# Testing GIS-generated Least-cost Path Predictions for Martes pennanti (Fisher) and its Application for Identifying Mammalian Road-crossings in Northern New Hampshire

> Where should a wildlife tunnel or overpass go? In northern New Hampshire, certified trackers recorded over 7000 animal tracks crossing or approaching two roads in one winter and spring, and the researchers used the fisher’s crossings to map its easiest travel routes. The predicted corridors held up for fishers and bobcats, and drew more kinds of mammals than other stretches of road, though not more black bears.

Summary by Kersey Lawrence, Original Wisdom. Updated 9 November 2026. Page: https://www.originalwisdom.com/tracking-literature/2012-leoniak-barnum-testing-gis-generated-least/

## Citation and access

- **Authors:** George Leoniak, Sarah Barnum, Jonathan L. Atwood, Kurt Rinehart and Mark Elbroch
- **Year published:** 2012
- **Journal or source:** Northeastern Naturalist
- **Full citation:** Leoniak, G., Barnum, S., Atwood, J. L., Rinehart, K. and Elbroch, M. 2012. Testing GIS-generated least-cost path predictions for Martes pennanti (Fisher) and its application for identifying mammalian road-crossings in northern New Hampshire. Northeastern Naturalist 19(2): 147–156.
- **DOI:** 10.1656/045.019.0201
- **Access status:** Publisher link only
- **Where to find it:** https://doi.org/10.1656/045.019.0201

## Tags

- **Species:** Mammals > American black bear; Mammals > American marten; Mammals > American mink; Mammals > Bobcat; Mammals > Canada lynx; Mammals > Coyote; Mammals > Fisher; Mammals > Grey fox; Mammals > Long-tailed weasel; Mammals > Moose; Mammals > Muskrat; Mammals > North American beaver; Mammals > North American porcupine; Mammals > North American river otter; Mammals > Raccoon; Mammals > Red fox; Mammals > Snowshoe hare; Mammals > Stoat; Mammals > Striped skunk; Mammals > Virginia opossum; Mammals > White-tailed deer
- **Region:** North America > United States
- **Tracking:** Snow tracks; Track counts; Tracking; Tracks; Tracks and signs identification
- **Behaviour and ecology:** Behaviour; Connectivity; Habitat selection; Movement; Road crossings; Wildlife corridors
- **Methods and technology:** CyberTracker; CyberTracker software; CyberTracker Tracker Certification; Geographic Information System (GIS); Global Positioning System (GPS); Road transects
- **Data type:** Existing data; Modelling or theoretical
- **Observer qualification:** Certified trackers
- **Discipline:** Behaviour and ecology; Wildlife tracking
- **Year published:** 2012

## Detailed keywords

- **Species:** Studied: fisher (Martes pennanti), the focal species of the corridor model, plus bobcat (Lynx rufus) and American black bear (Ursus americanus). Also recorded crossing or approaching the roads (21 mammal species, 7099 tracks in all): moose (Alces alces), red fox (Vulpes vulpes), white-tailed deer (Odocoileus virginianus), coyote (Canis latrans), gray fox (Urocyon cinereoargenteus), snowshoe hare (Lepus americanus), mink (Mustela vison), long-tail weasel (Mustela frenata), raccoon (Procyon lotor), ermine (Mustela erminea), striped skunk (Mephitis mephitis), American marten (Martes americana), porcupine (Erethizon dorsatum), beaver (Castor canadensis), muskrat (Ondatra zibethicus), river otter (Lontra canadensis), Canada lynx (Lynx canadensis) and Virginia opossum (Didelphis virginiana). Left out of the analysis: tree squirrels (subfamily Sciurinae), domestic animals, wild turkey (Meleagris gallopavo) and ruffed grouse (Bonasa umbellus). One unknown fox track was counted as red fox, and four unknown weasel tracks as ermine.
- **Location and habitat:** Route 2 and Route 115 in the towns of Jefferson and Randolph, northern New Hampshire, United States, in valley bottoms of the White Mountain region. The study area extended 2 km either side of the roads (111.6 km²): mostly national forest, with patches of low-density development, pasture and hay fields. Land cover included conifers (white and red pine, spruce and fir, hemlock), mixed forest, hardwoods (beech and oak, paper birch and aspen, others), wetlands, hay and pasture, open water and developed land.
- **Topics:** least-cost path (LCP) corridor models; testing corridor predictions with field data; wildlife road crossings; road ecology; habitat fragmentation; focal species for corridor design; fisher habitat selection (conifers and mixed forest preferred, hay and pasture avoided); habitat suitability and resistance maps; land-cover map accuracy; multi-species use of single-species corridors; mitigation sites such as wildlife bridges, tunnels and overpasses.
- **Statistics or models:** Least-cost corridors built in ArcGIS 9.2 with the CorridorDesigner extension (weighted arithmetic mean algorithm), on a habitat suitability raster from the 2001 New Hampshire Land Cover Assessment (30 m by 30 m pixels), using 125 m buffers around fisher crossings. One-tailed Fisher’s exact tests (detection versus non-detection inside and outside corridors); Wilcoxon rank-sum tests (number of species per road section); software JMP 7. Inconsistencies in the paper: Route 2 is given as 17.9 mi/27.9 km (17.9 miles is about 28.8 km), and Route 115 as 5.9 mi/9.8 km (5.9 miles is about 9.5 km). The text calls the land-cover scores of 100 and 1 “resistance values”, but the methods explain that a score of 100 means zero resistance and 1 means 99% resistance, so these are suitability scores.
- **Data type:** Existing data: track surveys collected by New Hampshire Audubon (Barnum et al. 2007) on 51 days from 5 December 2005 to 25 May 2006, shared with the authors. Modelling: least-cost corridor model tested against these data.

## Synopsis

### 1. What they did

Roads can cut animals off from the places they need, split populations, and kill animals in collisions. To decide where to put wildlife bridges, tunnels or overpasses, biologists often use computer maps to predict the “least-cost path” (LCP): the route an animal would find easiest to travel. But few studies have checked these predictions against what animals actually do.

The authors tested an LCP model for the fisher, a wide-ranging mesocarnivore that is sensitive to habitat fragmentation, along two roads in northern New Hampshire:

- Field data. Certified track and sign observers surveyed the roads for tracks on 51 days from December 2005 to May 2006, recording 7151 places where wildlife crossed or approached the roads.

- Building the model. They compared the land cover within 125 m of the 117 places where fishers crossed the road with the land cover available along the roads as a whole. Each land-cover type was scored by how much fishers seemed to prefer or avoid it, guided also by the authors’ expert opinion and published studies of fisher habitat. A Geographic Information System (GIS) then drew the easiest routes for fishers between three blocks of habitat on either side of the roads.

- Testing the model. They compared tracks on 11 road sections inside the predicted corridors with tracks on 11 randomly placed road sections outside them. They asked whether fishers, bobcats and American black bears were found more often inside the corridors, and whether more species used them.

### 2. What they found

Fisher land-cover preferences

- Fishers crossed more often where the roadside had conifers (10.3% of use, 6.1% available) or mixed forest (13.4% used, 8.7% available), and less often where it had hay or pasture (2.9% used, 11.7% available).

- Hardwoods and other cleared land were used in proportion to what was available. Wetlands were used slightly more than available.

Tracks recorded

- The analysis used 7099 tracks of 21 mammal species. The most common were moose (2590), red fox (1862), white-tailed deer (1157) and coyote (662). Fishers made 117 crossings, bobcats 32 and black bears 42.

- 1366 tracks (19%) fell within the corridors, including 35 of the fisher tracks (30%) and 14 of the bobcat tracks (44%).

Inside versus outside the corridors

- Fishers were found on 8 of the 11 road sections inside the corridors, and on 2 of the 11 outside them.

- Bobcats were found on 6 of the 11 sections inside, and on none outside.

- Black bears were found on 2 sections inside and 2 outside: no difference.

- Road sections inside the corridors had more species (an average of 7.36) than those outside (4.45).

- 15 of the 21 mammal species were found within the corridors.

### 3. What they concluded

- The fisher corridor model, tested with field tracking data, successfully identified roadside corridors for fishers and bobcats, but not for black bears.

- Corridors designed for the fisher also picked out places used by many other mammals. This may be because the fisher is sensitive to habitat fragmentation, or because the roadside cover fishers choose marks movement routes for a range of wildlife. Fishers and other mammals may also travel in conifers in winter, where snow is shallower and its crust harder, and bobcats may follow prey there.

- Black bears were dormant for most of the study and were sampled only briefly in spring. They may use the corridors more in other seasons, and the authors cannot say whether the patterns hold year-round.

- Uncertainties remain: the 2001 land-cover map may not match the land cover of 2005 and 2006; land cover was the only factor in the model; and terrain, which other studies use, should be explored.

- Multi-species corridors may prove better than single-species ones, but this study shows single-species LCP models are still a valuable tool in conservation planning.

### 4. How tracking was or was not used

Tracking was the basis of both building and testing the model. Observers drove slowly (under 16 km/h, 10 mph) along the roads, stopping often to record the species whenever tracks were within 10 m of the road. Surveys were done 48 to 72 hours after new snow, and the same observers repeated them, so old tracks were not counted twice. Bridges and culverts were checked every session. In spring, after the snow had gone, observers walked the whole road system twice a week to record black bear and moose tracks, erasing each track once recorded. No trailing is reported. The authors note that, unlike radio-telemetry, their snow-tracking method recorded fishers at the predicted corridor locations and also gave information on the other species crossing the roads.

### 5. Tracker or observer certification

The track and sign observers were tested in the field and certified by CyberTracker Conservation (citing Elbroch et al., then in press, and Evans et al. 2009). The level of certification and the number of observers are not given. Rose Graves is thanked for help with data collection.

### 6. Other technologies, skills or innovations

- CyberTracker data collection software on Global Positioning System (GPS) data loggers (TDS Recon and Trimble GeoExplorer) to record where tracks were found.

- The CorridorDesigner extension for ArcGIS 9.2 to draw least-cost corridors from a land-cover-based habitat suitability map.

### 7. Implications for conservation (from the paper)

- LCP analysis is widely used to choose places for road crossing structures, but its predictions need testing with real data. Here roadside track data confirmed the model for fishers and bobcats.

- Roadside habitats chosen by fishers may help identify and protect movement corridors for many species.

- The model could be tested further by predicting corridors elsewhere in northern New England and checking them with track data on several species.

### 8. Implications for tracking (Original Wisdom's observations)

_These are Original Wisdom's observations, not the authors'._

- Road-based track surveys by certified observers gave enough reliable data, in a single winter and spring, both to build a corridor model and to test it, for 21 mammal species at once.

- The study shows a simple way to check computer-drawn wildlife corridors on the ground: compare tracks crossing roads inside and outside the predicted routes.

## Cite this summary

Lawrence, K. 2026. Summary of: Testing GIS-generated Least-cost Path Predictions for Martes pennanti (Fisher) and its Application for Identifying Mammalian Road-crossings in Northern New Hampshire (Leoniak and Barnum et al., 2012). Original Wisdom, Tracking Literature. https://www.originalwisdom.com/tracking-literature/2012-leoniak-barnum-testing-gis-generated-least/