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Snow Tracking and Trapping Harvest as Reliable Sources for Inferring Abundance: A 9-year Comparison
Toshinori Kawaguchi, André Desrochers, and Héloïse Bastien

Northeastern Naturalist, Volume 22, Issue 4 (2015): 798–811

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Northeastern Naturalist 798 T. Kawaguchi, A. Desrochers, and H. Bastien 22001155 NORTHEASTERN NATURALIST 2V2(o4l). :2729,8 N–8o1. 14 Snow Tracking and Trapping Harvest as Reliable Sources for Inferring Abundance: A 9-year Comparison Toshinori Kawaguchi1,*, André Desrochers1, and Héloïse Bastien2 Abstract - Trapping harvest and snow tracking are frequently used to infer population dynamics, yet there have been few evaluations of these indices. We developed population indices for Martes americana (American Marten), Mustela spp. (weasels), and Tamiasciurus hudsonicus (American Red Squirrel) from 9 years of snow-tracking data in eastern Canada. We employed mean track counts per unit effort as population indices derived from a generalized linear model (GLM) of track counts as a function of year and covariates including forest age. Mean track counts were significantly correlated with American Marten and weasel pelt sales and year effects in GLM were correlated with American Red Squirrel and weasel pelt sales. The results of both methods are in agreement; therefore they are likely valid sources to infer population dynamics for these species. Introduction Monitoring animal populations is key to understanding their ecosystems, functions, and responses to anthropogenic and natural disturbances (Lindenmayer et al. 2012). Making direct estimates of population densities by live-trapping or mark–recapture methods is very labor intensive and expensive (Gese 2001). Thus, ecologists often resort to population indices, such as those derived from snow tracking (Pellikka et al. 2005) or trapping harvest (Roberts and Crimmins 2010) data, observation reports by hunters (Simard et al. 2012), and scat surveys (Krebs et al. 2001, Mowat and Slough 2003). Snow tracking is non-invasive (Halfpenny et al. 1995) and is frequently used to estimate relative abundance of mammals wintering in North America (Mowat and Slough 2003) and Europe (Pellikka et al. 2005). Some studies assumed that the number of tracks is proportional to population size and used tracking counts per unit effort to infer relative abundances of mammals such as Lepus americanus Erxleben (Snowshoe Hare; Jensen et al. 2012), Tamiasciurus hudsonicus Erxleben (American Red Squirrel; Jensen et al. 2012), Martes americana Turton (American Marten; Krebs 2011), and Mustela frenata Lichtenstein (Long-tailed Weasel; Fitzgerald 1977). Snow tracking is relatively easy to conduct (Halfpenny et al. 1995) and has higher detection rates than other non-invasive techniques such as camera traps and track plates (Gompper et al. 2006). Also, snow tracking does not 1Centre d’Étude de la Forêt and Département des Sciences du Bois et de la Forêt, Université Laval, 2405, Rue de la Terrasse, Québec, PQ, G1V 0A6, Canada. 2Direction de la Gestion de la Faune de la Capitale-Nationale et de la Chaudière-Appalaches, Ministère des Forêts, de la Faune et des Parcs, 1300, Rue du Blizzard, local 100, Québec, PQ G2K 0G9, Canada. *Corresponding author - toshinori.kawaguchi.1@ulaval.ca. Manuscript Editor: Tom French Northeastern Naturalist Vol. 22, No. 4 T. Kawaguchi, A. Desrochers, and H. Bastien 2015 799 require the baits or attractants that are employed in other population-monitoring techniques (Raphael 1994). However, there may be significant noise and bias in population indices obtained from snow-tracking data. Track counts can be affected by weather conditions such as strong winds and recent snow falls (Gompper et al. 2006, Raphael 1994), and can vary with the activity level of animals. For example, activity of American Marten (Thompson and Colgan 1994) and Mustela erminea L. (Short-tailed Weasel; Robitaille and Baron 1987) decreases at extremely low temperatures . Presence of prey can attract the predator, resulting in an increased track count for the predator as shown for Lynx canadensis Kerr (Canada Lynx; Keim et al. 2011). Observer bias and misidentification can also affect results. In many jurisdictions, trapping harvest data in the form of total catch and catch per unit effort is commonly used to infer relative population sizes and trends of fur-bearing species (Douglas and Strickland 1987), particularly American Marten and Pekania pennanti (formaly Martes pennanti) Erxleben (Fisher; Jensen et al. 2012). In the US, harvest surveys are frequently used to monitor Lynx rufus Schreber (Bobcat) population status (Roberts and Crimmins 2010). In Quebec, Canada, the number of pelts that are sold is the primary index used to track population size for weasels (composed mostly of Short-tailed and Long-tailed Weasel), Mephitis mephitis Schreber (Striped Skunk), and Ondatra zibethicus L. (Muskrat). For Canada Lynx, American Marten, and Fisher, abundances can be inferred from the logbooks that trappers are required to maintain. Those logbooks record the numbers of trapped animals for each species, together with trapping effort. Numbers of pelts sold can be obtained from fur-transaction reports. Trapping harvest is mainly affected by trapping effort (Fortin and Cantin 2004), which in turn is influenced by several ecological and socioeconomic factors, such as food abundance (Ryan et al. 2004), market prices (Weinstein 1977), government quotas (Smith et al. 1984), landscape changes (Raphael 1994), other disturbances over time (Raphael 1994), and temperature (Kapfer and Potts 2012). In addition to problems that are specific to each of these indirect methods, temporal changes in sampling locations may introduce additional bias to the index. Moreover, even if field surveys are conducted in fixed locations each year, successional patterns in vegetation will affect the response of different species to the sampled plots (Anderson 2001). As is the case with any model, methods that are used to develop population indices should optimize the combination of simplicity and accuracy. Few studies have evaluated the reliability of population indices that have been obtained through those indirect methods. In a 5-year study in Ontario, Thompson and Colgan (1987) reported different population trends from trapping harvest and snow tracking. Thompson et al. (1989) found no correlation between track counts and trapping harvest, but they did find a significant correlation between track counts and live-trapping data. The authors of those studies suggested that track counts correctly described population changes over 5 years. Northeastern Naturalist 800 T. Kawaguchi, A. Desrochers, and H. Bastien 2015 Vol. 22, No. 4 To evaluate population indices that were based on snow tracking and pelt sales, we examined the correlation between the 2 techniques with data from 3 mammal species: American Marten, weasels, and American Red Squirrel. Although the 2 indices did not directly measure population size, we hypothesized that if, after accounting for sources of error described above, 2 indices describe population changes accurately over time, then they would be highly correlated. We predicted that if one or both of the indices failed to describe annual population dynamics, the correlation coefficient between the 2 indices should not be different from zero for the species concerned. Field-Site Description We conducted snow-tracking surveys in the Montmorency Forest, a 66-km2 area, ~80 km north of Quebec City (47°20'N, 71°10'W), Canada (Fig. 1). Trapping and hunting for the 3 species were not allowed in this area. Most of the study area was originally clear cut between 1941 and 1945 and is now managed with a combination Figure 1. Spatial location of snowtracking site (the M o n t m o r e n c y Forest; black shading) and trapping area of Furbearer Management Unit (UGAF) 39 (gray shading), southern Quebec, Canada, 2004–2012. Northeastern Naturalist Vol. 22, No. 4 T. Kawaguchi, A. Desrochers, and H. Bastien 2015 801 of clear cuts and selective cuts. The resulting forest is composed of mature (more than 40-y-old), mid-succession (21- to 40-y-old), and regenerating (less than 20-yold) forest stands, which comprised 55%, 25%, and 20% of the study area at the time of the study, respectively. Locations of different-aged stands shift with time due to continuing timber harvest and forest stand succession, but mean stand age remained stable throughout the study period (42.97 ± 1.67 years, range 0–113 y). A dense road network is present, with about 150 km of dirt roads. During winter, several roads were machine-groomed as cross-country ski trails. Elevation ranges from 650 m to 1000 m. From 1999 to 2011, annual mean temperature was 0.3 °C. Annual precipitation was 1417 mm (33% as snow). Annual maximum snow depth at the weather station in the Montmorency Forest ranged from 62 cm to 146 cm during the period from 1999 to 2011 (Vigeant-Langlois and Desrochers 2011). Abies balsamea (L.) Miller (Balsam Fir) dominated second-growth mature forest stands. Picea mariana (Miller) Britton, Sterns, and Poggenburg (Black Spruce), Betula papyrifera Marshall (White Birch), Populus tremuloides Michaux (Trembling Aspen), and P. glauca (Moench) Voss (White Spruce) were also common. Recent (<5-y-old) clear cuts were generally colonized by Rubus idaeus L. (Red Raspberry), Balsam Fir, and White Birch (de Bellefeuille et al. 2001). We obtained pelt-sales data from fur-transaction reports of Furbearer Management Unit (French acronym, UGAF) 39, a 7934-km2 area (Fig. 1). In this area, Balsam Fir and Black Spruce were the dominant plant species. White Birch, Trembling Aspen, Betula alleghaniensis Britt. (Yellow Birch), and Acer spp. (maples) were also common (Dussault et al. 2006). Similar to the Montmorency Forest, timber harvest was conducted in UGAF 39, resulting in a heterogeneous stand mosaic (Dussault et al. 2006). From 2004 to 2012, mean daily winter temperature ranged from -16 oC to -9 oC and total annual winter precipitation ranged from 165 mm to 265 mm (Environment Canada 2014; Fig. 2). In summary, the main difference between the snow-tracking and trapping areas we studied was the presence of trapping in the latter. Thus, furbearers were possibly more abundant in the snow-tracking area than in the trapping area, potentially resulting in different movement patterns due to higher density. However, we assumed that those 2 locations exhibited similar population trends because they overlapped geographically and had similar forest composition and forest management. In addition, wildlife populations within 100 km have been shown to vary synchronously in various taxa, including mammals (Liebhold et al. 2004). Therefore, we expected that few individuals changed their locations (Montmorency or UGAF 39) depending on study year. Methods Snow tracking We conducted snow tracking each winter (20 December–31 March) from 2004 to 2012. We counted tracks along a portion of a network of ~150 km of roads, 40 km of trails, and 60 km of off-trail straight-line transects (Fig. 3). None of the roads that we surveyed had been snowplowed. Transect length depended upon snow Northeastern Naturalist 802 T. Kawaguchi, A. Desrochers, and H. Bastien 2015 Vol. 22, No. 4 condition, current weather, time of day, and personnel availability (Table 1). Offtrail transects were randomly selected from a systematic grid covering the entire study area at the beginning of each year. However, in the selection process, we removed transects that had been surveyed in the previous 2 years. For each year, we surveyed selected transects only once to cover as large an area as possible. As a result, we surveyed a mean ± SD of 91.3 ± 28.9 km of transects each year (Table 1). We conducted our transect surveys within 24–72 hours after any snowfall >3 cm. We recorded the locations of all tracks found within 2 m of either side of the transect lines using a GPS receiver and identified the species based on track pattern and size. We ignored conspecific tracks that were within 3 m of a recorded track. We utilized temperature and snow depth as measured at an Environment Canada weather station at the Montmorency Forest. Figure 2. Inter-annual dynamics of winter precipitation and winter temperature in the study sites: the Montmorency Forest and the Laurentides Wildlife Reserves (UGAF 39), southern Quebec, Canada, 2004–2012. (a) Winter temperature (oC), (b) winter precipitation (mm). The data for 2006 was not available. Northeastern Naturalist Vol. 22, No. 4 T. Kawaguchi, A. Desrochers, and H. Bastien 2015 803 We georeferenced track and transect data with ArcGIS (Version 10.1, ESRI, Redlands, CA) and then split transects into 200-m fixed-length segments, resulting in 4123 segments for the entire study (Table 1). We selected this fixed length as a compromise that limited the number of zeros in the data while retaining a sufficiently large number of sampling units. We counted tracks on each transect segment. Within a 100-m buffer around each segment, we determined the mean forest-stand age (weighted by area), slope (the difference between minimum and maximum elevation), variance of age, and mean elevation. We calculated mean forest-stand age with age in years and area (w) in hectares for each forest stand in a buffer: Mean age = ik = (w1)(age1) + (w2)(age2) + (w3)(age3) + … + (wi)(agei) , (w1) + (w2) + (w3) + … + (wi) where i = the number of forest stands in a buffer. Buffers occasionally included roads, rivers, and lakes; thus, we also calculated the percentage of vegetated area inside each buffer. Figure 3. Spatial distribution of sampling transects in the Montmorency Forest, southern Quebec, Canada, 2004– 2012. Black lines indicate off-trail transects and gray lines indicate either roads or trails. Northeastern Naturalist 804 T. Kawaguchi, A. Desrochers, and H. Bastien 2015 Vol. 22, No. 4 Population indices from snow-tracking data We assumed that track count is a function of population size, animal-activity level, and exposure time of transect since the last disturbance. We developed 2 population indices. The first was mean track count per 200-m-transect segment per hours of exposure since the last snowfall. This index has the advantage of simplicity, but ratios raise statistical issues including spurious correlation (Atchley et al. 1976). Thus, we used a second index based on a generalized linear model (GLM) with a negative binomial distribution and log link by using package MASS in the software R (Venables and Ripley 2002). We used negative binomial distribution instead of Poisson due to the large number of zero counts. In the GLMs, track count was a function of year as a categorical variable, and combinations of the following covariates: mean stand age, variance of stand age, mean temperature since last disturbance, exposure time since last disturbance (snow or wind), slope, mean elevation, proportion of vegetated area, and transect type (road or off-trail). We included transect type in the model to account for differences in length of off-trail transect surveyed among study years. Year effect represents a population index (or anomaly) by comparing mean counts in a given year with those in the reference year (2004). Thus, year-effect estimates reflected differences in mean track counts between 2004 and other years, after accounting for the effect of covariates on track counts described above. We were aware that GLMs do not account for spatial autocorrelation, which likely occurred in track counts. However, average Moran’s I values for American Marten, American Red Squirrel, and weasels over all study years were 0.044 (range = 0.015–0.080), 0.062 (range = 0.007–0.119) and 0.024 (range = 0.006–0.040), respectively. These values suggest weak spatial autocorrelation in track counts after accounting for covariates. More importantly, we considered years, not transects, as sampling units for statistical inference on the comparison of time series. Thus we do not consider spatial autocorrelation an issue. Table 1. Sampling effort for snow tracking and track counts for American Marten, American Red Squirrel, and weasels at the Montmorency Forest, southern Quebec, Canada, 2004–2012. Sampling 200-m American American On road Off-trail events segments Marten Red Squirrel Weasels Year /trails (km) (km) (days) (n) (count) (count) (count) 2004 33 17 14 252 70 133 36 2005 60 5 14 325 66 187 11 2006 51 23 12 370 88 63 58 2007 71 16 14 435 140 2061 130 2008 97 23 12 602 329 441 575 2009 58 12 11 346 98 78 76 2010 91 12 8 518 106 251 35 2011 110 13 8 619 163 175 89 2012 91 39 19 656 194 1497 71 Total 662 160 112 4123 1254 4886 1081 Mean 74 18 12.4 458 139 543 120 Standard deviation 25 10 3.4 146 83 723 174 Northeastern Naturalist Vol. 22, No. 4 T. Kawaguchi, A. Desrochers, and H. Bastien 2015 805 Pelt sales We used pelt-sales data as a proxy for trapping harvest, based on fur-transaction reports, which were obtained from Quebec’s Ministère des Forêts, de la Faune et des Parcs. Pelt-sales data were composed mainly of trapping harvest totals and number of animals hunted. There were 112 trappers in the area who used bodygripping traps (Model 120, 160, or 220). Trapping season extended from 18 October to 1 March of the following year, and there was no harvest limit for the 3 species that we studied. However, the ministry asked trappers to stop trapping American Marten when they began to catch more females than males late in the trapping season. We obtained data of trapping effort (number of traps x number of nights) for American Marten from trappers’ mandatory reports and daily logbooks from the government. Data for American Red Squirrel and weasels were not available. Average trapping effort for American Marten from 2004 to 2012 was 70,187 (range = 52,046–87,756) trap-nights. Average number of trapping logbooks submitted was 72 (range = 58–83). Population indices from pelt-sales data Various confounding factors such as temperature and trapping effort can influence both pelt sales and track counts. We calculated Pearson product-moment correlations (r) between raw pelt sales and 4 factors: trapping effort, daily mean temperature (January–March) (oC), winter total precipitation (mm), and pelt price in the previous year adjusted for 2012 (Canadian dollars) in the UGAF 39. Only factors which had significant correlation with pelt sales were used to test correlation between 2 indices. Testing correlation between two indices In order to test if the 2 indices agreed, we calculated Pearson product-moment correlation (r) between pelt sales and each of the annual snow-tracking indices. If pelt sales had significant correlation with any potential external factors such as trapping effort, winter temperature, and winter precipitation, we also modeled pelt sales as a function of the significant external factors and one of the population indices derived from snow-tracking data. When the estimated parameter of an annual tracking index was significantly different from zero, we deemed that the 2 indices agreed. We replicated the procedure for each population index based on snow tracking. We conducted all statistical analyses in the software program R (R Development Core Team 2015). Results Annual track counts were 543 ± 723 (range = 63–2061) for American Red Squirrel, 120 ± 174 (range = 11–575) for weasels, and 139 ± 83 (mean ± SD) (range = 66–329) for American Marten. Mean annual pelt sales for American Red Squirrel, weasels, and American Marten were 184 (range = 74 –319), 361 (242–636), and 921 (629–1334) pelts respectively. None of the factors among trapping effort, winter temperature, winter Northeastern Naturalist 806 T. Kawaguchi, A. Desrochers, and H. Bastien 2015 Vol. 22, No. 4 precipitation, and pelt price in the previous year were significantly correlated with pelt sales (-0.64 < r < 0.44, P > 0.05). Mean number of track counts hr-1 200 m-1 for American Red Squirrel, weasels, and American Marten were 0.029 (0.004–0.131), 0.006 (0.007–0.027), and 0.008 (0.006–0.016), respectively. There was strong agreement between mean track counts per unit effort and pelt sales in American Marten and weasels (Table 2, Fig. 4), but the agreement was not statistically significant in the case of American Red Squirrel (Table 2, Fig. 4). Year-effect estimates of counts 200 m-1 (compared with 2004) in the GLM ranged from -1.76 to 2.34 for American Red Squirrel, -1.54 to 1.91 for weasels, and -0.61 to 0.81 for American Marten. Year-effect estimates agreed strongly with pelt sales of weasels and American Red Squirrel (Table 2, Fig. 4), but the agreement was not statistically significant in the case of American Marten (Table 2). Agreement between GLM-based snow-tracking indices and pelt sales was the highest in weasels, followed by American Red Squirrel and American Marten (Table 2). Discussion Track counts data were generally in strong agreement with pelt sales over the 9 years of this study, which indicated that both methods captured a real signal in the 3 taxa that we studied. Our results differed from those of Thompson et al. (1989), who found no correlation between track counts and trapping harvest in Short-tailed Weasel, Vulpes vulpes L. (Red Fox), or Canada Lynx. Agreement between track counts and pelt sales is remarkable especially because they were derived from completely Table 2. Pearson correlations between pelt sales and different population indices that were based on snow tracking for the 3 species (n = 9): American Marten, American Red Squirrel, and weasels in southern Quebec, Canada, 2004–2012. Year-effect GLM indicates estimates of year effect from a generalized linear model. Population index r P American Marten Mean of count/exposure hours 0.71 0.032 Year effect GLM 0.55 0.120 American Red Squirrel Mean of count/exposure hours 0.57 0.100 Year effect GLM 0.77 0.020 Weasels Mean of count/exposure hours 0.87 0.002 Year effect GLM 0.85 0.004 Figure 4 (following page). Comparison of population trends between snow tracking and pelt sales across three taxa: (a) American Marten, (b) American Red Squirrel, and (c) weasels, southern Quebec, Canada, 2002–2012. Two population indices are presented: left = year effect of a Generalized Linear Model (Year effect GLM), right = tracks/exposure time. Black lines represent pelt sales and gray lines represent population indices of year effect GLM (right) or tracks/exposure time (left). Vertical bars represent standard errors. Northeastern Naturalist Vol. 22, No. 4 T. Kawaguchi, A. Desrochers, and H. Bastien 2015 807 Northeastern Naturalist 808 T. Kawaguchi, A. Desrochers, and H. Bastien 2015 Vol. 22, No. 4 different methods, from different sets of locations, with contrasting trapping effort. Thus, both data sets likely captured population phenomena occurring over the entire study area. Agreement between indices may arise from common biases, such as weather and food-availability effects on exploratory behavior and movements. Thus, it could be argued that the variation in the population indices had little to do with actual population size. However, there was little confounding effect of trapping effort, mean daily temperatures. or precipitation (snow) on indices, because none of these factors were strongly correlated with indices; food-related biases may have occurred. Jensen et al. (2012) reported that success rate of American Marten harvest was lower in mast years of Fagus grandifolia Ehrhart (American Beech) and Sorbus aucuparia L. (Mountain Ash) than in year of mast failure. When food is abundant, American Marten could be less likely to be attracted to bait associated with trapping devices, possibly leading to underestimates of the relative abundance. High food-abundance might decrease track counts. For example, high levels of food abundance was reported to decrease daily movement length of Mustela nivalis L. (Least Weasel; Klemola et al. 1999), potentially leading to fewer track counts. American Marten track counts showed less agreement with pelt sales than did weasels. Lower agreement could have arisen due to enactment of a Quebec government policy that recommended that trappers stop trapping adults (H. Bastien, Ministère des Forêts, de la Faune et des Parcs, Quebec, Canada, pers. comm.). However, we were unable to determine year of the implementation. In Michigan, trapping harvest limits greatly impacted the number of Fishers harvested (Hiller et al. 2011). American Red Squirrel track counts also exhibited a lower degree of agreement with pelt sales than did weasel track counts. Sales of American Red Squirrel pelts might reflect population sizes poorly because not all pelts are sold, given that American Red Squirrel is often used as bait for other furbearers (H. Bastien, pers. comm.). Furthermore, American Red Squirrel pelts are considered of no significant commercial value by local trappers. The price of a American Red Squirrel pelts varied between 0.65 and 1.44 Canadian dollars (CAD) from 2004 to 2012 (where 1 CAD @ 1 USD), which was much less than the price of a weasel pelt (range = 2.24 CAD–8.87 CAD) or an American Marten pelt (range = 44.88 CAD–121.71 CAD). Thus, the motivation for capture and pelt harvest contrasted strongly between species. Population indices are not substitutes for true estimates of abundance. We did not compare the indices used in this study with population trends obtained from direct measures of abundance, but given the high correlations obtained, at least in the case of American Marten and weasels, our study adds to existing support for the use of either snow tracking or pelt sales. With those indices, one should be able to infer relative inter-annual population-changes of the furbearer species studied, and possibly others such as Red Fox and Canada Lynx. Therefore, use of these indices would be useful to investigate impact of local forestry and wildlife management on population dynamics of furbearer species. Northeastern Naturalist Vol. 22, No. 4 T. Kawaguchi, A. Desrochers, and H. Bastien 2015 809 Acknowledgments Financial support for this project was provided by a scholarship to T. Kawaguchi from the Leadership and Sustainable development Scholarship Program of Laval University, by a scholarship to T. Kawaguchi from the Foundation F.-K.-Morrow, and by a Natural Sciences and Engineering Research Council of Canada (NSERC) grant to A. Desrochers. We are grateful to the 29 skilled field-workers who helped collect snow-tracking data, and to the Ministère des Forêts, de la Faune et des Parcs for providing us with pelt-sales data, trappingeffort data, and geographical information. We thank D. Fortin, L. Bélanger, and C. Samson for their assistance in the design of the study and W.F.J. 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