Challenges and opportunities for estimating abundance of a low‐density moose population

Abstract

Monitoring large herbivores across their core range has been readily accomplished using aerial surveys and traditional distance sampling. But for peripheral populations, where individuals may occur in patchy, low‐density populations, precise estimation of population size and trend remains logistically and statistically challenging. For moose (Alces alces) along their southern range margin in northern New York, USA, we sought robust estimates of moose distribution, abundance, and population trend (2016–2019) using a combination of aerial surveys (line transect distance‐sampling), repeated surveys in areas where moose were known to occur to boost the number of detections, and density surface modeling (DSM) with spatial covariates. We achieved a precise estimate of density (95% CI = 0.00–0.29 moose/km2) for this small population (656 moose, 95% CI = 501–859), which was patchily distributed across a large and heavily forested region (the 24,280‐km2 Adirondack Park). Local moose abundance was positively related to active timber management, elevation, and snow cover, and negatively related to large bodies of water. As expected, moose abundance in this peripheral population was low relative to its core range in other northern forest states. Yet, in areas where abundance was greatest, moose densities in New York approached those where epizootics of winter tick (Dermacentor albipictus) have been reported, underscoring the need for effective and efficient monitoring. By incorporating autocorrelation in observations and landscape covariates, DSM provided spatially explicit estimates of moose density with greater precision and no additional field effort over traditional distance sampling. Combined with repeated surveys of areas with known moose occurrence to achieve viable sample sizes, DSM is a useful tool for effectively monitoring low density and patchy populations.

A model-based estimate of winter distribution and abundance of white-tailed deer in the Adirondack Park

“…During 2015–2019, monitoring of the low-density moose population in the Park was undertaken via aerial distance sampling [ 21 ]. Given the interest in how white-tailed deer may negatively influence moose abundance through competition and disease, deer were also recorded in a winter aerial survey to gain a contemporary, non-harvest-based assessment of their distribution and abundance and compare spatial patterns in moose-deer co-occurrence across the Park.…”

“…Total white-tailed deer abundance for the Park was then obtained by summing predicted deer abundance across all relevant cells. To correlate winter densities of white-tailed deer and moose in the Park, we used a Spearman rank-order correlation to compare our deer density surface map to a moose density surface map previously derived from the same aerial surveys [ 21 ].…”

“…In the Adirondack Park region (hereafter “the Park”) of northern New York, white-tailed deer and moose co-occur along an ecotonal temperate-boreal forest region where deer are expanding their range [ 20 ] and moose exist as a stable, low-density population [ 21 ]. Mild winters associated with climate change have allowed white-tailed deer to winter in land cover that formerly could only sustain them in the summer [ 3 , 4 ].…”

“…White-tailed deer in the Park respond positively to anthropogenic landscape features by concentrating their winter yards along corridors of residential development and roads for shelter, forage, and movement corridors during winter [ 12 ]. Moose in the Park are non-migratory and most of the low-density population inhabits the northern region where large-scale forestry operations provide concentrated patches of regenerative forest [ 21 ]. Both species rely on timber harvest forest regeneration for forage and ongoing timber management in the northern areas of the Park has the potential to further concentrate moose and white-tailed deer in that region.…”

“…The DSM approach uses the spatial variability of white-tailed deer abundance to model abundance at finer resolutions than CDS alone, provide additional insight into species-environment relationships within survey areas, and predict abundance in neighboring areas that were not surveyed. Our secondary objective was to compare winter distribution and density of white-tailed deer in the Park to those previously estimated for moose [ 21 ].…”