms within the stream test these alternative hypotheses, we believe channel because of excessive water velocity that our results provide greater support for and insufficient water depth (Warren and rejection of the focus on local processes. First, Pardew, 1998). Within federal lands in the the probability of D. aterrimus occurrence was northern Rocky Mountains, there are 1.3 cul- best predicted by landscape-scale models that verts per road kilometer (FEMAT, 1993). Rela- reflect predictions from metapopulation theory tive to fish, stream salamanders are poor about the importance of patch fragmentation swimmers, suggesting that culverts may signif- and connectivity. Second, low support for local- icantly limit D. aterrimus movement along scale models and low importance weights for streams significantly (Sagar, 2004). Also, cul- local-scale covariates suggest that D. aterrimus is verts may create predation hotspots that affect tolerant of a wide range of local conditions in a salamanders directly through increased mortal- stream. Finally, the lack of correlation between ity from large fish that accumulate in culvert landscape and local-scale covariates suggests outlets or indirectly through predator-avoid- that landscape-scale processes were not closely ance behavior. However, these mechanisms linked to our metrics of local habitat condition. remain speculative because we do not know For example, we found no correlation between the spatial extent, frequency, and pathways of road density at the landscape scale and fine- movement in D. aterrimus. Future research is embedded substrate at the local scale, even needed that addresses specifically the influence though fine-embedded substrate was the best of culverts and roads on stream salamander predictor of D. aterrimus density. However, it is movement. possible that landscape-scale covariates were The high importance weights of road density correlated with local habitat variables that we and stream connectivity in our occurrence did not measure (e.g., stream invertebrate models may relate to a reduced ability of D. production may be related to old-growth tree aterrimus to recolonize suitable streams after density, Hawkins et al., 1982). natural disturbances in human-altered stream Our results provide greater support for the drainages. Natural disturbances, such as floods rejection of the focus on local processes, but we and debris flows, have been linked to impaired cannot readily assume that D. aterrimus has habitat quality and lower densities of D. a metapopulation structure. The significance tenebrosus and stream fishes (e.g., Cottus sp.) in 480 A. J. SEPULVEDA AND W. H. LOWE

Oregon streams (Harvey, 1987; Swanson et al., why road density is not correlated with forest 1998). Furthermore, land use activities in the stand structure. surrounding drainage, such as road construc- We found that the D. aterrimus density was tion and timber harvest, are believed to exacer- greatest in streams with a high proportion of bate the impact of natural disturbance on the fine-embedded substrate. For most stream recolonization of biotic communities (Gregory amphibians, fine-embedded substrate is nega- et al., 1991; Swanson et al., 1998). The Lochsa tively correlated with occurrence and abun- River basin has a high frequency of natural dance because interstitial spaces between sub- disturbance that includes fire, rain on snow strate provide egg-laying sites (Bruce, 1978), floods, and landslides (Jones, 1999). These important refuge from natural disturbances disturbances are often confined to local stream (e.g., high-flow events) and predation (Sih et reaches and are spatially heterogeneous within al., 1992), and are linked to increases in stream and across stream networks. For example, 49% invertebrate richness and abundance (Flecker of the Lochsa River basin has burned since 1910, and Allan, 1984). A simple hypothesis to explain and there is an average of 57 fire starts per year the observed relationship between D. aterrimus (Brown et al., 1994), but most fire starts burn 2 density and fine-embedded substrate is that less than 1 km . The combination of fire- sampling efficiency of D. aterrimus is greater damaged soils and rain-on-snow floods results when substrates are more embedded. We can in frequent landslide mass wasting events and reject this hypothesis because probability of debris flows that affect stream channels. In recapture was not correlated with embedded 1996–97, there were 907 landslides in this substrate or any other local-scale variables. region; however, streams adjacent to roads Rather, the positive association with fine-em- and timber harvest may be more vulnerable to bedded substrate may reflect adaptation to % landslides, as 60 of the 1996–97 landslides natural disturbances such as fires and land- were related to cut and fill slopes along roads slides that add sediment to streams (Lytle and % and 12 were associated with timber harvest Poff, 2004). Streams in the Selway-Bitterroot (Jones, 1999). All streams in our study area seem Wilderness Area, where only natural distur- to be vulnerable to natural disturbance, but bances occur, have greater stream sediment streams in drainages with roads may have an volumes than streams in the adjacent national elevated extinction risk. forest land, where roads and timber harvest also The negative relationship we observed be- occur (Jones, 1999). Having evolved with high tween D. aterrimus occurrence and old-growth stream sediment loads in the Lochsa River tree density may reflect a habitat condition that basin, D. aterrimus may be able to burrow we did not measure, such as standing stock of through the fine sediment to seek refugia, or primary production and invertebrate biomass. Previous studies on stream salamanders in the they may use microhabitat sites that our surveys Pacific Northwest have shown increased occur- did not record, such as undercut banks. It is rence and abundance of Dicamptodon species in interesting to note that this species may not streams running through clearcut and second- need refugia from fish predation: models of D. growth timber stands relative to streams in old- aterrimus occurrence and density had weak growth stands (Hawkins et al., 1983; Richardson support for the covariate of fish presence and Neill, 1998; Steele et al., 2003). These (Tables 2–5). increases are thought to be linked to increases Conclusion.—We found that landscape-scale in primary production and invertebrate biomass models were the best predictors of D. aterrimus caused by greater light penetration and warmer occurrence in the Lochsa River basin. Specifi- temperatures of streams with removed riparian cally, we found that probability of D. aterrimus canopy cover (e.g., Hawkins et al., 1982). We are occurrence was greatest in roadless drainages reluctant to make inferences about the influence and lowest in isolated stream drainages. In of timber harvest on D. aterrimus and stream addition, we found that the relative density of community dynamics because the covariate of D. aterrimus was greatest in streams with a high old-growth density had a lower importance proportion of embedded substrate and fine weight than road density and stream isolation. sediment. These results suggest that D. aterrimus In addition, we believe that our metric of old- patches are spatially structured across stream growth forest (northern Idaho old growth networks and that D. aterrimus is tolerant of a standards) failed to discern effects of timber wide range of local conditions within streams. harvest from effects of fire on stand structure. In Further research is needed on D. aterrimus our study area, fires have consumed old-growth natural history for a mechanistic understanding trees in areas with and without roads; thus, of local habitat associations. We suggest that most stands are mixed aged, similar to harvest- management efforts focus on protecting roadless ed areas. The occurrence of fire may explain areas and restoring stream and overland connec- SALAMANDER OCCURRENCE AND DENSITY IN HEADWATER STREAMS 481

TABLE 4. Model selection results (AIC values corrected for small sample size) for the relative density of Dicamptodon aterrimus in headwater streams in the Lochsa River basin, Idaho, USA. K refers to the number of parameters in each model, including the intercept. Boldface type indicates the best model and all those with an Akaike weight (w) . 0.1. The evidence ratio (wj:wi) indicates the multiplicative probability by which the best model (wj) is more likely than competing models (wi), given the set of candidate models and the data. Covariate abbreviations are listed in Table 1.

Model name Log-likelihood K D AICc w Evidence ratio Density ES 251.31 2 0.00 0.21 1 ES, FP 251.97 3 1.67 0.09 2.33 ES, SW 251.75 3 2.10 0.07 3.00 ES, A 251.66 3 2.28 0.07 3.00 ES, ST 251.66 3 2.29 0.07 3.00 ES, P 251.53 3 2.54 0.06 3.50 ES, P, FP 252.62 4 3.86 0.03 7.00

Global: P, ES, ST, A, SW, FP 253.15 7 17.90 0.00 — tivity in human-impacted areas, rather than on and abundance as ecosystem indicators of only improving habitat quality within a stream. human land-use. We caution that our manage- Our analysis of models relating D. aterrimus ment recommendations are based on limited occurrence and density to local and landscape information about D. aterrimus occurrence and factors is a useful starting point for understand- density patterns. Our understanding of the ing the spatial ecology and habitat associations conservation biology of this and other stream of this stream salamander species. Our results amphibians would be strengthened with future support findings of other studies of amphibians research using direct (i.e., mark-capture-recap- and stream fishes in the Pacific Northwest and ture and radio-telemetry) and indirect (i.e., Northern Rocky Mountains that show the genetics) methods to determine the spatial importance of roadless areas and stream con- structure of populations, the frequency of nectivity to population persistence (e.g., Dun- dispersal between populations, and dispersal ham and Rieman, 1999; Rieman and Dunham, pathways. 2000; Welsh and Ollivier, 1998). We also found that D. aterrimus had broad habitat require- Acknowledgments.—This research was funded ments and was positively associated with by grants from the Society for Northwestern embedded substrate. These results conflict with Vertebrate Biology, Charlotte Martin Founda- data on other stream amphibian species about tion, the Pacific Rivers Council, and the Uni- local habitat associations and question the versity of Montana. This study and manuscript broad application of amphibian occurrence benefited from the comments of an anonymous

TABLE 5. Importance weights, coefficients (range), and 95% confidence intervals of local habitat covariates predicting Dicamptodon aterrimus density, obtained by averaging across all models weighted by the Akaike weights (Burnham and Anderson, 2002). The intercept is not shown. Boldface indicates estimates for which the 95% confidence intervals did not overlap zero.

Scale Importance weight Coefficient estimate 95% C.I.

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