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Ecology, management and conservation in natural and modified habitats
RESEARCH ARTICLE

Improving the efficiency of aerial surveys for monitoring North American beaver population dynamics

Madeline Kenyon A , Catherine C. Dennison https://orcid.org/0000-0002-6966-7837 B and Viorel D. Popescu https://orcid.org/0000-0001-7138-0774 A C D *
+ Author Affiliations
- Author Affiliations

A Department of Biological Sciences, Ohio University, 107 Irvine Hall, Athens, OH 45701, USA.

B Ohio Department of Natural Resources, Division of Wildlife, 2045 Morse Road, Columbus, OH, USA.

C Department of Ecology, Evolution and Environmental Biology, Columbia University, Schermerhorn Extension 10th Floor, 1200 Amsterdam Avenue, New York, NY 10027, USA.

D Centre for Environmental Research, University of Bucharest, 1 N. Balcescu Boulevard, Bucharest, Romania.

* Correspondence to: viorelpopescu@gmail.com

Handling Editor: Catarina Campos Ferreira

Wildlife Research 51, WR23105 https://doi.org/10.1071/WR23105
Submitted: 31 August 2023  Accepted: 10 September 2024  Published: 14 October 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing

Abstract

Context

The North American beaver (Castor canadensis) was extirpated from much of its range in the US in the 1800s due to fur trapping and change in land use. However, the species has recolonised much of its former range, including the US state of Ohio. Since 2013, the Ohio Division of Wildlife (ODOW) has monitored trends in beaver colony density via aerial surveys of 40 km × 40 km plots classified as low, medium, or high suitability based on the amount of wetland. Nonetheless, the current classification system may miss important correlates of beaver colony density.

Aims

Our study aimed to (1) identify predictors of beaver colony density (number of colonies inferred from aerial counts of lodges) across Ohio, and (2) develop a model-based classification system to improve the efficacy of monitoring efforts.

Methods

To predict beaver colony density in Ohio we used an aerial survey dataset of 54 plots (40 km × 40 km) collected by ODOW annually between 2013 and 2020, along with a suite of environmental, anthropogenic, and climate variables in a mixed effects modelling framework.

Key results

Beaver colony density was positively associated with wetland and reclaimed surface mine areas and inversely associated with the proportion of agricultural lands. There was a negative interaction between wetland and surface mines; in general, beaver colony density increased with wetland and surface mine area. However, in plots with wetland area >1000 ha, beaver colony density was weakly negatively associated with surface mine area. Using median and interquartile ranges of model-averaged predicted beaver colony density, we developed a new classification of low, moderate and high suitability for both the survey plots and the entire state of Ohio. We found that eastern Ohio had high suitability, while the central and western parts of the state had lower suitability for C. canadensis.

Conclusions

Our approach to identifying predictors for beaver colony density at broad spatial scales highlights the importance of reclaimed surface mines and wetlands for beaver populations, while the model-based habitat classification provides ODOW additional information for monitoring and beaver management decisions.

Implications

Improved C. canadensis monitoring at the landscape scale using habitat classifications that consider local conditions can both improve annual survey cost-effectiveness and facilitate the sustainable management of this recovering species.

Keywords: aerial survey, beaver, conservation, landcover, recovering population, surface mine, wetland, wildlife monitoring.

References

Arnold TW, Alisauskas RT, Sedinger JS (2020) A meta-analysis of band reporting probabilities for North American waterfowl. The Journal of Wildlife Management 84, 534-541.
| Crossref | Google Scholar |

Barthelmess EL (2014) Spatial distribution of road-kills and factors influencing road mortality for mammals in Northern New York State. Biodiversity and Conservation 23, 2491-2514.
| Crossref | Google Scholar |

Barton K (2020) Package ‘MuMIn’ - Multi-Model Inference. Available at https://cran.r-project.org/web/packages/MuMIn/index.html

Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67, 1-48.
| Crossref | Google Scholar |

Beale MM, Boyce MS (2020) Mine reclamation enhances habitats for wild ungulates in west-central Alberta. Restoration Ecology 28, 828-840.
| Crossref | Google Scholar |

Beck JL, Dauwalter DC, Gerow KG, Hayward GD (2010) Design to monitor trend in abundance and presence of American beaver (Castor canadensis) at the national forest scale. Environmental Monitoring and Assessment 164, 463-479.
| Crossref | Google Scholar | PubMed |

Burnham KP, Anderson DR (2002) ‘Model selection nd multimodel inference. A practical information-theoretic approach.’ 2nd edn (Springer)

Campbell RD, Rosell F, Newman C, Macdonald DW (2017) Age-related changes in somatic condition and reproduction in the Eurasian beaver: resource history influences onset of reproductive senescence. PLoS ONE 12, e0187484.
| Crossref | Google Scholar | PubMed |

Chapman FB (1949) The beaver in Ohio. Journal of Mammalogy 30, 174-179.
| Crossref | Google Scholar |

Chucholl F, Fiolka F, Segelbacher G, Epp LS (2021) eDNA detection of native and invasive crayfish species allows for year-round monitoring and large-scale screening of lotic systems. Frontiers in Environmental Science 9, 639380.
| Crossref | Google Scholar |

Cove MV, Kays R, Bontrager H, Bresnan C, Lasky M, Frerichs T, Klann R, Lee TE, Jr, Crockett SC, Crupi AP, et al. (2021) SNAPSHOT USA 2019: a coordinated national camera trap survey of the United States. Ecology 102, e03353.
| Crossref | Google Scholar |

Curtis PD, Jensen PG (2004) Habitat features affecting beaver occupancy along roadsides in New York state. Journal of Wildlife Management 68, 278-287.
| Crossref | Google Scholar |

Doucet CM, Fryxell JM (1993) The effect of nutritional quality on forage preference by beavers. Oikos 67, 201-208.
| Crossref | Google Scholar |

Ellington EH, Lewis KP, Koen EL, Vander Wal E (2020) Divergent estimates of herd-wide caribou calf survival: ecological factors and methodological biases. Ecology and Evolution 10(15), 8476-8505.
| Crossref | Google Scholar | PubMed |

Fryxell JM, Packer C, McCann K, Solberg EJ, Sæther B-E (2010) Resource management cycles and the sustainability of harvested wildlife populations. Science 328, 903-906.
| Crossref | Google Scholar | PubMed |

Gehrt SD, Hubert GF, Ellis JA (2006) Extrinsic effects on long-term population trends of Virginia opossums and striped skunks at a large spatial scale. The American Midland Naturalist 155, 168-180.
| Crossref | Google Scholar |

Gilland KE, McCarthy BC (2014) Microtopography influences early successional plant communities on experimental coal surface mine land reclamation. Restoration Ecology 22, 232-239.
| Crossref | Google Scholar |

Gregory RD (2000) Development of breeding bird monitoring in the United Kingdom and adopting its principles elsewhere. The Ring 22, 35-44.
| Google Scholar |

Johnston CA, Windels SK (2015) Using beaver works to estimate colony activity in boreal landscapes. The Journal of Wildlife Management 79, 1072-1080.
| Crossref | Google Scholar |

Kaminski DJ, Harms TM, Coffey JM (2019) Using spotlight observations to predict resource selection and abundance for white-tailed deer. The Journal of Wildlife Management 83, 1565-1580.
| Crossref | Google Scholar |

Kimball BA, Perry KR (2008) Manipulating beaver (Castor canadensis) feeding responses to invasive tamarisk (Tamarix spp.). Journal of Chemical Ecology 34, 1050-1056.
| Crossref | Google Scholar | PubMed |

Knudsen LL, Struthers PH (1953) Stripmine reclamation research in Ohio. The Ohio Journal of Science 56, 351-355.
| Google Scholar |

Lautenbach JM, Stricker N, Ervin M, Hershner A, Harris R, Smith C (2020) Woody vegetation removal benefits grassland birds on reclaimed surface mines. Journal of Fish and Wildlife Management 11, 89-98.
| Crossref | Google Scholar |

Lindenmayer DB, Likens GE (2009) Adaptive monitoring: a new paradigm for long-term research and monitoring. Trends in Ecology & Evolution 24, 482-486.
| Crossref | Google Scholar | PubMed |

Lituma CM, Cox JJ, Spear SF, Edwards JW, De La Cruz JL, Muller LI, Ford WM (2021) Terrestrial wildlife in the post-mined appalachian landscape: status and opportunities. In ‘Appalachia’s coal-mined landscapes: resources and communities in a new energy era’. (Eds CE Zipper, J Skousen) pp. 135–166. (Springer International Publishing: Cham) doi:10.1007/978-3-030-57780-3_6

Lüdecke D (2018) ggeffects: tidy data frames of marginal effects from regression models. Journal of Open Source Software 3, 772.
| Crossref | Google Scholar |

Lyons JE, Runge MC, Laskowski HP, Kendall WL (2008) Monitoring in the context of structured decision-making and adaptive management. The Journal of Wildlife Management 72, 1683-1692.
| Crossref | Google Scholar |

McLaren AAD, Newton EJ, Silver A, Allan MR, Middel KR, Pond BA, Patterson BR (2022) Too many to count: using orthophotography to census an unharvested beaver (Castor canadensis) population in Ontario. Ecosphere 13, e4185.
| Crossref | Google Scholar |

Mitsch WJ, Wise KM (1998) Water quality, fate of metals, and predictive model validation of a constructed wetland treating acid mine drainage. Water Research 32, 1888-1900.
| Crossref | Google Scholar |

Naiman RJ, Melillo JM, Hobbie JE (1986) Ecosystem alteation of boreal forest streams by beaver (Castor canadensis). Ecology 67, 1254-1269.
| Crossref | Google Scholar |

Oakley KL, Thomas LP, Fancy SG (2003) Guidelines for long-term monitoring protocols. Wildlife Society Bulletin 31, 1000-1003.
| Google Scholar |

Peterson MN, Riley SJ, Busch L, Liu J (2007) Reconciling wildlife management’s conflicted purpose with a land community worldview. The Journal of Wildlife Management 71, 2499-2506.
| Crossref | Google Scholar |

Prange IS, Rose C (2020) Investigating uneven recovery of repatriated bobcats (Lynx rufus) in a mined landscape: space use, habitat use and condition in coal country. Wildlife Research 47, 77-88.
| Crossref | Google Scholar |

R Core Team (2021) ‘R: a language and environment for statistical computing.’ (R Core Team)

Robel RJ, Fox LB (1993) Comparison of aerial and ground survey techniques to determine beaver colony densities in Kansas. The Southwestern Naturalist 38, 357-361.
| Crossref | Google Scholar |

Robinson RA, Morrison CA, Baillie SR (2014) Integrating demographic data: towards a framework for monitoring wildlife populations at large spatial scales. Methods in Ecology and Evolution 5, 1361-1372.
| Crossref | Google Scholar |

Rousseau JS, Alexander JD, Betts MG (2020) Using continental-scale bird banding data to estimate demographic migratory patterns for Rufous Hummingbird (Selasphorus rufus). Avian Conservation and Ecology 15, 2.
| Crossref | Google Scholar |

Sauer JR, Pardieck KL, Ziolkowski DJ, Jr, Smith AC, Hudson M-AR, Rodriguez V, Berlanga H, Niven DK, Link WA (2017) The first 50 years of the North American breeding bird survey. The Condor: Ornithological Applications 119, 576-593.
| Crossref | Google Scholar |

Scamardo JE, Marshall S, Wohl E (2022) Estimating widespread beaver dam loss: habitat decline and surface storage loss at a regional scale. Ecosphere 13, e3962.
| Crossref | Google Scholar |

Severud WJ, Windels SK, Belant JL, Bruggink JG (2013) The role of forage availability on diet choice and body condition in American beavers (Castor canadensis). Mammalian Biology 78, 87-93.
| Crossref | Google Scholar |

Swab RM, Lorenz N, Byrd S, Dick R (2017) Native vegetation in reclamation: improving habitat and ecosystem function through using prairie species in mine land reclamation. Ecological Engineering 108, 525-536.
| Crossref | Google Scholar |

Walker DA, Webber PJ, Walker MD, Lederer ND, Meehan RH, Nordstrand EA (1986) Use of geobotanical maps and automated mapping techniques to examine cumulative impacts in the Prudhoe Bay Oilfield, Alaska. Environmental Conservation 13, 149-160.
| Crossref | Google Scholar |

Weir LA, Royle JA, Gazenski KD, Villena O (2014) Northeast regional and state trends in anuran occupancy from calling survey data (2001–2011) from the North American Amphibian Monitoring Program. Herpetological Conservation and Biology 9, 223-245.
| Google Scholar |

Wickham H (2016) ‘ggplot2: elegant graphics for data analysis.’ (Springer Verlag: New York)

Wright JP, Jones CG, Flecker AS (2002) An ecosystem engineer, the beaver, increases species richness at the landscape scale. Oecologia 132, 96-101.
| Crossref | Google Scholar | PubMed |