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Contributions: Judith Bentancourt Rossoli, Agustina Soto- Cabrera, Nathalia Scioscia, Julieta Pedrana and Yanina Hecker participated in the capture of rodents in dairy farms. Judith Bentancourt Rossoli, Nathalia Scioscia, and Yanina Hecker wrote the first draft of the manuscript. Judith Bentancourt Rossoli, Nathalia Scioscia and Yanina Hecker carried out the necropsies of the rodents. María V. Scioli performed the histological technique and Eleonora Morrell, Dadín Moore and Judith Bentancourt Rossoli performed the histopathological diagnosis and prepared the figures. Lucia M. Campero collaborated in DNA extractions. Judith Bentancourt Rossoli, Nathalia Scioscia and Gastón Moré processed the samples by direct microscopy, performed PCR studies, as well as analyzed the results. Gastón Moré and Walter Basso analyzed the molecular data and wrote the final version of the manuscript. All the authors revised the manuscript., The occurrence of Sarcocystis species was investigated in synanthropic (Muridae) and wild (Cricetidae) rodents from Argentina. Nine species were captured (n = 356). Sarcocysts were detected in muscles of 8.7% (31/356) and 3.7% (4/106) of the rodents by histopathology and direct microscopic observation, respectively. PCR-sequencing targeting the 18S rRNA, cox1, and ITS1 regions was performed on samples with positive histopathology. Four different 18S rRNA sequences or sequence groups with high intra-group identities (99.6–100%) were detected in Mus musculus, Oxymycterus rufus, Akodon azarae, and Necromys lasiurus. Eight sequences showed 99.5–99.7% identity with S. dispersa. Thirteen sequences showed low identity (95.3–96.4%) with other Sarcocystis spp. The obtained coxI sequences (n = 9) were almost identical to each other and showed a high similarity with S. strixi (99.2–99.5%) and S. lutrae (99.1%), despite the 18S rRNA sequences from the same samples suggested the occurrence of at least two species. This suggests that coxI may not show high variability in Sarcocystis spp. that use rodents as intermediate hosts. Six ITS1 sequences were obtained, showing high identity but low coverage with several Sarcocystis spp. Multilocus sequence typing and BLAST analysis did not lead to an accurate species identification. Possible reasons are the detection of new species or the limited molecular information available from previously described Sarcocystis spp. Phylogeny suggests that the detected Sarcocystis spp. may use raptor birds or snakes as definitive hosts. This study represents the first molecular identification of Sarcocystis spp. in naturally infected rodents of the Cricetidae and Muridae families in South America., Agencia Nacional de Promoción Científica y Tecnológica (Argentina), Instituto Nacional de Tecnología Agropecuaria (Argentina), Depto. de Sanidad Animal, Fac. de Veterinaria, TRUE, pub

Understanding human influence on ecosystems and their services is crucial to achieve sustainable development and ensure the conservation of biodiversity. In this context, the human footprint index (HFI) represents the anthropogenic impacts on ecosystems and the natural environment. Our objective was to characterize the HFI in Southern Patagonia (Argentina) across the landscape, qualifying the differences among the main ecological areas and especially the forested landscapes. We also assessed the potential utility of HFI to identify priority conservation areas according to their wilderness quality and potential biodiversity values. We created a HFI map (scores varied from 0 representing high wilderness quality to 1 representing maximum human impact) using variables related to direct (e.g. infrastructure) and indirect (e.g. derived from economic activities) human impacts, including settlements, accessibility, oil industry, and sheep production. HFI varied significantly across the natural landscapes, being lower (0.07−0.11) in remote ecosystems close to the Andes Mountains and higher (0.38−0.40) in southern areas close to the provincial capital city. Forested landscapes presented different impact values, which were directly related to the economical values of the different forest types. We determined that the current protected area network is not equally distributed across the different ecological areas and forest types. Priority conservation areas were also identified using the fragmentation produced by the human impact, the patch size, and the potential biodiversity values. HFI can present high compatibility with other land-use management decision making tools, acting as a complement to the existing tools for conservation planning or management.

Different methodologies had been developed for species management and conservation based on modelling of potential biodiversity at regional scales. However, most of these models were fitted for umbrella species (e.g. big mammals) rather than micro-fauna. Beetles should be included to improve conservation strategies due to their functional roles and vulnerability in arid environments. The maps of potential biodiversity (MPB) based on different potential habitat suitability (PHS) maps are useful to indicate high biodiversity areas. Firstly, we aim to elaborate a MPB of darkling beetles (Coleoptera: Tenebrionidae) based on 10 species PHS maps inhabiting Santa Cruz Province (Argentina). Then, we analysed the MPB an environmental gradients and land-use variables. We explored 41 potential variables to develop PHS maps. The MPB was included into a GIS project, and was analysed considering climatic and topographic variables, ecological areas and soil organic carbon (SOC) stock, also sheep density, desertification and protected area network. The modelled showed great variability in their habitat requirements (e.g. temperature), where marginality (PHS differs from the available conditions) and specialization (environmental condition range of PHS) determined three species groups. MPB increased from grasslands in the NE to shrublands in the SE, and was higher with SOC, sheep density and desertification degree. Protection areas included lower MPB for darkling beetles, where provincial reserves have a major conservation role compared with national parks. MPB allowed us to understand the potential trade-offs with the environment and human uses. This gave us a tool to development new strategies (e.g. land-sparing) for management and conservation.

Context: The guanaco is the largest wild herbivore inhabiting the Patagonian steppes. Since the end of the 19th Century, it has suffered a progressive decline in numbers owing to poaching and unregulated hunting because of on an assumed competition with sheep. Unfortunately, there has never been a management program for guanaco populations in Argentine Patagonia. Consequently, the guanaco is still considered a pest species by ranchers and has never been considered profitable in the range management model implemented in Patagonia. Aims: The present article updates the distribution limits of guanaco and estimate its abundance across Chubut, a large province of Patagonia, Argentina. The relative effects of several environmental and anthropogenic factors on guanaco distribution are also assessed. Methods: Road surveys (7010 km) and species distribution modelling were used to build a habitat suitability model and a distribution map. A distance sampling method was used to estimate guanaco population densities and size. The survey effort required to monitor population trends in this region was also calculated. Key results: According to the best habitat suitability model, guanaco distribution decreased with altitude and primary productivity, as measured by Normalised Difference Vegetation Index (NDVI), and increased with the distance to the nearest urban centre and oil field. Guanaco distribution showed a clear geographical pattern in Chubut, with low to medium occurrence probability towards the west and higher values towards the east. Guanaco population size was estimated as 657 304 individuals (95% CI 457 437 to 944 059), with a mean density of 2.97 guanacos km . Finally, through simulations of guanaco monitoring, it was estimated that an annual survey effort of 10 to thirty 30-km road transects is needed to detect with confidence a significant population decrease or increase over the next 6 or 10 years. Conclusions: The habitat suitability map presented herein highlights

Most studies concerning the foraging ecology of marine vertebrates are limited to breeding adults, although other life history stages might comprise half the total population. For penguins, little is known about juvenile dispersal, a period when individuals may be susceptible to increased mortality given their naïve foraging behaviour. Therefore, we used satellite telemetry to study king penguin fledglings (n = 18) from two sites in the Southwest Atlantic in December 2007. The two sites differed with respect to climate and proximity to the Antarctic Polar Front (APF), a key oceanographic feature generally thought to be important for king penguin foraging success. Accordingly, birds from both sites foraged predominantly in the vicinity of the APF. Eight king penguins were tracked for periods greater than 120 days; seven of these (three from the Falkland Islands and four from South Georgia) migrated into the Pacific. Only one bird from the Falkland Islands moved into the Indian Ocean, visiting the northern limit of the winter pack-ice. Three others from the Falkland Islands migrated to the eastern coast of Tierra del Fuego before travelling south. Derived tracking parameters describing their migratory behaviour showed no significant differences between sites. Nevertheless, generalized linear habitat modelling revealed that juveniles from the Falkland Islands spent more time in comparatively shallow waters with low sea surface temperature, sea surface height and chlorophyll variability. Birds from South Georgia spent more time in deeper waters with low sea surface temperature and sea surface height, but high concentrations of chlorophyll. Our results indicate that inexperienced king penguins, irrespective of the location of their natal site in relation to the position of the APF, develop their foraging skills progressively over time, including specific adaptations to the environment around their prospective breeding site.

The Lesser Rhea (Rhea pennata pennata) has suffered a marked decline in numbers over recent decades, probably mainly as a result of livestock production and overhunting. Our aim was to investigate the factors that determine the distribution of Lesser Rheas in southern Patagonia and to generate a predictive regional distribution map. We surveyed 8000 km of roads and sighted 795 Lesser Rhea individuals or flocks. We also estimated environmental predictors from remotely sensed data and analysed the occurrence of Lesser Rheas in relation to these predictors. The predictors we examined were associated with four hypotheses explaining the distribution of Lesser Rheas: the persecution by ranchers, primary productivity, topography, and anthropogenic disturbance hypotheses. We built models for each hypothesis. Our results suggest that the distribution of Lesser Rheas is not negatively affected by persecution by ranchers, as the species is more abundant in areas with high stocking levels of sheep, but is positively influenced by primary productivity and negatively by the proximity of human habitation. The resulting distribution map can be used as a management tool for government agencies and highlights the conservation priorities for managing this declining and emblematic species.

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.