Invasive axis deer and wild boar in a protected area in Argentina, controlled hunting, and Taylor’s law
R. E. Gürtler
A B F and J. E. Cohen C D E F
+ Author Affiliations
- Author Affiliations
A Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales, Laboratory of Eco-Epidemiology, Ciudad Universitaria, C1428EHA, Buenos Aires, Argentina.
B Consejo Nacional de Investigaciones Científicas y Técnicas-Universidad de Buenos Aires. Instituto de Ecología, Genética y Evolución de Buenos Aires (IEGEBA), Ciudad Universitaria, C1428EHA, Buenos Aires, Argentina.
C Laboratory of Populations, Rockefeller University, New York, NY 10065, USA.
D Earth Institute and Department of Statistics, Columbia University, New York, NY 10027, USA.
E Department of Statistics, University of Chicago, Chicago, IL 60637, USA.
F Corresponding authors. Email: gurtler@ege.fcen.uba.ar, cohen@rockefeller.edu
Wildlife Research 49(2) 111-128 https://doi.org/10.1071/WR20119
Submitted: 23 July 2020 Accepted: 11 June 2021 Published: 15 October 2021
Journal Compilation © CSIRO 2022 Open Access CC BY-NC-ND
Abstract
Context: Spatial and temporal variability in population density tends to increase with an increasing mean density, as widely documented by Taylor’s law (TL) of fluctuation scaling. A management program based on local hunters has been used to control invasive wild boar and axis deer in a protected area of north-eastern Argentina since 2006.
Aim: We determine the effects of species (boar or deer), hunting shift (diurnal, overnight), human disturbance (by comparing one section open for public use, one not) and time scale (one- versus three-month periods) on the values of the parameters of TL, and consider both its spatial and temporal forms.
Methods: Park management collected data on the hunting efforts and harvest of 6104 hunting parties shooting from elevated blinds from 2006 to 2015. The log-transformed sample means and variances of four indices of relative abundance were computed for each period and blind, and analysed through least-squars linear regression and ANCOVA.
Key results: Axis deer satisfied the spatial TL by all four indices, but wild boar had a significantly non-linear relationship for crude catch per unit effort (CP–UE) only. In the spatial TL, the slope b did not deviate significantly from 1 when using crude or standardised catch per hunting-party session or standardised CPUE, but b was substantially >1 for crude CPUE in both species (range, 1.307–1.434). Human disturbance, hunting shift, and time scale did not significantly modify the parameters of the spatial TL, except in two cases. All metrics at identified blinds over consecutive trimesters confirmed the temporal TL. Wild boar crude catch was 43% greater in the restricted zone of greater conservation value, whereas axis deer catch was 60% greater in the public-use zone.
Conclusions: With rare exceptions, TL describes well the mean and variance of four metrics of abundance of wild boar and axis deer under sustained hunting pressure. This paper may be the first to demonstrate the connection of TL with any aspect of vertebrate pest control.
Implications: TL identifies key zones with a high mean and high variance of ungulate density for targeted control, and can be used to attain fixed-precision estimates of abundance through sequential sampling.
Keywords: invasive exotic species, abundance, wildlife management, population dynamics, ungulates, Taylor’s law.
References
Ballari, S. A., Cuevas, M. F., Ojeda, R. A., and Navarro, J. L. (2015). Diet of wild boar (Sus scrofa) in a protected area of Argentina: the importance of baiting. Mammal Research 60, 81–87.| Diet of wild boar (Sus scrofa) in a protected area of Argentina: the importance of baiting.Crossref | GoogleScholarGoogle Scholar |
Barnes, R. F., Beardsley, K., Michelmore, F., Barnes, K. L., Alers, M. P., and Blom, A. (1997). Estimating forest elephant numbers with dung counts and a geographic information system. The Journal of Wildlife Management 61, 1384–1393.
| Estimating forest elephant numbers with dung counts and a geographic information system.Crossref | GoogleScholarGoogle Scholar |
Barrios-García, M. N., and Ballari, S. A. (2012). Impact of wild boar (Sus scrofa) in its introduced and native range: a review. Biological Invasions 14, 2283–2300.
| Impact of wild boar (Sus scrofa) in its introduced and native range: a review.Crossref | GoogleScholarGoogle Scholar |
Batista, W. B., Mochi, L. S., and Biganzoli, F. (2018). Cattle decrease plant species diversity in protected humid temperate savanna. Phytocoenologia 48, 283–295.
| Cattle decrease plant species diversity in protected humid temperate savanna.Crossref | GoogleScholarGoogle Scholar |
Campbell, T. A., and Long, D. B. (2009). Feral swine damage and damage management in forested ecosystems. Forest Ecology and Management 257, 2319–2326.
| Feral swine damage and damage management in forested ecosystems.Crossref | GoogleScholarGoogle Scholar |
Carpio, A. J., Apollonio, M., and Acevedo, P. (2021). Wild ungulate overabundance in Europe: contexts, causes, monitoring and management recommendations. Mammal Review 51, 95–108.
| Wild ungulate overabundance in Europe: contexts, causes, monitoring and management recommendations.Crossref | GoogleScholarGoogle Scholar |
Cattadori, I. M., Haydon, D. T., Thirgood, S. J., and Hudson, P. J. (2003). Are indirect measures of abundance a useful index of population density? The case of red grouse harvesting. Oikos 100, 439–446.
| Are indirect measures of abundance a useful index of population density? The case of red grouse harvesting.Crossref | GoogleScholarGoogle Scholar |
Chébez, J. C., and Rodríguez, G. (2014). La fauna gringa: especies introducidas en la Argentina. (Fundación de Historia Natural Félix de Azara: Buenos Aires, Argentina.)
Choquenot, D., McIlroy, J., and Korn, T. (1996). Managing vertebrate pests: feral pigs. (Bureau of Resource Sciences, Australian Government Publishing Service: Canberra, ACT, Australia.)
Clark, C. W. (1985). Bioeconomic modelling and fisheries management. (Wiley: New York, NY, USA.)
Cohen, J. E. (2013). Taylor’s power law of fluctuation scaling and the growth-rate theorem. Theoretical Population Biology 88, 94–100.
| Taylor’s power law of fluctuation scaling and the growth-rate theorem.Crossref | GoogleScholarGoogle Scholar | 23689021PubMed |
Cohen, J. E., and Xu, M. (2015). Random sampling of skewed distributions implies Taylor’s power law of fluctuation scaling. Proceedings of the National Academy of Sciences of the United States of America 112, 7749–7754.
| Random sampling of skewed distributions implies Taylor’s power law of fluctuation scaling.Crossref | GoogleScholarGoogle Scholar | 25852144PubMed |
Cohen, J. E., Xu, M., and Brunborg, H. (2013a). Taylor’s law applies to spatial variation in a human population. Genus 69, 25–60.
Cohen, J. E., Xu, M., and Schuster, W. S. (2013b). Stochastic multiplicative population growth predicts and interprets Taylor’s power law of fluctuation scaling. Proceedings of the Royal Society B. Biological Sciences 280, 20122955.
| Stochastic multiplicative population growth predicts and interprets Taylor’s power law of fluctuation scaling.Crossref | GoogleScholarGoogle Scholar |
Cohen, J. E., Lai, J., Coomes, D. A., and Allen, R. B. (2016). Taylor’s law and related allometric power laws in New Zealand mountain beech forests: the roles of space, time and environment. Oikos 125, 1342–1357.
| Taylor’s law and related allometric power laws in New Zealand mountain beech forests: the roles of space, time and environment.Crossref | GoogleScholarGoogle Scholar |
Cohen, J. E., Poulin, R., and Lagrue, C. (2017a). Linking parasite populations in hosts to parasite populations in space through Taylor’s law and the negative binomial distribution. Proceedings of the National Academy of Sciences of the United States of America 114, E47–E56.
| Linking parasite populations in hosts to parasite populations in space through Taylor’s law and the negative binomial distribution.Crossref | GoogleScholarGoogle Scholar | 27994156PubMed |
Cohen, J. E., Rodríguez-Planes, L. I., Gaspe, M. S., Cecere, M. C., Cardinal, M. V., and Gürtler, R. E. (2017b). Chagas disease vector control and Taylor’s law. PLoS Neglected Tropical Diseases 11, e0006092.
| Chagas disease vector control and Taylor’s law.Crossref | GoogleScholarGoogle Scholar | 29190728PubMed |
Davis, N. E., Bennett, A., Forsyth, D. M., Bowman, D. M. J. S., Lefroy, E. C., Wood, S. W., Woolnough, A. P., West, P., Hampton, J. O., and Johnson, C. N. (2016). A systematic review of the impacts and management of introduced deer (family Cervidae) in Australia. Wildlife Research 43, 515–532.
| A systematic review of the impacts and management of introduced deer (family Cervidae) in Australia.Crossref | GoogleScholarGoogle Scholar |
Dolman, P. M., and Wäber, K. (2008). Ecosystem and competition impacts of introduced deer. Wildlife Research 35, 202–214.
| Ecosystem and competition impacts of introduced deer.Crossref | GoogleScholarGoogle Scholar |
Döring, T. F., Knapp, S., and Cohen, J. E. (2015). Taylor’s power law and the stability of crop yields. Field Crops Research 183, 294–302.
| Taylor’s power law and the stability of crop yields.Crossref | GoogleScholarGoogle Scholar |
Eisler, Z., Bartos, I., and Kertész, J. (2008). Fluctuation scaling in complex systems: Taylor’s law and beyond. Advances in Physics 57, 89–142.
| Fluctuation scaling in complex systems: Taylor’s law and beyond.Crossref | GoogleScholarGoogle Scholar |
Ferretti, F., Sforzi, A., and Lovari, S. (2008). Intolerance amongst deer species at feeding: roe deer are uneasy banqueters. Behavioural Processes 78, 487–491.
| Intolerance amongst deer species at feeding: roe deer are uneasy banqueters.Crossref | GoogleScholarGoogle Scholar | 18395364PubMed |
Forsyth, D. M., Pople, A., Woodford, L., Brennan, M., Amos, M., Moloney, P. D., Fanson, B., and Story, G. (2019). Landscape scale effects of homesteads, water, and dingoes on invading chital deer in Australia’s dry tropics. Journal of Mammalogy 100, 1954–1965.
| Landscape scale effects of homesteads, water, and dingoes on invading chital deer in Australia’s dry tropics.Crossref | GoogleScholarGoogle Scholar |
Gamelon, M., Gaillard, J. M., Servanty, S., Gimenez, O., Toıgo, C., Baubet, E., Klein, F., and Lebreton, J. D. (2012). Making use of harvest information to examine alternative management scenarios: a body weight structured model for wild boar. Journal of Applied Ecology 49, 833–841.
| Making use of harvest information to examine alternative management scenarios: a body weight structured model for wild boar.Crossref | GoogleScholarGoogle Scholar |
Gogan, P. J., Barrett, R. H., Shook, W. W., and Kucera, T. E. (2001). Control of ungulate numbers in a protected area. Wildlife Society Bulletin 29, 1075–1088.
Gürtler, R. E., Izquierdo, V. M., Gil, G., Cavicchia, M., and Maranta, A. (2017). Coping with wild boar in a conservation area: impacts of a 10-year management program of Sus scrofa in north-eastern Argentina. Biological Invasions 19, 11–24.
| Coping with wild boar in a conservation area: impacts of a 10-year management program of Sus scrofa in north-eastern Argentina.Crossref | GoogleScholarGoogle Scholar |
Gürtler, R. E., Rodríguez-Planes, L. I., Gil, G., Izquierdo, V. M., Cavicchia, M., and Maranta, A. (2018). Differential long-term impacts of a management control program of axis deer and wild boar in a protected area of north-eastern Argentina. Biological Invasions 20, 1431–1447.
| Differential long-term impacts of a management control program of axis deer and wild boar in a protected area of north-eastern Argentina.Crossref | GoogleScholarGoogle Scholar |
Hess, S. C., Muise, J., and Schipper, J. (2015). Anatomy of an eradication effort. Removing Hawaii’s illegally introduced deer. The Wildlife Professional 9, 26–29.
Hilborn, R., and Walters, C. J. (1992). Quantitative fisheries stock assessment. Choice, dynamics and uncertainty. (Chapman and Hall: New York.)
Hone, J. (2002). Feral pigs in Namadgi National Park, Australia: dynamics, impacts and management. Biological Conservation 105, 231–242.
| Feral pigs in Namadgi National Park, Australia: dynamics, impacts and management.Crossref | GoogleScholarGoogle Scholar |
Hone, J. (2012). Applied population and community ecology: the case of feral pigs in Australia. (Wiley-Blackwell: West Sussex, UK.)
Keeling, M. J., and Grenfell, B. T. (1999). Stochastic dynamics and a power law for measles variability. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 354, 769–776.
| Stochastic dynamics and a power law for measles variability.Crossref | GoogleScholarGoogle Scholar |
Kvasnes, M. A., Pedersen, H. C., Solvang, H., Storaas, T., and Nilsen, E. B. (2015). Spatial distribution and settlement strategies in willow ptarmigan. Population Ecology 57, 151–161.
| Spatial distribution and settlement strategies in willow ptarmigan.Crossref | GoogleScholarGoogle Scholar |
Lancia, R. A., Bishir, J. W., Conner, M. C., and Rosenberry, C. S. (1996). Use of catch-effort to estimate population size. Wildlife Society Bulletin 24, 731–737.
Latham, J. (1999). Interspecific interactions of ungulates in European forests: an overview. Forest Ecology and Management 120, 13–21.
| Interspecific interactions of ungulates in European forests: an overview.Crossref | GoogleScholarGoogle Scholar |
Lowe, S., Browne, M., Boudjelas, S., and De Poorter, M. (2004). 100 of the World’s Worst Invasive Alien Species: a Selection from the Global Invasive Species Database. (Invasive Species Specialist Group: Auckland, New Zealand.)
Massei, G., Kindberg, J., Licoppe, A., Gacic, G., Šprem, N., Kamler, J., Baubet, E., Hohmann, U., Monaco, A., Ozoli, J., Cellina, S., Podgórski, T., Fonseca, C., Markov, N., Pokorny, B., Rosell, C., and Náhlik, A. (2015). Wild boar populations up, numbers of hunters down? A review of trends and implications for Europe. Pest Management Science 71, 492–500.
| Wild boar populations up, numbers of hunters down? A review of trends and implications for Europe.Crossref | GoogleScholarGoogle Scholar | 25512181PubMed |
Maunder, M. N., and Piner, K. R. (2015). Contemporary fisheries stock assessment: many issues still remain. ICES Journal of Marine Science 72, 7–18.
| Contemporary fisheries stock assessment: many issues still remain.Crossref | GoogleScholarGoogle Scholar |
McMahon, C. R., Bester, M. N., Burton, H. R., Hindell, M. A., and Bradshaw, C. J. (2005). Population status, trends and a re‐examination of the hypotheses explaining the recent declines of the southern elephant seal Mirounga leonina. Mammal Review 35, 82–100.
| Population status, trends and a re‐examination of the hypotheses explaining the recent declines of the southern elephant seal Mirounga leonina.Crossref | GoogleScholarGoogle Scholar |
Miller, R. G. (1966). Simultaneous statistical inference. (McGraw-Hill: New York, NY, USA.)
Morand, S., and Krasnov, B. R. (2008). Why apply ecological laws to epidemiology? Trends in Parasitology 24, 304–309.
| Why apply ecological laws to epidemiology?Crossref | GoogleScholarGoogle Scholar | 18514576PubMed |
Nicosia, G., Rodríguez-Planes, L., and Gürtler, R. E. (2019). Patrones de actividad de ungulados exóticos del Parque Nacional El Palmar sometidos a un intenso control: implicancias para el manejo. In ‘XXXII Jornadas Argentinas de Mastozoología’. Libro de Resúmenes, p. 224. (Sociedad Argentina para el Estudio de los Mamíferos (SAREM): Puerto Madryn, Argentina.)
Novak, J. M., Scribner, K. T., Dupont, W. D., and Smith, M. H. (1991). Catch-effort estimation of white-tailed deer population size. The Journal of Wildlife Management 55, 31–38.
| Catch-effort estimation of white-tailed deer population size.Crossref | GoogleScholarGoogle Scholar |
Nugent, G., and Choquenot, D. (2004). Comparing cost-effectiveness of commercial harvesting, state-funded culling, and recreational deer hunting in New Zealand. Wildlife Society Bulletin 32, 481–492.
| Comparing cost-effectiveness of commercial harvesting, state-funded culling, and recreational deer hunting in New Zealand.Crossref | GoogleScholarGoogle Scholar |
Ohashi, H., Saito, M., Horie, R., Tsunoda, H., Noba, H., Ishii, H., Kuwabara, T., Hiroshige, Y., Koike, S., Hoshino, Y., Toda, H., and Kaji, K. (2013). Differences in the activity pattern of the wild boar Sus scrofa related to human disturbance. European Journal of Wildlife Research 59, 167–177.
| Differences in the activity pattern of the wild boar Sus scrofa related to human disturbance.Crossref | GoogleScholarGoogle Scholar |
Paolini, K. E., Strickland, B. K., Tegt, J. L., VerCauteren, K. C., and Street, G. M. (2019). The habitat functional response links seasonal third‐order selection to second‐order landscape characteristics. Ecology and Evolution 9, 4683–4691.
| The habitat functional response links seasonal third‐order selection to second‐order landscape characteristics.Crossref | GoogleScholarGoogle Scholar | 31031935PubMed |
Pauly, D., Hilborn, R., and Branch, T. A. (2013). Fisheries: does catch reflect abundance? Nature 494, 303–306.
| Fisheries: does catch reflect abundance?Crossref | GoogleScholarGoogle Scholar | 23426308PubMed |
Pedrosa, F., Salerno, R., Padilha, F. V. B., and Galetti, M. (2015). Current distribution of invasive feral pigs in Brazil: economic impacts and ecological uncertainty. Nature Conservation 13, 84–87.
Pople, A. R., Phinn, S. R., Menke, N., Grigg, G. C., Possingham, H. P., and McAlpine, C. L. (2007). Spatial patterns of kangaroo density across the South Australian pastoral zone over 26 years: aggregation during drought and suggestions of long distance movement. Journal of Applied Ecology 44, 1068–1079.
| Spatial patterns of kangaroo density across the South Australian pastoral zone over 26 years: aggregation during drought and suggestions of long distance movement.Crossref | GoogleScholarGoogle Scholar |
Rist, J., Milner‐Gulland, E. J., Cowlishaw, G. U., and Rowcliffe, M. (2010). Hunter reporting of catch per unit effort as a monitoring tool in a bushmeat‐harvesting system. Conservation Biology 24, 489–499.
| Hunter reporting of catch per unit effort as a monitoring tool in a bushmeat‐harvesting system.Crossref | GoogleScholarGoogle Scholar | 20491849PubMed |
Ruiz Selmo, R., Minotti, P. G., Scopel, A., and Parimbelli, M. (2007). Análisis de la heterogeneidad fisonómico-funcional de la vegetación del Parque Nacional El Palmar y su relación con la invasión por leñosas exóticas. In ‘Teledetección–Hacia un mejor entendimiento de la dinámica global y regional’. pp. 257–263. (Ed. Martin: Buenos Aires, Argentina.)
Skalski, J. R., Ryding, K. E., and Millspaugh, J. J. (2005). Wildlife demography. Analysis of sex, age, and count data. (Elsevier Academic Press: Boston, MA, USA.)
Snow, N. P., Jarzyna, M. A., and VerCauteren, K. C. (2017). Interpreting and predicting the spread of invasive wild pigs. Journal of Applied Ecology 54, 2022–2032.
| Interpreting and predicting the spread of invasive wild pigs.Crossref | GoogleScholarGoogle Scholar |
Spear, D., and Chown, S. L. (2009). Nonindigenous ungulates as a threat to biodiversity. Journal of Zoology 279, 1–17.
| Nonindigenous ungulates as a threat to biodiversity.Crossref | GoogleScholarGoogle Scholar |
Stata Corp (2018) Stata Statistical Software: Release 15.1. (Stata Corporation: College Station, TX, USA.)
Taylor, L. R. (1961). Aggregation, variance and the mean. Nature 189, 732–735.
| Aggregation, variance and the mean.Crossref | GoogleScholarGoogle Scholar |
Taylor, L. R., Woiwod, I. P., and Perry, J. N. (1978). The density dependence of spatial behaviour and rarity of randomness. Journal of Animal Ecology 47, 383–406.
| The density dependence of spatial behaviour and rarity of randomness.Crossref | GoogleScholarGoogle Scholar |
Taylor, L. R., Perry, J. N., Woiwod, I. P., and Taylor, R. A. J. (1988). Specificity of the spatial power-law exponent in ecology and agriculture. Nature 332, 721–722.
| Specificity of the spatial power-law exponent in ecology and agriculture.Crossref | GoogleScholarGoogle Scholar |
Thurfjell, H., Spong, G., and Ericsson, G. (2013). Effects of hunting on wild boar Sus scrofa behavior. Wildlife Biology 19, 87–93.
| Effects of hunting on wild boar Sus scrofa behavior.Crossref | GoogleScholarGoogle Scholar |
Tippett, M. K., and Cohen, J. E. (2016). Tornado outbreak variability follows Taylor’s power law of fluctuation scaling and increases dramatically with severity. Nature Communications 7, 10668.
| Tornado outbreak variability follows Taylor’s power law of fluctuation scaling and increases dramatically with severity.Crossref | GoogleScholarGoogle Scholar | 26923210PubMed |
Tolleson, D. R., Pinchak, W. E., Rollins, D., and Hunt, L. J. (1995). Feral hogs in the rolling plains of Texas: perspectives, problems, and potential. In ‘Proceedings of the twelfth Great Plains wildlife damage control workshop’. (Eds R. E. Masters, and J. G. Huggins) pp. 124–128. (Noble Foundation: Ardmore.)
Vetter, S. G., Ruf, T., Bieber, C., and Arnold, W. (2015). What is a mild winter? Regional differences in within-species responses to climate change. PLoS One 10, e0132178.
| What is a mild winter? Regional differences in within-species responses to climate change.Crossref | GoogleScholarGoogle Scholar | 26158846PubMed |
Walsh, P. D., White, L. J., Mbina, C., Idiata, D., Mihindou, Y., Maisels, F., and Thibault, M. (2001). Estimates of forest elephant abundance: projecting the relationship between precision and effort. Journal of Applied Ecology 38, 217–228.
| Estimates of forest elephant abundance: projecting the relationship between precision and effort.Crossref | GoogleScholarGoogle Scholar |
Walters, C. (2003). Folly and fantasy in the analysis of spatial catch rate data. Canadian Journal of Fisheries and Aquatic Sciences 60, 1433–1436.
| Folly and fantasy in the analysis of spatial catch rate data.Crossref | GoogleScholarGoogle Scholar |
West, B. C., Cooper, A. L., and Armstrong, J. B. (2009). Managing wild pigs: a technical guide. Human-Wildlife Interactions Monographs 1, 1–55.
Willebrand, T., Hörnell‐Willebrand, M., and Asmyhr, L. (2011). Willow grouse bag size is more sensitive to variation in hunter effort than to variation in willow grouse density. Oikos 120, 1667–1673.
| Willow grouse bag size is more sensitive to variation in hunter effort than to variation in willow grouse density.Crossref | GoogleScholarGoogle Scholar |
Xu, M., Kolding, J., and Cohen, J. E. (2017). Taylor’s power law and fixed precision sampling: application to abundance of fish sampled by gillnets in an African lake. Canadian Journal of Fisheries and Aquatic Sciences 74, 87–100.
| Taylor’s power law and fixed precision sampling: application to abundance of fish sampled by gillnets in an African lake.Crossref | GoogleScholarGoogle Scholar |
Xu, M., Kolding, J., and Cohen, J. E. (2019). Sequential analysis and design of fixed-precision sampling of Lake Kariba fishes using Taylor’s power law. Canadian Journal of Fisheries and Aquatic Sciences 76, 904–917.
| Sequential analysis and design of fixed-precision sampling of Lake Kariba fishes using Taylor’s power law.Crossref | GoogleScholarGoogle Scholar |
