A non-harvest based assessment of river otter (Lontra canadensis) in the Mohawk River Valley of New York.

A non-harvest based assessment of river otter (Lontra canadensis) in the Mohawk River Valley of New York. Summary Assessments of the status of wide-ranging and elusive species like river otter (Lontra canadensis) are difficult to conduct, and usually rely on data collected from managed harvests. Efficient and reliable protocols for tracking changes in otter populations in areas closed to harvest are needed, and the focus of this research. This two-year study builds on existing survey protocols for otter, and aims specifically to identify the optimal survey effort for robust inferences of site occupancy as an index to population size, establish a relationship between site occupancy and local otter abundance through inclusion of noninvasive genetic surveys, and provide a formal assessment of the status of river otter populations in the Mohawk River Valley an area where harvest restrictions are subject to revision within the next few years. Background The historical range of river otter included every watershed in New York State. Unregulated harvest early in the 18 th and 19 th centuries, spurred by the high value of otter pelts, led to reductions in otter range and numbers, so much so that populations were extirpated from the western part of the state, and a 9-year harvest moratorium was instituted in 1936 to stem further population declines (NYS DEC 2010a). More recently (1995-2000), management efforts to recover populations in central and western NY further involved translocations of otter from their stronghold in the Adirondacks. These recovery efforts have been successful to the degree that otter now occur in areas they had been extirpated from, but formal surveys of the status of populations, to a level of precision sufficient to judge whether harvest may be reopened, have not been undertaken. Roughly 1/3 of NY State is open to otter harvest today. In these areas, the NY State Department of Environmental Conservation (DEC) requires trappers to affix a plastic seal to the un-skinned carcass or pelt of all otter they take, providing biologists with harvest information and a means by which effort data can be collected for monitoring population trends. But in the roughly 2/3 of the state where trapping of otter is currently prohibited (referred to as non-harvest zones), such data are unavailable and additional effort is required to reliably survey population status. There are two large, non-harvest zones of particular interest with respect to otter populations today: 1) the Mohawk Valley and Catskill Wildlife Management Unit Aggregates where otter have persisted but harvest has remained closed due to concern over population viability, and 2) the larger extirpation area of central and western New York into which otter were reintroduced in the late 1990s (Figure 1). Otter pelts remain valuable, and interest in re-establishing harvest in the Mohawk Valley and Catskill areas is high. Although a fairly large database of incidental otter observations exists for these areas, its utility in evaluating the status of otter populations has proven limited primarily because efforts to validate reports were not made, no formal survey design was in place, and thus no standards in data collection were enforced. Similarly, the occasional 1

incidental or illegal harvest of otter in the non-harvest zones have provided brief glimpses but incomplete pictures of the status of otter populations in these regions. In 2009, DEC biologists ranked the highest two priorities for river otter management being development and implementation of nonharvest, population survey techniques, and the assessment of population status in select areas (NYS DEC 2009). True population estimates are difficult to acquire for wide-ranging and elusive species like otter. Precise estimates of abundance are typically feasible for most species in small geographic regions rather than covering the broad geographic range of interest over which harvest is managed for river otter. Thus, an alternative state variable is required that allows efficient monitoring of changes in otter populations over broad geographic space as well as in time. Habitat occupancy is one such alternative state (Mackenzie and Royle 2005), that is well rooted in ecological theory, specifically that as population size increases greater proportions of the available habitat will be occupied by the species. Tracking occupancy patterns has the advantage over traditional population surveys in not needing to observe or capture individuals, but rather rely on signs that animals are present at a given site. Sign surveys are commonly used as indices of abundance when monitoring elusive species. For otter, snow tracking (Sulkava 2007), scat surveys (Gallant et al. 2007), bridge-site (Gallant et al. 2008 and Crimmins et al. 2009) and random-site surveys (Crimmins et al. 2009) have all been used with varying levels of success. In 2002, DEC biologists implemented winter track surveys, in which otter sign at selected bridge sites were sought once each winter, in both harvest and non-harvest zones (Ermer 2003). Common to each of these methods is the critical assumption that if otter occupy a given survey site, they will be detected with certainty. This assumption ignores the all too common problem of false absences that occur when a site is used by a species but the species is not detected during the survey (Mackenzie and Royle 2005). The detection of animal sign, such as tracks, is highly dependent on local site characteristics (soil, vegetation) and snow conditions, and detection probabilities are most certainly always less than 1. Without accounting for detection probabilities, we systematically underestimate the number of sites occupied by a species. More concerning, without knowledge of detection probabilities the interpretation of a change in a population index (such as track counts) becomes obscured specifically, it remains unclear whether that change reflects variation in detectability or a change in the population itself. The problem of false absences can be corrected by modeling the probability of detection, such as independent sightability models that are used to correct aerial counts of animals. Sign-based surveys rely more on repeat surveys, such as the double-counting 2

often done to correct pellet groups counts along transects. Robust designs for sign-based surveys typically rely on repeated surveys of a given site that allow one to first identify sites where the animal is known to occur, and second determine what proportion of survey attempts yielded evidence of animal occurrence at the site. Of course, our ability to correctly conclude absence of a species at a given site increases with the number of repeat visits, but some optimal balance between the number of survey sites and number of repeat visits is required for an efficient population survey. The literature on occupancy models has recently been synthesized and compiled in to a software program (MacKenzie et al. 2006; Hines 2006) that facilitates simultaneous estimation of detection and occupancy probabilities, and provides routines for optimizing survey designs to achieve specific objectives. The primary objective of this research is to develop an optimal design for quantifying habitat occupancy by river otter as a means to monitor changes in the population status in areas closed to harvest, as well as providing a metric independent of harvest data elsewhere. Occupancy approaches are indeed useful for monitoring population status, but in themselves do not provide an estimate of the actual number of otter in a given area. The latter may be needed to formally assess whether local populations can support a given level of harvest, especially in areas where harvest is currently closed. Thus, additional information on otter numbers is required to link occupancy patterns to local otter abundances. When searching for sign includes collecting faeces or hair samples, then additional information on the number of otter can be acquired by extracting DNA from these materials and essentially finger-printing different individuals so as to enumerate how many occupy a given area (Waits 2004; Waits and Paetkau 2005). Thus, a second objective in this study is to include DNA-based assessments of the number of otter identified in different areas, as well as information on their repeated use of a given site, so as to effectively link occupancy patterns to local otter abundance. Lastly, conducting intensive field surveys is becoming problematic for state agencies that face ever-shrinking budgets and staffing levels. In light of this, low investment monitoring programs will become increasingly important for effectively monitoring populations over time and citizen-science efforts offer a potential avenue for meeting this challenge. People can be readily trained to identify otter sign, and tracking charismatic species like otter is already a popular citizen activity (NYS DEC 2010b). Linking that enthusiasm to a formal survey design that provides reliable information on otter population status is thus another consideration when identifying the optimal study design. Study Objectives 1) Design a robust protocol for monitoring habitat occupancy by river otter that is adaptable to implementation as a citizen-science program, 2) Link habitat occupancy patterns by river otter to local population size using noninvasive genetic samples, and 3) Formally evaluate the status of otter populations in the Mohawk Valley Wildlife Management Unit Aggregate. 3

Methods The study is broken into two components; a pilot study conducted Jan Mar 2010 to meet objectives 1-2, followed by full implementation of the refined survey design Dec 2010 Mar 2011 to meet objective 3. Year 1 - pilot study A representative subunit of the Mohawk Valley Wildlife Management Unit Aggregate (MVWMUA), specifically Wildlife Management Unit (WMU) 6S (Figure 2), will be intensively surveyed from Jan-Mar 2010 for otter sign. Building on the current otter survey protocol, a random sampling of 25 bridge sites over 3 rd and higher order streams will be surveyed weekly for otter sign (tracks, scat, or otter themselves). At each location, 500 meters of shoreline (both sides of stream), either up- or down-stream will be inspected for signs of otter presence using two observers. Gallant et al (2008) found that detection of otter increased with increased transect length at bridge sites, and the 500-m length represents a compromise between increased detection rates and efficiency (allowing us to survey 5 sites per day for a total of 25 sites per week). The direction of for a given survey, up- or down-stream, will be balanced among our samples so as to avoid some unforeseen bias due to choosing only one direction. The entire cumulative 1000 meters of shoreline will be inspected on each visit. WMU 6S 4

Data collected at each survey site will include type of sign observed (track, scat, animal, etc), the geographic coordinate of all sign, and relevant covariates thought to affect both habitat occupancy (e.g., stream order, substrate, presence/absence of beaver, shoreline characteristics) and detectability of sign (e.g., presence/absence of snow, time since last snow/rain event). Properly identifying the sign of otter remains problematic. Evans et al. (2009) found that even experienced observers had a substantial error rate in sign identification and suggested the use of methodologies such as photography to record and aid in verifying sign encountered in the field. Thus, all sign observed in the field will be photographed with digital cameras for independent verification by experts. Photo validation is likely to be a key component of a citizen-science survey effort, and so we will take care to develop protocols for reliable photo validation. Data will be analyzed as a single species, single season, occupancy model with heterogeneous detection probabilities as outlined in Mackenzie et al. (2006) using the program PRESENCE 2.0 (Hines 2006). Following estimation of detection and occupancy probabilities, simulation routines provided in PRESENCE will help identify the optimal design (number of sites versus number of repeat surveys) to achieve a specific target for our full surveys, such as identifying a difference of say 20% between different areas. All otter scats found will be collected, stored to protect the DNA content, and later analyzed to both confirm species and identify individuals. For individual identification, a multilocus genotype (10-14 loci) is required to identify different individuals (Taberlet et al. 1999). To account for the problems of low quantity and quality DNA acquired from scats, each scat will be analyzed at least three times to derive a consensus genotype (Taberlet et al. 1999, Waits 2004). DNA analyses will be conducted by sub-contract to Wildlife Genetics International or other appropriate commercial facility. We do not expect to find sufficient data for a formal capture-markrecapture analysis of population size in a given area, rather we will evaluate the persistence of occupancy of a site by known individuals, and the average size of groups using specific survey locations to scale occupancy patterns to local abundance. Year 2 full implementation The pilot study will result in an optimized design for monitoring occupancy and linking occupancy to abundance that will be applied to the entire MVWMUA (WMU 4A, 5R, 6R, and 6S; Figure 2) in year 2. Research Schedule Finalize proposal and complete MOU February 2010 First field season February 2010 March 2010 Progress report to NYS DEC Fur Team April 2010 Second field season Jan. 2011 March 2011 Progress report to NYS DEC Fur Team April 2011 Data analysis for thesis April 2011- September 2011 Thesis defense Spring 2012 5

Deliverables Final report March 2012 Products of this research will include public service presentations, peer-reviewed research articles, annual progress reports, and a comprehensive final project report detailing the survey design, implementation, and results. DEC will be notified of all invitations to give presentations on the otter project, and to whom any project materials or results will be made available. Written documentation and training will be provided to DEC personnel on all aspects of the field survey design and analysis. We will also involve DEC personnel in pre-reviewing manuscripts prior to submission to journals, credit the DEC as the project sponsor on all manuscripts and presentations, and provide the DEC with reprints following publication. Progress reports will be submitted to the DEC at the specified time intervals with a final, comprehensive report produced at the time of project completion. Key Personnel Dr. Jacqueline Frair, Principle Investigator, SUNY ESF Gordon Batcheller, Principle Investigator, NYS DEC Andrew MacDuff, M.S. candidate, SUNY ESF Cooperators NYS DEC Regional staff, Regions 4, 5, and 6 New York State Trappers Association Literature Cited Crimmins, S.M., N.M. Roberts, D.A. Hamilton, and A.R. Mynsberge. 2009. Seasonal detection rates of river otters (Lontra canadensis) using bridge-site and random-site surveys. Canadian Journal of Zoology. 87: 993-999. Ermer, M. 2003. New York winter river otter survey 2003. Unpublished internal report. New York State Department of Environmental Conservation. 6pp. Evans, J.W., C.A. Evans, J.M. Packard, G. Calkins, and M. Elbroch. 2009. Determining observer reliability in counts of river otter tracks. J. Wildl. Manage. 73(3): 426-432. Gallant, D., Vasseur L., and Berube, C.H. 2007. Unveiling the limitation of scat surveys to monitor social species: a case study on river otters. J. Wildl. Manage. 71(1): 258-265. 6

Gallant, D., Vasseur L., and Berube, C.H. 2008. Evaluating bridge survey ability to detect river otter Lontra canadensis presence: a comparative study. Wildl. Biol. 14: 61-69. Hines, J.E. 2006. PRESENCE software to estimate patch occupancy rates and related parameters. Patuxent Wildlife Research Center, Laurel, Maryland. [Online.] Available at www.mbr-pwrc.usgs.gov/software/presence.html. MacKenzie, D.I. and Royle, J.A. 2005. Designing occupancy studies: general advice and allocating survey effort. J. Appl. Ecol. 42: 1105-1114. MacKenzie, D.I., Nichols, J.D., Royle, J.A., Pollock K.H., Bailey, L.L., and J.E. Hines. 2006. Occupancy estimation and modeling: inference patterns and dynamics of species occurrence. Elsevier, Burlington, MA. 324pp. New York State Department of Environmental Conservation (NYS DEC). In prep. River otter management plan 2009-2018. 5pp. New York State Department of Environmental Conservation (NYS DEC). 2010a. River otter history in New York State. Web site, http://www.dec.ny.gov/animals/9372.html, accessed 9 Feb 2010. New York State Department of Environmental Conservation (NYS DEC). 2010b. River otter history in New York State. Web site, http://www.dec.ny.gov/animals/9369.html, accessed 9 Feb 2010. Sulkava, R. 2007. Snow tracking: a relevant method for estimating Lutra lutra populations. Wildl. Biol. 13(2): 208-218. Taberlet, P, L.P. Waits, and G. Luikart. 1999. Noninvasive genetics sampling: look before you leap. Trends Ecol. Evol. 14:323-327. Waits, L. 2004. Using non-invasive genetic sampling to detect and estimate abundance of rare wildlife species. Pages 211-228 in W. L. Thompson, editor. Sampling rare or elusive species. Island Press, Washington, D. C. Waits, L., and D. Paetkau. 2005. Noninvasive genetic sampling tools for wildlife biologists: a review of applications and recommendations for accurate data collection. J. Wildl. Manage., 69:1419-1433. 7