Modelling the abundance and productivity distribution to understand the habitat–species relationship: the guanaco (Lama guanicoe) case study
Celina E. Flores
A E , Laura M. Bellis B C and Schiavini Adrián A C D
+ Author Affiliations
- Author Affiliations
A Centro Austral de Investigaciones Científicas (CADIC-CONICET), Bernardo Houssay 200, Ushuaia, V9410, Tierra del Fuego, Argentina.
B Instituto de Altos Estudios Espaciales ‘Mario Gulich’ (CONAE-UNC), CONICET, Falda del Cañete, Ruta Provincial C45, CPA X5187XAC, Falda del Carmen, Córdoba, Argentina.
C Cátedra de Ecología de la Facultad de Ciencias Exactas Físicas y Naturales de la Universidad Nacional de Córdoba, Av. Velez Sarsfield 299, X5000 JJC, Córdoba, Argentina.
D Universidad Nacional de Tierra del Fuego Antártida e Islas del Atlántico Sur. Instituto de Ciencias Polares, Ambiente y Recursos Naturales. Fuegia Basket 251, Ushuaia, Tierra del Fuego, Argentina.
E Wildlife Conservation Society, Representación Argentina.
F Corresponding author. Email: celiflores1@gmail.com
Wildlife Research 47(6) 448-459 https://doi.org/10.1071/WR19114
Submitted: 6 July 2019 Accepted: 13 April 2020 Published: 14 August 2020
Abstract
Context: The conservation of large wild herbivores presents a challenge posed by the fact that their broad habitat requirements overlap with various human activities. Elucidating the factors that explain their distribution patterns provides us with a better understanding of habitat–species relationships and facilitates the design of effective management policies.
Aims: Identify the natural (forage availability, weather) and anthropogenic (hunting, interspecific competition) factors that explain the abundance and productivity distribution of the guanaco. Estimate guanaco abundance and productivity and describe their distribution.
Methods: We estimated the abundance and productivity of guanaco by using aerial surveys during the breeding and non-breeding season of two consecutive years, following the strip-transect methodology; we then modelled these as a function of environmental factors by means of density surface models.
Key results: The highest abundance and productivity of guanaco occurred mostly where mesic grassland was dominant. Guanaco abundance presented three hotspots on the basis of geographic location, and family groups were more productive at low to intermediate livestock level. Abundance was significantly higher in the breeding season for both years (5614 and 14 092 individuals) than in the non-breeding season (2922 and 6926 individuals), and it was higher in 2015 than in 2014. Productivity was higher in 2015 than in 2014 (0.54 and 0.46 calves per adult respectively).
Conclusions: Guanaco responded to forage availability, occupying zones with low to intermediate food availability in the breeding season, and those with the highest availability in the non-breeding season. This could be due to interspecific competition between livestock and guanaco family groups. We propose that the overall guanaco response could also be explained by social structure or by unassessed factors such as predation risk by feral dogs.
Implications: The guanaco could compensate for the use of habitats with a lower food availability during the breeding season by using better-quality habitats during the non-breeding season.
Additional keywords: environmental heterogeneity, density surface modelling, population distribution.
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