ASSESSING MOOSE HUNTER DISTRIBUTION TO EXPLORE HUNTER COMPETITION
Tessa R. Hasbrouck1, Todd J. Brinkman2, Glenn Stout3, and Knut Kielland2
1Alaska Department of Fish and Game, 2030 Sea Level Drive, Ketchikan, AK 99901, USA; 2University of Alaska Fairbanks, 1731 South Chandalar Drive, Fairbanks, AK 99975, USA; 3Alaska Department of Fish and Game, 1300 College Road, Fairbanks, AK 99701, USA
ABSTRACT: Traditional values, motivations, and expectations of seclusion by moose (Alces alces) hunters, more specifically their distributional overlap and encounters in the field, may exacerbate perceptions of competition among hunters. However, few studies have quantitatively addressed over- lap in hunting activity where hunters express concern about competition. To assess spatial and tempo- ral characteristics of competition, our objectives were to: 1) quantify temporal harvest patterns in regions with low (roadless rural) and high (roaded urban) accessibility, and 2) quantify overlap in harvest patterns of two hunter groups (local, non-local) in rural regions. We used moose harvest data (2000–2016) in Alaska to quantify and compare hunting patterns across time and space between the two hunter groups in different moose management areas. We created a relative hunter overlap index that accounted for the extent of overlap between local and non-local harvest. The timing of peak har- vest was different (P < 0.01) in urban and rural regions, occurring in the beginning and middle of the hunting season, respectively. In the rural region, hunter overlap scores revealed a concentration in 20% of the area on 16–20 September, with 50% of local harvest on 33% of the area and 54% of non-local harvest on 18% of the area. We recommend specific management strategies, such as lifting the air transportation ban into inaccessible areas, to redistribute hunters and reduce overlap and con- cerns of competition in high-use areas. We also encourage dissemination of information about known hotspots of hunter overlap to modify hunter expectations and subsequent behavior. Our hunter overlap index should prove useful in regions where similar concerns about hunter competition, hunter satis- faction, and related management dilemmas occur.
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Key words: Alaska, Alces alces, competition, harvest patterns, hunting, moose, overlap index
Local concern over competition for which occurs when the physical presence of game with non-local hunters has been an an individual directly interferes with the ongoing policy issue for wildlife managers in goals of another individual (Jacob and most places hunting occurs, including Alaska Schreyer 1980), or when individuals pursu- (ADF&G 2016a). We define local hunters as ing the same goal have different values people who hunt in the same area they reside, (Saremba and Gill 1991) or norms (Ruddell whereas non-local hunters are those who and Gramann 1994). We define competition travel away from their resident management as the rivalry between user groups for game area for hunting opportunities. These two or hunting space, either of which may be groups typically have the same hunting regu- limited or perceived to be limited, regardless lations, although in some situations rural res- of harvest success. For example, hunters idents in Alaska have priority on federal may experience negative consequences from lands. Competition may be linked to conflict competition even when they successfully
79 MOOSE HUNTER DISTRIBUTION – HASBROUCK ET AL. ALCES VOL. 56, 2020 harvest game because the pursuit of seclu- 1990, Brinkman et al. 2013). Hunters have sion or a certain traditional experience can expressed that the number of other hunters be equally or more important than harvest seen (i.e., perceived crowding) is an import- success (Vaske et al. 1995, Brinkman 2014). ant factor of hunt satisfaction (Heberlein and Competition is exacerbated when different Kuentzel 2002). Overall, these findings sup- hunter types (local hunters or non-local port multiple-satisfactions-approach-based hunters) overlap in the same area at the same management that recognizes multiple fac- time, creating a higher potential for direct tors contribute to hunt satisfaction (Hendee encounters. 1974). Conflict can occur when spatial and Proposed solutions to address competi- temporal overlap among hunters surpasses tion concerns are often related to changes in expected hunter density (Shelby and the allocation of hunting opportunities. For Heberlein 1986, Brinkman 2018), and when example, 22 proposals were submitted to the hunters encounter other hunters who exhibit, Alaska Board of Game requesting changes or are perceived to exhibit, hunting values in statewide or regional allocation of big and motivations that do not align with local game among hunter user groups between norms (Fix and Harrington 2012). This is 2015 and 2018 (ADF&G 2016a). Many pro- relevant in rural communities that may per- posals (n = 12) focused specifically on ceive that non-locals do not understand or moose (Alces alces) and described how respect traditional local practices (Kluwe local hunters are concerned that non-local and Krumpe 2003). Altered behavior in hunters take too many moose and create response to competition may increase the excessive competition. Importantly, the time, effort, and cost (e.g., fuel) of hunters, extent of competition has not been objec- which is particularly salient to rural commu- tively assessed in the areas where changes nities with relatively weak cash economies in allocation were requested. Information (Brinkman et al. 2014). Assessing character- regarding the distribution and overlap of istics of competition may help better define hunting activity by different stakeholder the problem, enhance communication, and groups should provide insight about the inform resolution of these issues (Decker extent of competition and inform potential and Chase 1997). solutions. Our research addresses this infor- Moose are an ideal species to explore mation gap by quantifying harvest patterns hunter competition because of their critical and overlap across space and time between nutritional, cultural, and economic impor- two hunter types (local and non-local) in tance to Alaskan residents (Timmermann two hunting regions (accessible urban and and Rodgers 2005, Northern Economics Inc. inaccessible rural areas). 2006). Hunter motivations may range from It is important to address concerns about meat provision to trophy experiences, and moose hunter competition because of the from spending time with family to interact- direct relationship with hunt satisfaction and ing with nature (Brinkman 2018). In 1987– success (Heberlein and Kuentzel 2002, Fix 2007 in Alaska, 29,000 hunters harvested and Harrington 2012). Managers regulate 7260 moose annually (on average) (Titus et hunter activity and maintain wildlife popula- al. 2009) that were used by > 90% of rural tions to optimize hunting opportunities and Interior Alaska community households maximize hunt satisfaction (Ericsson 2003), (Brown et al. 2010), representing > $78 mil- but satisfaction requires more than providing lion annually in the state economy sufficient animal density (Hammitt et al. (McDowell Group 2014).
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With so much interest, importance, and In addition to comparing different hunter investment in moose hunting, federal and groups, it is also important to consider how state agencies create moose hunting regula- hunting patterns may change in areas with dif- tions to sustain populations while optimizing ferent levels of accessibility (Brinkman et al. diverse hunting opportunities. The Alaska 2013). Access is a central logistical challenge Department of Fish and Game (ADF&G) has to hunting in remote parts of Alaska and fac- divided the state into 5 management regions tors into the allocation of hunting permits. For with 26 Game Management Units (GMU). example, easily accessible regions in Alaska Some GMUs are subdivided into subunits are more likely to be managed using draw per- allowing for more precise and localized man- mits (limited) because of higher hunter agement of wildlife populations. Management demand and the potential for hunter pressure seeks to mitigate biological and sociopoliti- and competition (Woodford 2014). Similar to cal issues (Bath 1995) with regulations that areas outside of Alaska, harvest generally are often complex and differ in space and increases with proximity to roads (Fuller time. The Alaska Constitution ensures equal 1990) or in areas with higher road density access to fish and wildlife for all Alaskans. (Hayes et al. 2002). General harvest permits However, the Alaska National Interest Lands (unlimited) in Alaska are more common in Conservation Act of 1980 (ANILCA; P.L. remote and inaccessible areas where hunter [Public Law] 96-487) mandates that hunting activity is reduced and overharvest less likely. and fishing priority be given to rural Alaskans The goal of our research was to explore on federal land. These contradictory pieces of hunter distribution in management areas with literature have added complexity to hunting different accessibility and between hunter systems in Alaska. groups to assess competition concerns. Our Although hunting opportunities exist objectives were to: 1) quantify harvest pat- for diverse interests of many stakeholder terns over time in regions with low (roadless types, conflict from perceived competition rural) and high (roaded urban) accessibility, and differences in value systems among and 2) quantify overlap in harvest patterns of hunter types occurs. Typically, all Alaskan 2 hunter groups (local, non-local) in rural residents (i.e., local and non-local hunters) regions where competition is more frequently recreate under the same hunting regulations. expressed. By comparing local and non-local Most hunting in Alaska occurs on public hunting patterns, our study addressed the spa- land, with many hunters using the same tial and temporal characteristics of user group areas year-after-year including setting up issues that have not been studied extensively hunting camps and informal territories on among moose hunters in Alaska. public land (Johnson et al. 2016, Brinkman 2018). Hunting motivations vary by individ- ual, but local rural hunters place a high sig- STUDY AREA nificance on meat provision, whereas We examined hunting patterns in GMUs non-resident and non-local hunters may be 20, 21, and 24 in Interior Alaska (Fig. 1) where more motivated by novel experiences and the main ecotype is the boreal forest com- trophy opportunities. It is common for prised mostly of white spruce (Picea glauca), people born in rural communities to move to black spruce (P. mariana), birch (Betula papy- urban areas for education or employment rifera), aspen (Populus spp.), and willow but return to rural communities to hunt (Salix spp.). The area contains low-lying wet- (Kofinas et al. 2010). lands mottled with lakes, low scrub bogs,
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Fig. 1. Map of study area depicting the high-access urban and low-access rural regions used to evaluate hunter competition in Alaska, USA. herbaceous meadows, and forb-herbaceous nearly all of Alaska’s boreal forest, includ- marshes. Intense winters and summers cre- ing the wildland-urban interface. ate annual temperatures ranging from -40 to To examine differences between urban 22°C (Brabets et al. 2000). Fire is the pri- and rural hunting regions, we compared mary disturbance regime; however, fire sup- 3 GMU subunits with high accessibility near pression is concentrated on only 17% of the Fairbanks (GMU 20A, 20B, and 20D; landscape, typically near roads and commu- 34,600 km2) with 2 subunits (GMU 21D and nities (DeWilde and Chapin 2007). Fire 24D; 28,000 km2) with low accessibility near alters moose habitat quality and has shaped Koyukuk, ~250 km west of Fairbanks.
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Table 1. Difference in area, census, density, highway availability, and employment rate for the high access urban and low-access rural study regions, Alaska, USA. Census and employment rate was created by U.S. Census Bureau 2011. Urban Region Rural Region Area (km2) 34,600 28,000 Census (2000) 104,079 1,461 Density (# people/km2) 3.0 0.05 Highway length (km) 737 0 U.S. Census Bureau unemployment rate 7% 20% # of moose hunting regulations (2017) 64 10
Although these two regions have relatively finer spatial resolution to assess hunter har- similar habitats, climate, and September vest. Trail systems are used to access parts of moose hunting regulations, they have vastly this region and hunters use road vehicles, different social systems, infrastructure, and ATVs, watercraft (motorized or man-pow- defining characteristics (Table 1), as well as ered), and aircraft for access (Brinkman moose and predator population dynamics. 2018). This GMU is subdivided into many Precise estimates of predator numbers are smaller hunt areas with unique regulations unknown, but wolf (Canis lupus) and bear that can change annually. For example, there (Ursus spp.) predation are significant causes were 64 different sets of regulations for of moose mortality in both regions. Although moose harvest in 2017 (ADF&G 2016b) that moose counts (density estimates) were done included antlerless moose, any bull, and ant- during our study period, they were not regu- ler or brow-tine restriction hunts. Law larly completed across the entirety of the study enforcement is low in the region but “peer-po- area and we did not incorporate them for this licing” may help limit illegal hunting activ- reason. These subunits were selected because ity, although the regional poaching level is of the importance of moose hunting in each unknown. In 2016, 6,222 moose hunting per- region despite their differences in social sys- mits were issued with 1,550 moose harvested tems and infrastructure. In the study GMUs, (25% success rate; ADF&G 2020). hunters are typically allotted one moose during a September hunting season regardless Low-access rural region of residency, ethnicity, or background; The rural region (GMUs 21D and 24D) rural-priority hunts were not instituted during was divided into 35 UCUs, not road-accessi- the study period. Although regulations around ble, and situated along the Yukon and Fairbanks have been dynamic, harvest Koyukuk Rivers (Fig. 2); local hunters are chronology has remained relatively constant predominantly Koyukuk Athabascan. The over time in both study regions. Koyukuk Controlled Use Area (KCUA; 12,408 km²) straddles the northern GMU High-access urban region 21D and the southern GMU 24D. Although The road-accessible urban region (GMU moose hunting occurs across the entirety of 20A/B/D) was situated around Fairbanks this region, the majority (especially non-lo- North Star Borough and was divided into 87 cals) occurs within the KCUA. In other com- Uniform Coding Units (UCU) to provide munities on the Yukon River, all hunters
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Fig. 2. Map of Uniform Coding Units (UCU) within Game Management Units 21D and 24D in the low-access rural study region used to evaluate hunter competition in Alaska, USA. essentially travel and hunt by boat (Johnson required to stop at the check-in station, a et al. 2016). Although some trail systems comprehensive 20-year dataset of hunter and exist, they are seldom used by moose hunt- harvest information was available for the ers. The KCUA has a mandatory ADF&G KCUA. The check-in process helps ensure check-in station on the Koyukuk River and that hunters are harvesting legally, and the reporting rate is believed high compared although minor violations are common, it is to other rural areas. Because all hunters believed that egregious activity is minimal coming from the lower Koyukuk River are during the hunting season; the level of
84 ALCES VOL. 56, 2020 MOOSE HUNTER DISTRIBUTION – HASBROUCK ET AL. poaching outside of the hunting season is hunters can only participate if they receive a unknown. Access to this region for non-local draw permit, and although they do not have hunters requires considerable logistic effort to destroy an antler, they are mandated to and expense. For example, a Fairbanks resi- harvest a bull with a minimum of 4 brow dent would drive 220 km north on the Dalton tines on at least one antler or with antler Highway to the Yukon River bridge and then spread >127 cm. In 2016, 756 hunt permits boat 483 km down the Yukon River to access were issued and 375 moose were harvested Galena near the mouth of the Koyukuk (50% success rate) (ADF&G 2020). River; however, many non-local hunters travel much further to access this hunting METHODS region. Hunter database This region had 10 different sets of The best available information on hunter moose harvest regulations in 2017 (Table 2; patterns was accessible from ADF&G annual ADF&G 2016b). Under the registration hunt, harvest data. Although mandatory harvest Alaskan hunters (regardless of ethnicity or reporting exists statewide, harvest data is residency) can shoot any bull but are required likely incomplete due to underreporting in to render the antlers unusable for trophy con- remote areas (Schmidt et al. 2015), but is not sideration by cutting one antler palm in half considered an issue in our rural region where and forfeiting the cut portion to ADF&G. hunters accept and are compliant with the This “antler destruction” regulation was cre- check-in station. We assumed that hunters ated to emphasize harvest for meat rather who report harvest are representative of all than trophy value (G. Stout, pers. commun.). successful hunters within the GMU with Resident hunters can apply for a draw permit respect to location of harvest, hunt patterns, that allows them to harvest any size bull and and effort. Because unsuccessful hunters do to keep the antlers intact. Non-resident not report fine-resolution details such as
Table 2. Regulations in the low access rural hunting region in 2016 (ADF&G 2016b). Permit types are registration (RP) and drawing (DP), and residency types are residents (R) and non-residents (NR). GMU Permit Type Residency Special Instruction Open Season 21D/24D, RP R Any bull, destroy antler Sept 1–Sept 25 within KCUA DP R Any bull Sept 5–Sept 25 DP NR Antlers ≥127 cm, OR ≥4 Sept 5–Sept 25 browtines on one side 21D, outside RP R Any bull, destroy antler Aug 22–Aug 31, KCUA Sept 5–Sept 25 DP NR Any bull Sept 5–Sept 25 DP NR Antlers ≥127 cm, OR ≥4 browtines Sept 5–Sept 25 on one side 21D, east of DP R Any bull Sept 5–Sept 25 KCUA DP NR Antlers ≥127 cm, OR ≥4 browtines Sept 5–Sept 25 on one side 24D, remainder RP R Any bull, destroy antler Sept 5–Sept 25 DP R Any bull Sept 5–Sept 25 DP NR Antlers ≥127 cm, OR ≥4 browtines Sept 5–Sept 25 on one side
85 MOOSE HUNTER DISTRIBUTION – HASBROUCK ET AL. ALCES VOL. 56, 2020 temporal and spatial details of their hunt, our evaluate if local and non-local hunters used analysis was limited to hunters who har- the same hunting space. We used a vested a moose. We acknowledge that unsuc- Kolmogorov-Smirnov test to compare the cessful hunters contribute to and are affected temporal overlap (i.e., distribution of date of by hunter overlap and competition, but we kill) among hunters for the full study period. assumed that successful hunters were a rea- Further, we compared local harvest and sonable representation of all hunters because non-local harvest over two even time peri- the hunter success rate was consistent over ods: 2000–2008 and 2009–2016. This split time, and many hunters use the same hunting allowed us to examine harvest locations over area year-after-year, yet are unsuccessful time for each hunter type while maintaining 50% or more of the time. adequate sample sizes. Due to non-normal We analyzed all harvest data during the distribution, we used paired Wilcoxon signed September hunting season from 2000 to 2016. ranks tests to assess changes in UCU use for We included the following harvest data fields each hunter type between the two study peri- in our analysis: hunter residency, success (yes ods and used Mann-Whitney U tests to quan- or no), date of kill, and hunt location (UCU). tify the differences between local and Although harvest data included number of non-local hunters use in each UCU for each hunting days, we deemed these data insuffi- time period. cient to assess hunter effort because of Considering that spatial overlap was changes in reporting rates and possible issues compared among UCUs, we created a rela- with memory recall. We excluded data that tive overlap index (Eq. 1) that incorporated were missing hunter residency or date of kill. the non-local hunter density and the local Antlerless hunts and hunts that occurred out- hunter proportion in each UCU. Due to the side the normal September hunting season low number of non-resident hunters and the were not included in the analysis because similarity of patterns, we pooled non-resi- these hunts did not occur regularly during the dents and non-local resident hunters. We study period and were not comparable used river length (Hydrology 1:1000000) between regions. Special hunts introduce cir- within each UCU to calculate non-local cumstances that potentially influence harvest hunter density because nearly all hunters in decisions that may introduce bias or inaccu- the rural region access their moose hunting racy in our results. areas by watercraft (Johnson et al. 2016) and areas away from navigable waters are seldom Analysis used. Current regulations prohibit airplane ESRI ArcGIS was used to map and visu- transportation for hunters within the KCUA alize harvest locations and the underlying portion of 21D/24D. We ranked each UCU in landscape. We used a Mann-Whitney U test order of highest relative overlap index. The to compare the date of kills in the high-ac- relative overlap index was calculated for cess urban and low-access rural regions each UCU across the entire study period, and (Objective 1). We calculated descriptive sta- between the two time periods (2000–2008 tistics on the proportion of successful local and 2009–2016). We used a Wilcoxon signed- and non-local hunters in each UCU in the ranks test to compare overlap in distribution low-access rural region to measure spatial between the two time periods to capture any and temporal overlap between these 2 hunter temporal change in overlap levels. groups (Objective 2). We compared the pro- In our index, an increase in non-local portions using a Mann-Whitney U test to hunter density within a specific UCU caused
86 ALCES VOL. 56, 2020 MOOSE HUNTER DISTRIBUTION – HASBROUCK ET AL. an increase in the relative overlap index a relative overlap index for each UCU for score, but the increase was mediated by the each time block across the full study period. level of importance of that UCU for local This step allowed us to simultaneously eval- hunters. For example, a UCU with a high uate the temporal and spatial overlap during non-local hunter density that had high the hunting season while maintaining ade- importance to local hunters had a higher quate sample sizes. score than a UCU with a high non-local hunter density that had low importance to RESULTS local hunters. This approach was reasonable A total of 25,113 harvest records from because this project was derived from and 2000 to 2016 were analyzed with 19,423 and motivated by local hunter concerns in the 5692 moose in the urban and rural regions, rural region, and competition concerns respectively. The urban and rural regions between hunter types may be asymmetrical exhibited different date-of-kill distributions (i.e., locals are concerned about non-local (P < 0.01) based on the date of peak harvest presence, but not necessarily vice versa). (Fig. 3). Peak harvest occurred at the begin- ning of the season in the high-access urban #Non-Local region and in the middle of the season in the Hunters low-access rural region. Although local (n = × 2286) and non-local hunters (n = 3156) in the CumulativeRiver rural region had different distributions in date Length(km) (Eq. 1) of kill (P < 0.01), they had complete tempo- Relative ral overlap across the hunting season (Fig. 3). #Local Hunters In the rural region, 50% of the local har- = Overlap TotalLocal Hunters vest occurred on 33% of the hunting area (5 Index UCUs), whereas 54% of the non-local har- vest occurred on 18% of the area (3 UCUs). Finally, we split the hunting season into Local and non-local hunters harvested fewer 5 equal time periods (1–5, 6–10, 11–15, moose in the remaining UCUs (Fig. 4), 16–20, and 21–25 September) and generated revealing an uneven hunter distribution
Fig. 3. The total daily number of moose harvested in September 2000–2016 by local, non-local, and non-resident hunters in a high-access urban hunting region, Alaska, USA.
87 MOOSE HUNTER DISTRIBUTION – HASBROUCK ET AL. ALCES VOL. 56, 2020 across the region. Local and non-local hunt- local hunters changed (P = 0.02) as their ers overlapped spatially (P = 0.448) but with proportional use declined in 5 UCUs and differences in importance level among increased in 4 UCUs. Further, local and UCUs (Fig. 5). Spatial distribution of non-lo- non-local hunters harvested a moose in sim- cal hunter was similar (P = 0.70) in the early ilar locations in the early (P = 0.43) and late (2000–2008) and late study periods (2009– (P = 0.47) study periods, indicating that 2016). Conversely, spatial distribution of overlap existed across time.
A 40 t 35 30 25 20 15 10
Proportion Non-Local Harves 5 0 0510 15 20 25 30 35 UCU (n = 35)
B 40 35 t 30 25 20 15 10 Proportion Local Harves 5 0 0510 15 20 25 30 35 UCU (n = 35)
Fig. 4. Proportional use of UCUs by local (A) and non-local hunters (B) within the low-access rural hunting region, Alasaka, USA. The X-axis refers to the most used UCUs for each hunter type and not a specific sub-area; therefore, the UCU value does not necessarily correspond to the same location for local and non-local hunters. Data points displayed as boxes sum together UCUs that constituted 50% of harvest by local (n = 5) and non-local hunters (n = 3).
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Hunter overlap scores ranged from 0 to regions exhibited different distributions of 0.106 with a mean score of 0.010 (SD = 0.02) date-of-kill. The high-access urban region and median score of 0.003. In the rural spiked soon after the opening of the hunting region, high overlap existed in 12% of the season, followed by steep decline and then hunting area, moderate overlap in 8%, and moderate increase near the middle of the minimal overlap in the remaining 80% season, and then rapid decline; this pattern (Fig. 6). Twenty-seven UCUs had overlap may reflect that specific regulations end on scores less than the mean, 4 UCUs had scores different dates (15, 20, 25, 30 September). ranging from 0.011 to 0.014, and 4 UCUs Similar research with other species indicates had the highest overlap scores of 0.033, that white-tailed deer (Odocoileus virgin- 0.051, 0.054, and 0.106. The relative overlap ianus) harvest is most closely associated index did not differ between the early and with day of hunting season (Hansen et al. late study period (P = 0.92). Within the hunt- 1986) and red deer (Cervus elaphus) harvest ing season, the relative overlap index showed increases on weekends and moon phase that the extent of overlap (by 5-day periods) (Rivrud et al. 2014). We speculate that within each UCU ranged from 0 to 0.017 warmer weather early in the season might (mean = 0.0005, SD = 0.0016) and was high- create more comfortable conditions for est on 16–20 September (Fig. 7). urban hunters less reliant on a successful annual harvest. Or, the beginning of the sea- DISCUSSION son encompasses Labor Day weekend which Our research focused on analyzing the provides urban hunters an extra hunting day spatial and temporal overlap between hunter in a region where employment is high rela- groups because we assumed that the degree tive to remote communities. “Opening day of overlap is directly related to the level of rush” may be a dominant factor in urban perceived competition. Our analyses indi- areas where there are higher numbers of cate that rural and urban hunting hunters targeting a limited number of moose.
.! .!
Legend Legend .! Communities .! Communnities .! Rivers (simplified) Rivers (simplified) >10% Harvest (33% actually) .! >10% Harvest .! .! .! 5-9.9% Harvest .! .! .! 5-9.9% Harvest 1-4.9% Harvest 1-4.9% Harvest 0-.9% Harvest <1% Harvest .! .!
¯ 0630 0120 Kilometers ¯ 0630 0120 Kilometers
Fig. 5. Percent harvest by UCU in 2000–2016 by non-local (left) and local (right) hunters within the low-access rural hunting region, Alaska, USA.
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Fig. 6. Depiction of UCUs with the top 4 highest overlap scores, moderate overlap scores, and zero- low overlap scores in the low-access hunting region, Alaska, USA.
We speculate that hunting patterns in the (Joly et al. 2015), with peak movement low-access rural region are driven by biolog- during the rut around 1–7 October (Brown et ical and environmental conditions more so al. 2018) indicating that hunters may have a than in the high-access urban area with higher chance of encountering moose late in higher hunter numbers. Fewer hunter num- the season. Also, cooler temperatures in late bers/density in the rural region may afford September facilitate meat preservation in hunters the opportunity to align their effort remote regions where several days may with ideal environmental conditions. Bull elapse between harvest and processing; moose increase movement as rut approaches lower ambient temperatures are critical as
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Fig. 7. 3D plot showing when and where overlap is greatest between local and non-local hunters in the low access rural hunting region, Alaska, USA. The X-axis refers to individual UCUs, y-axis to the hunter overlap scores, and z-axis to the time period within the hunting season. Colors help visualize scores. meat begins to spoil at 4.4 °C (USDA 2011). conditions reducing hunt success or lower Further, local hunters suggest that hunting local participation. Local hunters may have moose is easier after leaf-fall in mid- to late more flexibility in their hunting dates September in Interior Alaska (Appendix A) whereas non-local hunters likely choose because of improved sightability along river hunting dates in advance of the season networks and sloughs. Due to traditionally because of the substantial time and effort lower employment opportunities in rural needed to access the region. Also, some regions, some local hunters presumably have draw permits shorten the hunting season for flexibility when selecting hunting dates. non-resident hunters, therefore artificially Local and non-local hunters were not bounding their hunting period. Our results evenly distributed across the landscape as indicated that local hunters changed hunt 50% of local harvest occurred in 5 UCUs, locations over time, but without further with the remaining 50% across 30 UCUs. investigation, it is difficult to determine if Similarly, 54% of non-local harvest occurred this was a result of overlap, perceived com- in 3 UCUs with the remaining 46% in 32 petition, shifting moose densities, or other UCUs. In the rural region we found that parameters. overlap was concentrated within 20% of the The “availability framework” uses area, indicating that 80% of the area had hunter accessibility, game abundance, and minimal overlap. Figures 5 and 6 indicate seasonal distribution of game to inform that the greatest overlap and highest use by local management decisions, and is also local and non-local hunters occurred in useful to assess hunting opportunities UCUs crossed by the Yukon and Koyukuk (Brinkman et al. 2013). Consistent with this Rivers. Although tributaries exist throughout framework, our findings suggest that along the area, these major rivers offer more con- with game supply (e.g., moose abundance sistent and safe access to moose. Overlap and distribution), it is critically important to occurred later in the month, aligning with account for hunter access when exploring the peak harvest. Low overlap in the early how hunting systems function. For exam- season could reflect inferior weather ple, our research shows concentrations of
91 MOOSE HUNTER DISTRIBUTION – HASBROUCK ET AL. ALCES VOL. 56, 2020 harvest mainly in UCUs with major naviga- conflict where hunters expect solitude but ble rivers (Fig. 5). Similarly, Lebel et al. encounter others. (2012) found that higher levels of access Our ability to assess hunter competition increased the number of harvested white- was limited by the data that successful hunt- tailed deer. Hunters in areas with different ers provided on harvest reports. Because we levels of accessibility may also have differ- were unable to assess overlap by unsuccess- ent perceptions of acceptable levels of ful hunters, the role of hunter effort in over- crowding (Shelby et al. 1989). Due to the lap and perceived competition is unknown. near complete reliance on waterways for To fully understand competition and hunter access, we used cumulative total river length satisfaction, we recommend that more data within each UCU to determine hunter den- be collected from unsuccessful hunters, sity. Application in developed regions with especially with regard to hunter effort. uniform access could use road length, travel Questioning hunters about their own percep- corridor length, or overall area as parame- tions as to why they were unsuccessful (e.g., ters. Identifying differences in regions with bad weather, too many people, varying lev- good and poor access may provide insight to els of effort, too many predators) may help employ local management strategies where managers address the root of satisfaction hunter satisfaction is a concern. issues. Tensions with non-local hunters are This research focused on competition often discussed as issues in rural communi- concerns of local hunters and we acknowl- ties, but the magnitude and scope of such edge that non-local hunters’ perceived com- attitudes and tensions have not been assessed petition by non-local hunters was not quantitatively. Hunter survey research may assessed, and that they may feel negative provide insight into these characteristics and impacts from competition or may not agree their influence (Brinkman 2018). Without with or appreciate local-societal norms. A hunter interviews, monitoring hunter inter- more inclusive survey should assess the per- actions in the field, or conducting a robust ceived competition in a management area by moose behavioral study during the hunting all hunters – local, non-local, and non-resi- season, we do not have estimates of the dent. Yet, we provide novel information actual levels of competition. However, our about previously untested hypotheses related findings support and inform future studies to the issue of hunter competition. Our that may directly assess competition and its hunter overlap index can be modified and potential consequences. applied for different regions and game spe- Local hunters are generally more toler- cies to assess the existence and relative level ant of other local hunters (Brinkman 2018). of hunter competition. Because this overlap Future research might consider assessing equation index does not include a temporal proximity of harvest to communities, birth component, it would be important for place of hunters instead of current address, researchers to assess the relative overlap and hunter expectations as to where other score within salient time periods (Fig. 7). hunters will be encountered. For example, constructing maps with overlap index scores (see Fig. 2) would help local hunters distin- MANAGEMENT IMPLICATIONS guish areas of potential conflict with high To minimize hunter dissatisfaction, we numbers of non-local hunters. Additionally, recommend that game managers employ “unexpected” overlap may increase hunter strategies to distribute hunters from areas
92 ALCES VOL. 56, 2020 MOOSE HUNTER DISTRIBUTION – HASBROUCK ET AL. with a relatively high overlap index scores, ACKNOWLEDGMENTS especially during the peak and latter half of Our research was part of a larger project, the hunting season. We encourage altering University of Alaska Fairbanks’s Community current regulations, specifically lifting the Research Partnerships for Supporting prohibition of using aircraft for transporta- Sustainable Traditional Harvest Practices. tion into the KCUA with the caveat that air- Through this larger project, individual stud- craft use occur >1.6 km from the Yukon and ies were designed to work collaboratively Koyukuk River corridors. This should with communities to design objective, rele- increase hunter distribution across the land- vant, and important research related to hunt- scape, provide unique opportunities for ing and fishing practices. Two entities, non-local hunters, and reduce conflict with Koyukuk Traditional Council and Nulato local hunters who seldom use airplane trans- Tribal Council, located in the rural GMUs port or travel > 1.6 km from a navigable partnered with us to address local research river. We further recommend providing priorities related to hunter competition. We information about harvest hot spots and gratefully acknowledge Nulato Tribal “high overlap” to help hunters avoid areas Council (specifically A. Demoski), Koyukuk with historically high hunter density and Traditional Council, Council of Athabascan potential competition. Ultimately, both indi- Tribal Governments, and Tanana Chiefs vidual hunters and agencies should adapt to Conference for their partnerships and input. alleviate hunter competition and optimize Further, we thank K. Heeringa and the hunt satisfaction. Community Research Partnerships for Our research provides a methodology to Supporting Sustainable Traditional Harvest quantify hunter distribution which is broadly Practices crew for their guidance and feed- applicable in addressing concerns about back. Our research was funded by the hunter competition, a problem shared by National Science Foundation (award many states and provinces. By using pre-ex- 1518563) and NASA (award NNX15AT72A). isting data (i.e., harvest records) that nearly all wildlife management agencies collect, REFERENCES our approach can help managers identify Alaska Department of Fish and Game where new strategies might be useful to (ADF&G). 2016a. Interim reports moose – modify hunter distribution to ease hunter Year 2016.
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