The Guanaco (Lama guanicoe) has suffered a progressive decline in numbers because of unregulated hunting and poaching by an assumed competition with sheep. Inadequate livestock management, including keeping sheep numbers above carrying capacity, has led to a degradation of the Patagonian steppe. Recently, interest has grown towards a reduction in sheep density and diversification of extractive activities. Guanaco populations could be potentially amenable to a number of sustainable uses. Our aim was to investigate the factors that determine guanaco distribution in southern Argentine Patagonia and to generate a predictive cartography at the regional scale. We hypothesized that guanaco distribution could be determined by primary productivity, terrain ruggedness, human disturbance and poaching, and competition with livestock. Guanaco surveys were performed from vehicles using a road survey method. To analyze the relationship between guanaco occurrence and potential predictors we built Generalized Additive Models (GAMs) using a binomial error and a logistic link. We found that guanaco occurrence increased in the less productive and remote areas, far from cities and oil camps, and decreased in regions with high sheep density. These results suggest that guanacos tend to occur where human pressure is lower. One way to promote guanaco conservation would be to highlight the economic value of guanacos under the regulations imposed by a sustainable exploitation of their populations. The predictive models developed here could be a useful tool for the implementation of conservation and management programs at the regional scale.

In this paper we show how new technologies can be incorporated from the gathering of field data on wildlife distribution to the final stage of producing distribution maps. We describe an integrated framework for conducting wildlife censuses to obtain data to build predictive models of species distribution that when integrated in a GIS will produce a distribution map. Field data can be obtained with greater accuracy and at lower costs using a combination of Global Positioning System, Personal Digital Assistant, and specific wildlife recording software. Sampling design benefits from previous knowledge of environmental variability that can be obtained from free remote sensing data. Environmental predictors derived from this remote sensing information alone, combined with automatic procedures for predictor selection and model fitting, can render cost‐effective predictive distribution models for wildlife. We show an example with guanaco distribution in the Patagonian steppes of Santa Cruz province, Argentina.

Background: Guanacos (Lama guanicoe) are thought to have declined in Patagonia mainly as a result of hunting and sheep ranching. Currently accepted estimates of total population size are extrapolated from densities obtained through strip transects in local studies. We used road surveys (8,141 km) and distance sampling to estimate guanaco density and population size over major environmental gradients of Santa Cruz, a large region in southern Patagonia. We also calculated the survey effort required to detect population trends in Santa Cruz. Results: We found considerable spatial variation in density (1.1 to 7.4 ind/km