Molt Phenology in the Mountain Goat

Molt Phenology in the Mountain Goat

Molt Phenology in the Mountain Goat Project lead: Katarzyna Nowak, The Safina Center Statistical analyses: Shane Richards, University of Tasmania Local collaborators in Canada: Don Reid, Wildlife Conservation Society-Canada and Aerin Jacob, Y2Y Conservation Initiative Citizen science web portal management: Greg Newman, CitSci, Natural Resource Ecology Laboratory, Colorado State University Climate data sourcing: Nick Young, Natural Resource Ecology Laboratory, Colorado State University Assistance with image sourcing & processing: Amy Panikowski, Ross Donihue, Joanna Nowak Field assistance in the Yukon: Atsushi Sugimoto Project Funders, Partners, and Sponsors: U.S. National Park Service and Yellowstone to Yukon Conservation Initiative to Wildlife Conservation Society through support to Joel Berger The Safina Center and Kokopelli Packraft through support to Katarzyna Nowak

Persons who contributed data in the form of mountain goat photos to this project appear on the next page

Suggested citation: Nowak K., Richards S.A., Reid D., Jacob A., Newman G., Young N., Panikowski A., Donihue R., Nowak J., Sugimoto A., Beckmann, J., Berger J. 2018. Molt phenology in the mountain goat. Unpublished report. U.S. National Park Service, Y2Y Conservation Initiative, Wildlife Conservation Society, and The Safina Center. November 2018.

Cover Photo: Mountain goat adult male molting on face, neck, shoulders (evaluated to be 44% shed from other photos of his side view) at a remote camera trap tied to a rock on a ledge at 1018 m above the Windy Arm of Tagish Lake, Pooly Canyon, near Montana Mountain, and above Klondike Highway, Yukon Territory, Canada. 2

Photo Contributors

Text size reflects relative contribution in number of photos.

3

Executive Summary As temperatures continue to rise around the world, wild animals will have to cope with these changes including summer heat. The cold-adapted species will confront challenges. Among them is North America’s mountain goat, an alpine ungulate adapted to snowy, cold conditions in montane environments where its double layer winter coat brings costs during summer. In April 2018, the Mountain Goat Molt Project was initiated through a series of collaborations across multiple institutions to examine the patterns and phenology of mountain goats’ winter coat molt in relation to warming. Amassing a sample of over 500 photographs – 72% of them crowd-sourced from citizen scientists (nonresearchers) from across mountain goat range – we sought to address the fundamental question: Has the onset of molt become earlier in recent years? We explored potential effects of extrinsic and intrinsic variables on molt including latitude, elevation, goat sex and new presence of young of the year. Our dataset was augmented by over three months of repeated photo samples collected in the Yukon Wildlife Preserve (YWP), Yukon, Canada, where 20 mountain goats are housed in two herds, one breeding, one non-breeding. We also captured camera trap photos of wild mountain goats at three sites in southern Yukon, the far north of mountain goat range where, as yet, their molting patterns are unaffected by ticks. Our main findings were: •

•

•

•

•

We did not find evidence of a temporal change in molt across time. We do not know whether a lack of relationship occurs but our small sample of only ten ‘older’ photos (from 1948-1999) would prevent the detection of a trend had one occurred. On the other hand, at a more proximate level, goats shed earlier at higher latitudes (where rate of photoperiod change is faster) and later at higher elevations (possibly due to temperature lapse rate). Males shed 14 days before females. Using data with repeated observations of animals housed at YWP, males shed approximately a month earlier than females. By mid-June the proportion of coat shed by sex and reproductive status was: males - 50% shed; females without offspring of the year - were 50% shed 29 days later, and females with newborns were at a similar point a further 17 days later. Data collected at three sites in southern Yukon where mountain goats are wild showed similar patterns in that males shed one month earlier than females; data were insufficient to detect effects of young of the year on maternal molt. We used provided geo-referenced locations of photos to first source elevation data, before attributing remote sensing data from AppEEARS and climate data from ClimateNA to each record. When feasible, a total of 35 variables were associated with each photograph. Our investigation of effects of vegetation properties on molt was limited to Gross Primary Production (GPP). We did not detect an effect of GPP on molt.

Data limitations should be considered in our findings. While our sample size is more than 500 photos, more than 95% are from the year 2000 and later. Additional older photographs are available to boost sample size from ‘historical’ images but further effort is required to locate and use these. Further, the rendering of photos for analyses was slowed because our hand processing method was less efficient than might be other methods, for instance Artificial Intelligence (AI) tools. At the time of this report, we were still crowd-sourcing additional photos from across time and mountain goat range so that further analyses can enable the potential teasing out of effects of additional variables. These will include mountain goat age, and environmental variables in and before months corresponding to when photos were taken including additional measures of vegetation green up and metrics of winter severity.

4

If, upon analyzing a larger set of data, we still find that mountain goats are not keeping up with thermal change by shedding their winter coats earlier in the year, behavioral strategies may or may not be one mechanism for coping with increasing temperatures. How, or if, this occurs will require additional study. Understanding those behavioral options will help conservation managers to better provide for mountain goats’ needs. In citizen science terms, we had a positive response from the public. In addition to hundreds of submissions of geo-tagged photos, we received stories from recreational anglers and people who live near lakes about mountain goats observed swimming and drinking at lakes, from truck drivers about mountain goats crossing roads at dusk and dawn (including to access lakes), from outfitters about alpine ungulates allegedly darkening coat color in response to less snowy periods, and emails from U.S. National Park and Forest Service staff requesting copies of our project poster to engage visitors in their areas. Using winter coat molt in a mammal as a discussion topic has proven to be a relatable and non-threatening way to engage the public on climate- and weather-related effects including those who do not necessarily track climate science. Continued inter-American conversation and collaboration including cross-boundary participatory science initiatives such as this one, have the potential to inspire a wider audience to engage and help conserve and protect vulnerable alpine species like the mountain goat.

5

Contents

Photo Contributors .....................................................................................................................................................3 Executive Summary ...................................................................................................................................................4 List of Figures and Tables ...........................................................................................................................................8 Introduction ...............................................................................................................................................................9 Methods .................................................................................................................................................................. 10 Photo Data Sources ............................................................................................................................................. 10 I Citizen science and photography................................................................................................................... 10 II Remote camera-trapping in southern Yukon ............................................................................................... 11 Climate and Remote-Sensing Data Sources ........................................................................................................ 14 Data Analysis ....................................................................................................................................................... 14 Analysis of shed extent from photographs ..................................................................................................... 14 Statistical models............................................................................................................................................. 14 Results ..................................................................................................................................................................... 17 Photo Sample ...................................................................................................................................................... 17 Citizen Science Data ............................................................................................................................................ 19 Repeated Samples from Yukon Wildlife Preserve ............................................................................................... 23 Wild Goats ........................................................................................................................................................... 27 Comparison Across Yukon Sites, Wild and Captive ............................................................................................. 30 A Note on Molt Patterns ..................................................................................................................................... 32 Challenges............................................................................................................................................................ 33 Night time photos ............................................................................................................................................ 33 Sexing goats in photos ..................................................................................................................................... 33 Mostly Dall sheep at site in Kluane NP ............................................................................................................ 33 Sourcing older photos ..................................................................................................................................... 36 Ad Hoc Observations of Behavioral Thermoregulation....................................................................................... 36 Discussion................................................................................................................................................................ 39 Effects of Sex and Kidding ................................................................................................................................... 39 Effects of Latitude, Elevation, and Gross Primary Production ............................................................................ 40 Age ....................................................................................................................................................................... 41 Participatory Science Aspect ............................................................................................................................... 41 Challenges............................................................................................................................................................ 41 Next Directions .................................................................................................................................................... 42 6

References............................................................................................................................................................... 43 Appendices .............................................................................................................................................................. 46 Appendix I Ways we crowd-sourced photos of mountains goats ....................................................................... 46 Appendix II Goat molt brief methods, other climate sources and variable description – Nick Young ............... 47 Appendix III Correlations of fixed effects (before using linear mixed effects models) – Shane Richards........... 52 A.

Yukon Wildlife Preserve Data. Fixed effects: goat sex, kid, day of year. ................................................. 52

B.

Wild Yukon Data. Fixed effects: goat sex, kid, day of year. ..................................................................... 52

C.

Public-Sourced Data. Fixed effects: day of year, year, goat sex, latitude. .............................................. 52

Appendix IV Project outreach.............................................................................................................................. 53 Media coverage ............................................................................................................................................... 53 Project info shared on a variety of websites ................................................................................................... 53 Yukon Wildlife Preserve summer 2018 newsletter ......................................................................................... 54 Other project resources .................................................................................................................................. 54 Acknowledgements ................................................................................................................................................. 55

7

List of Figures and Tables Figure 1 Adult mountain goat male in Mt. White forested area. This camera trap was at 1013 m a.s.l. ............... 12 Figure 2 Mountain goat billy above Tagish Lake and Klondike Highway; camera trap set at 963 m. ..................... 12 Figure 3 Mountain goat billy climbing a steep trail, Kluane National Park. Camera trap set at 862 m. ................. 13 Figure 4 Our process for estimating shed extent from photos. Photo by Alison Sheehey ..................................... 15 Figure 5 Map showing mountain goat range and source locations of our photos. ................................................ 17 Figure 6 The latitude and elevation associated with our 516 photo samples. ....................................................... 19 Figure 7 Fraction shed by day of year across years. ................................................................................................ 20 Figure 8 Shedding begins earlier at higher latitudes. .............................................................................................. 21 Figure 9 Mean fraction shed by goat sex and latitude group. ................................................................................ 21 Figure 10 Correlation matrix for latitude, elevation, and Gross Primary Production. ............................................ 22 Figure 11 State of shed in four individuals at Yukon Wildlife Preserve on June 8 (left) and June 27 (right). .... 23-24 Figure 12 Winter coat onset and progression in 14 adult individuals at the Yukon Wildlife Preserve. .................. 25 Figure 13 Fraction shed by sex and date and kid. ................................................................................................... 25 Figure 14 Rendered images of three nannies at Yukon Wildlife Preserve. ............................................................. 26 Figure 15 Photos of the same three nannies represented in Figure 14 on 14 August............................................ 26 Figure 16 Fraction shed by month and elevation at three sites in southern Yukon. .............................................. 27 Figure 17 Fraction shed across months by goat sex and presence of kid in wild Yukon......................................... 28 Figure 18 Three goats on the same Mt. White trail in late June ............................................................................. 29 Figure 19 Shedding fractions at four sites in the southern Yukon. ......................................................................... 30 Figure 20 Fraction shed by date, site, kid. ............................................................................................................... 30 Figure 21 Camera trap photos of three billy goats in mid-June .............................................................................. 31 Figure 22 Nanny with twins in Glacier NP showing a tick-related pattern of coat molt. Photo by Sumio Harada. 32 Figure 23 An example of a nighttime photo, here of an adult nanny along Pooly Creek near Klondike Highway.. 33 Figure 24 One of the only quality mountain goat photos we captured in Kluane National Park. .......................... 34 Figure 25 Dall sheep captured by cameras on Alsek Valley slopes. ........................................................................ 35 Figure 26 Typical scene of shade-use during summer 2018 at the Yukon Wildlife Preserve. ................................ 36 Figure 27 Geronimo (10-year-old billy) standing with one leg in water in late May. ............................................. 37 Figure 28 Billy (4-year-old billy) standing and lying in metal food trough during month of June........................... 37 Figure 29 Nanny swimming in Revelstoke Lake, B.C. Photo by Rod Spider. ........................................................... 38 Figure 30 Oldest and slowest-shedding nanny at Yukon Wildlife Preserve………………………………………………………….39 Figure 31 Nine goats including two nannies with kids at mineral lick at Mount White on July 17......................... 42 Figure 32 Mountain goats on Klondike Highway at c. 4:45 AM on July 23, 2018. Photo by Atsushi Sugimoto. ..... 43

Table 1 Remote camera trapping effort in southern Yukon during summer 2018. ................................................ 13 Table 2 Breakdown of sources of photos in our sample. ........................................................................................ 18 Table 3 Number of photo samples by year of observation. .................................................................................... 18 Table 4 Model effects and likelihood ratio tests. .................................................................................................... 19 Table 5 Correlation coefficients for latitude, elevation, Gross Primary Production. .............................................. 22 Table 6 Model effects and likelihood ratio tests having replaced latitude with elevation. .................................... 22 Table 7 Model effects and likelihood ratio tests incorporating Gross Primary Production. ................................... 23 Table 8 Model effects and likelihood ratio tests showing effects of goat sex, kidding, and day of year................ 26 Table 9 Model effects and likelihood ratio tests on our wild Yukon sample. ......................................................... 27 8

Introduction Understanding how wild animals cope with a warming climate and their risk of heat stress is important for informing conservation planning and management. The physiological costs of thermoregulation in hot weather can be significant and may necessitate improved water access and higher water intake (Boyles et al. 2011) or changes in habitat use. Cold-adapted species are likely to pay the highest costs and possibly be most prone to seasonal mismatches including in coat color (e.g., white snowshoe hares on a brown background) or arrival at calving grounds too late after spring vegetation flush (e.g., caribou) (Post & Forchhammer 2008; Mills et al. 2013, Zimova et al. 2014, Pedersen et al. 2017). Throughout the Pleistocene epoch, the distribution of plants and animals have undergone alteration, including striking changes due to warming temperatures in this century (Parmeson 2006). Among cold-adapted species is the mountain goat (Oreamnos americanus) that inhabits the northern latitudes of North America. From the subfamily of goat-antelopes, mountain goats are more closely related to chamois, goral, serow, and muskox and takin, than to true goats. In addition to their relatedness to other highland or cold-adapted species, we expect mountain goats to be sensitive to temperature. Historically their ranges have diminished due to warming from northern Mexico to Idaho (Festa-Bianchet & Cote 2008), and their thick, twolayered coats, which grow up to four inches long in winter (Foresman 2012), may become a liability in summer. While mountain goats occupy forested habitats in winter, during summer they use high, treeless, windy, steep, rocky slopes and avoid south-facing slopes (Chadwick 2002). Most mammals molt to replace worn out hairs and provide different summer and winter coats (Ryder 1965) including moose, bison, elk, and thinhorn sheep; however, few may be sentinels of warming climes in the ways that mountain goats are (White et al. 2018). The pattern and phenology of mountain goats’ coat molt could indicate how well they are adapting or will adapt to climate change. Molting is a poorly understood life history event and an understudied area of research (Beltran et al. 2018). Molt is triggered by a combination of lengthening daylight and increasing temperature (Murray 1965; Wilkinson 1974), and goats shed their doublelayer coats each year in late spring and summer. While photoperiod – considered to be the principal influence on mammalian furring mechanisms (Mo et al. 2006) – remains unchanged under climate change, globally, temperatures have risen, and in northern environments at two to three times the planetary average (Hansen et al. 1999; White et al. 2018). Phenology – the seasonal timing of life history events – is increasingly relevant in the framework of global change studies (Cohen et al. 2018). We set out to learn about the molt phenology of mountain goats and to examine if mountain goats are shedding their winter pelage earlier in the season in recent years. In the absence of a warming climate, mountain goats have the following opposing selection pressures: (i) to molt sufficiently early so as not to be heat stressed, balanced by (ii) molting sufficiently late that cold exposure is not a problem in spring, while also (iii) molting early enough in spring to put on a new coat in time for fall. Although experimental approaches are the only way to firmly disentangle the relative roles of these pressures, a first cut at understanding if change has occurred across time is to develop empirical bases, a process we detail below. Earlier shedding would imply that mountain goats are responding to changes in temperature or shifts in vegetation phenology, therefore tracking climate change and making them less constrained or prone to heat stress during summer months. If they are not shedding earlier, then this would lend support to 1) photoperiod change being the main driver of molt phenology, and 2) limited plasticity in molting (Zimova et al. 2018). If thermal loading is costly in goats when coats are not shed, we predicted that molt date shifts to earlier in the year. We expected molt to occur latest in females with kids given the energetic demands associated with 9

lactation and kidding that affect body condition. We predicted earlier molt at lower latitudes and elevations. To conduct our research, we involved the public. Citizen science data are an important source of information. Citizen science data have been combined with satellite data to, for example, examine how bird migration (arrival time at breeding grounds) is responding to advancing vegetation green-up dates (Mayor et al. 2017). Although some avian species are adjusting their arrival times, they are still lagging behind the fast-advancing green-up. In our case, we sought evidence to examine whether mountain goats are “keeping pace” with warming through potential changes in the phenology of molt onset. The sources of our data were primarily photographs taken by members of the public across mountain goat ranges. Our data sources were many but especially iNaturalist and CitSci, two online citizen science platforms. We also set out to fill a photo gap in the far northern extent of mountain goat range. Overall, the photos we used represent a combination of citizen photography across time and goat range, and our own photography in the southern Yukon, Canada.

Methods Photo Data Sources I Citizen science and photography There were several criteria for photo submissions. Photos had to be date- and location-stamped, ideally show the whole animal from the side, and image resolution needed to be high enough (a desired value of 300 dpi) to use pixel counts to estimate shed extents. We used such stringent metrics initially to be conservative, although we might have increased sample sizes had we relaxed such criteria. Using these stringent metrics, images were categorized as low (0-1000 pixels), medium (1001-2000), and high (>2000 pixels) resolution regardless of source (e. g. scanned from slide, film, digital camera, camera trap). We noted if the mountain goat in the image was obstructed (by rocks, vegetation, other individuals including kids), and whether multiple images of the same animal were submitted and available. We asked contributors to note whether the date of photo was exact or approximate. To crowd-source photos, we used CitSci.org, iNaturalist, posters hung in public places and delivered to relevant offices, social media including a Facebook page (see Appendices I and IV), as well as word of mouth. We established pages dedicated to the project on both CitSci.org and iNaturalist. While people uploaded photos to CitSci voluntarily, sourcing photos on iNaturalist outside our project page entailed seeking permissions as Creative Commons licenses vary. We therefore contacted each individual whose photo we wished to include in our analysis using direct messaging on the iNaturalist platform. In addition, we contacted professional photographers whose portfolios feature mountain goats (for example, Harada 2008). Given the nature of their work, professionals’ submissions tended not to be public but were submitted via a secure Dropbox folder or by e-mail ([email protected]). There were also members of the public who elected to submit photos to us by e-mail rather than by way of our established citizen science portals because they found this easier or had internet connectivity issues with file uploads. Finally, researchers with sensitive location data (e.g., areas where mountain goats gather) opted to share photos privately.

10

II Remote camera-trapping in southern Yukon We deployed remote cameras between mid-May and early September 2018 to develop sex and latitudinal chronologies of goat molt for contrasts with more southern goat populations. Areas included three locations at which mountain goats are wild, and the Yukon Wildlife Preserve (YWP), which is a captive facility where goats roam large enclosures and are available for public viewing. We had 16 cameras in total for deployment and these were set along active mountain goat trails. A. Observations of individual molt patterns at Yukon Wildlife Preserve The Yukon Wildlife Preserve is established on the Traditional Territories of Kwanlin Dün First Nations and Ta'an Kwäch'än First Nation where it houses two mountain goat herds: one with 14 and the other with six individuals. The larger herd was non-breeding, and the smaller was breeding in summer 2018. Each herd had one adult male (called billies); the larger herd of 14 had one yearling, while the small herd of six consisted of only adults (five females – called nannies – and the one billy). The larger herd had access to cliffs (and thus slightly higher areas) while the smaller herd did not. With YWP staff, we set up two camera traps in each of the two enclosures. In addition, one of us (KN) visited the Preserve 14 times (weekly) between May 18 and August 14, 2018 to take photos of molting individuals, most of which have ear tags or distinct markings. B. Camera trapping at three sites with wild goats As noted above, camera traps were at three locations within the Traditional Territories of Carcross/Tagish First Nation (CTFN) and Champagne and Aishihik First Nations (CAFN): Mount White (CTFN), Montana Mountain (CTFN), and Alsek Valley (CAFN) in Kluane National Park (KNP). We avoided these sites during the mountain goats’ kidding (birthing) periods, from mid-May to mid-June outside of KNP and until 1 July in KNP. While we managed to get cameras up before commencement of the parturition season in Mount White and Montana Mountain, we did not begin camera-trapping in Kluane National Park until after birthing commenced. Each of the three areas is in a distinct eco-region: i.

Mount White is in the Yukon Southern Lakes Ecoregion. Goat trails, and therefore camera traps, were in spruce-dominated forest (Figure 1). This mountain goat population was extirpated in the 1960s and re-established in 1983-1984. The area is estimated to support a minimum of 50 individuals based on a 2017 survey (K. Russell 2018, pers. comm., to KN in Oct.).

ii.

Pooly Canyon above the Windy Arm of Tagish Lake and near Montana Mountain in the Yukon Stikine Highlands Ecoregion is mostly open rocky sub-alpine habitat with patches of forest lower down on slopes and isolated trees higher up (Figure 2). The mountain goat population on Montana Mountain was estimated to number 120 individuals in 2016 (K. Russell 2018, pers. comm., to KN in Oct.).

11

Figure 1 Adult mountain goat male in Mt. White forested area. This camera trap was at 1013 m a.s.l.

Figure 2 Mountain goat male above the Windy Arm of Tagish Lake and Klondike Highway; camera trap at 963 m. 12

iii.

Alsek Valley slopes fall in the Saint Elias Mountains Ecoregion. The area is characterized by alluvial fans, gravel washes, grasslands, poplar and spruce stands. On the slopes above Dezadeash River where we set up camera traps, were patches of wind-stunted vegetation and alpine meadows, and some steep and prominent mountain goat trails such as the one pictured in Figure 3. We used packrafts to cross the river to access these slopes from a camp established near where commercial rafts embark for trips down the Alsek River.

Figure 3 Mountain goat billy climbing a steep mountain goat trail above Dezadeash River, Alsek Valley, Kluane National Park. Camera trap set at 862 m.

Table 1 Remote camera traps (CT) and efforts in southern Yukon during 2018. Site

Elevation range of cameras (m)

YWP Mt. White Montana Mountain Alsek Valley

728-773 854-1013 1018-1039*, then 703-963 673-1013

Dates of camera trapping in summer 2018 May 18-Sept 3 May 16-Aug 19 May 19-Aug 23

No. of CTs

Camera trap effort (days X no. of CTs)

No. of Oreamnos photos

Number of highquality photos processed so far

4 4 3-4

108 x 4 = 432 95 x 4 = 380 95 x 3 = 285

1000s 100+ 100+

58 18 11

July 4-Sept 9

5

67 x 5 = 335

13

2

* Initially, for the first one month, camera traps on Montana Mountain were set high until we learned in mid-June that mountain goats descend the steep slopes regularly to access the lake and other resources lower down.

13

Climate and Remote-Sensing Data Sources Potential sources for spatial remote sensing and climate variables were compiled with respect to our georeferenced photo locations. Remote sensing data from AppEEARS and climate data projects were from ClimateNA though all of these sorts of projected parameters are smoothed relative to specific photo locales and come with limitation (Wang et al. 2016). A total of 35 variables were associated with each record, where feasible, based on the record observation date. Variables were matched as closely as possible to the observation date (generally within a week for remotely sensed data and for the observed and previous month for climate data). A brief description of the variables, and further details on how the processing was performed are provided in Appendix II; R code can be provided upon request to KN. Elevation was the first variable sourced, and was then used to extract other location-based data including maximum temperature, precipitation and radiation in the observation month (from ClimateNA), Normalized Difference Vegetation Index (NDVI) and Gross Primary Production (GPP) (from AppEEARS).

Data Analysis Analysis of shed extent from photographs Pixel counts To estimate the extent of coat shed, we compared pixel counts of shed versus unshed areas of mountain goats’ coats in each photograph using Adobe Photoshop. First, we outlined the whole animal typically using a combination of the quick selection and lasso tools (omitting mountain goats’ hooves, eyes, nose, mouth, horns) and copied the animal, separated from its background, into a new layer (total layer). Second, we duplicated this layer, and in the duplicated file we outlined the shed and unshed areas and cut these as new layers (shed and unshed layers). We selected all pixels in the layers and obtained counts of both shed and unshed areas using the histogram tool. Lastly, to obtain a quick visual, we colored the unshed area red and the shed area black (Figure 4) and saved the entire Photoshop file as well as the red-black image as a PNG file. A tutorial of one of our methods can be found here: https://www.youtube.com/watch?v=h9cWl9Z1Odw.

Statistical models Our description below encumbers goat photos from three areas – 2018 captive in Yukon (YWP), 2018 wild in Yukon, and mostly contributed photos between 1948 and 2018 from throughout the entire distributional range of goats. These latter photos are sourced primarily from members of the public.

14

Figure 4 Our process for estimating shed extent from photos. Steps included cropping the animal from its background, selecting shed and unshed areas and obtaining pixel counts, coloring unshed areas red and shed areas black to obtain quick visuals. Photo by Alison Sheehey, iNaturalist. Yukon Wildlife Preserve data When modelling shedding, we assumed that an individual’s body is divided into N sections and we predicted the number of sections on day t that are classified as being shed. For our analyses, we assume N = 50; however, our general conclusions are robust to changes in N. A general linear mixed-effects model was used to fit the data. Our linear predictors of molt were: day of year (t), animal sex (x1; 0 = male, 1 = female), and presence of young of the year (x2; 0 = no, 1 = yes). To help with model fitting we rescaled time t so t = 0 corresponds to January 1 and t = 1 corresponds to December 31. Hence, reporting of time appears as a fraction for day number 1 and 365. In total, we had data on 14 animals with two to six observations per animal. A random factor was used to model repeated animal observations, and another random factor was used to model over-dispersion in the data with respect to the binomial distribution. We used the glmer function provided by the package lme4 in R (R Core Team 2018) to fit the linear model, which forces the parameterization of the model. Specifically, if pt is the proportion of blocks shed on day t, then:

15

logit pt = β0 + β1x1 + β2x2 + β3t A different parameterization incorporates the day when half the coat has been shed, and the expected days change in this date between males and females and between the two YWP groups. Ideally, the model would have the following parameterization: logit pt = γ(t − (α0 + α1x1 + α2x2)) Here, γ describes the rate of shedding, 0 < α0 < 1 is the date (fraction of the year) when shedding is intermediate (50%) for males, α1 is the time (years) that females delay shedding relative to males, and α2 is the additional time (years) that females with kids delay shedding relative to females without kids. Equating the two equations implies: γ = β3 and αi = −βi/β3 for i = 0, 1, 2. Wild Yukon data Like above with the Yukon Wildlife Preserve (YWP) data, we fit a linear mixed-effects model assuming a binomial distribution. The only random effect was at the observation level and was used to model over-dispersion. Public-sourced data Finally, we analyzed the public-sourced photos. We ignored repeated samples from YWP as they were not independent. Our analysis included latitude, elevation, and Gross Primary Production, which were z-transformed to help with fitting. Sex was also included. There were not enough data to incorporate the presence of young of the year. We performed likelihood ratio tests (LRTs) using the drop1 function in R to test the null hypothesis that the data were generated by the full model without the model term at the start of each row, provided that the predictor variables (fixed effects) included in models were not highly correlated (which we tested; Appendix III). Elevation, climate and remote-sensing data Climate data were only available for observations up to 2014 (see Appendix II for details about these limitations), while remote-sensed data were not time-limited and available for the majority of our data samples. We elected to use elevation and remote-sensed data on Gross Primary Production (GPP) as fixed factors in our models. We used GPP and not temperature for two reasons. First, temperature data were only available until 2014. Second, temperature may influence coat molt indirectly through its effects on vegetation (Ryder, 1971), though this assumption needs firm testing. A caution on elevation Elevation was not always provided by photo contributors, although we asked contributors if their observation was above, at, or below treeline as part of their data uploads. We therefore examined elevation on the basis of the latitude/longitude coordinates provided. As an aside, elevation associated with an observation is not necessarily representative of the elevation range an animal normally uses; it indicates only where the animal was for a snapshot. While females have high site fidelity relative to males, nursery groups are highly mobile in summer whereas adult males remain within a small area (Festa-Bianchet & Cote 2008). Locations of summer photos may therefore be less representative of the range of elevation used by females than by males. Additionally, in five cases data were included from zoo populations (Alaska Zoo, Oregon Zoo, and Woodland Park Zoo in Seattle, Washington) with elevations less than 300 meters.

16

Results Photo Sample Our sample comprised 516 photos (photos which qualified for analysis and which we hand-processed in time for this report – late October 2018). The sample represents all of mountain goat range except for Northwest Territories and Nevada (Figure 5). Of these 516 photos, 72% were crowd-sourced from citizen scientists (nonresearchers) while 17% came from our summer 2018 fieldwork in the southern Yukon (96 in total from the Yukon including photos from previous years submitted by members of the public and 89 photos captured by us; Table 2). The most photographed mountain goats, according to our sample, are those in Glacier National Park, Montana, USA (see the table embedded in Figure 5).

Figure 5 Map showing mountain goat range and source locations of our photos.

17

Table 2 Sources of photos in our sample. Source CitSci iNaturalist Professional photographers Our photos at YWP Researchers Our camera traps By e-mail to K. Nowak Professional societies

No. photos 153 122 69 58 55 31 15 13

Additional details on source Our designated project web portal on CitSci.org Via iNaturalist on the whole and our designated project page Professional photographers whom we contacted & who contributed Our photos taken on visits to the Yukon Wildlife Preserve Caw Ridge, Glacier NP, Kluane NP researchers and wardens Our camera-trapping in southern Yukon Citizen scientists who opted to send photos by e-mail B.C. Mountain Goat Society, The Wilderness Society, Summit Post

Table 3 Number of photo samples by year of observation.

The majority of our photos were from post-2008 and especially from 2018 (Table 3), as people engaged with the project over the course of summer 2018 after learning about it. Our oldest photo was from July 1948 from Gunsight Pass, Glacier National Park, Montana. This photo was submitted by Chris Peterson, Managing Editor of Hungry Horse News in Columbia Falls, Montana. Our experience with archives with which we engaged including those of the U.S. National Park Service, Whyte Museum of the Canadian Rockies (Banff, Alberta), and Yukon Archives was that older photos tended not to be dated exactly or even approximately. It is possible that newspaper archives may prove to be superior to other archives in supplying photos with dates as we continue to attempt to source older photos. Four latitudinal data clusters emerged: Colorado, Montana, Alberta and Yukon (Figure 6). The first two, Colorado and Montana, were driven by photo contributions from the public and professional photographers, while the latter two (Caw Ridge and southern Yukon) were driven by researchers (including us). The inverse relationship between elevation and latitude is evident in Figure 6 and is tested in the next section.

18

Figure 6 The latitude and elevation associated with our 516 photo samples.

Citizen Science Data We did not find evidence of a long-term temporal effect on mountain goat molt (Table 4, p = 0.543 for “Year”; Figure 7), nor was a relationship expected due to the limited sample (N = 10) for the period between 1948-1999. By contrast, day of year, goat sex, and latitude affected the proportion of coat molted (Table 4). Recall from the Methods that each row with an LRT value (Table 4) corresponds to testing the null hypothesis that the data were generated by the full model without the model term at the start of the row.

Table 4 Model effects and likelihood ratio tests. Factor Day of year Year Goat sex Latitude (z-transformed)

LRT 430.81 0.37 31.23 17.09

df 1 1 2 1

p-value