Threatened birds, climate change, and human footprint: protected areas network in Neotropical grassland hotspot.

Climate change (CC) and human footprint (HF) shape species spatial patterns and may affect the effectiveness of Protected Areas (PAs) network. Spatial patterns of threatened bird species of Subtropical-temperate hotspots in Southeastern South American grasslands are relevant biodiversity features to guide conservation policies. However, the PAs network covers less than 1% of grassland areas and does not overlap areas with the most suitable environmental conditions for threatened birds. Our aim was to find the most environmentally suitable areas for both current and future threatened birds (2050 and 2070) in Entre Ríos. We applied Systematic Conservation Planning protocols with Ecological Niche Models (ENMs) and ZONATION using distribution interaction function and HF as a cost. Then we overlapped binary maps to find priority areas among time periods. HF showed a more fragmented spatial configuration. The PAs network may include environmentally suitable conditions for threatened birds in CC scenarios and HF. We found areas that showed more connectivity in landscape prioritization over time and ensure high-quality environmental conditions for birds. We concluded that the effectiveness of the PAs network could be improved by overlapping priority areas. Our approach provides a knowledge base as a contribution to conservation-related decisions by considering HF and CC.

Species Richness and Composition of Forest Birds in Urban Parks and Reserves of Buenos Aires City, Argentina.

Urbanization is among the main factors of ecosystem transformation and threats to global biodiversity. Urban green spaces provide multiple services, being important for biodiversity and human well-being. However, the relationship between green spaces and forest birds has been scarcely studied in the Global South. In this work, we used citizen science data (eBird) to assess the variation in the species richness and composition of forest birds in two types of public urban green spaces characterized by different vegetation composition and management: parks and reserves. In general, reserves had more native and unmanaged vegetation than parks. We selected parks and reserves located in the coastal area of the Metropolitan Area of Buenos Aires, Argentina. Sampling effort was considered as the number of checklists for each site. The database allowed information to be extracted from 12 sites and 33 species. The most common species were the Green-barred Woodpecker (Colaptes melanochloros), the Narrow-billed Woodcreeper (Lepidocolaptes angustirostris), and the White-crested Tyrannulet (Serpophaga subcristata). Bird species richness was higher in reserves than in parks and was positively related to sampling effort. The forest bird species composition varied according to the type of green area and sampling effort. Species composition showed a significant nestedness, with the least rich sites being a subset of species from the richest sites. Reserves and sites with the highest sampling effort concentrated all species. The results obtained show the importance of urban reserves in the conservation of forest birds.

Responses of Urban Bird Assemblages to Land-Sparing and Land-Sharing Development Styles in Two Argentinian Cities.

Urbanization negatively affects biodiversity worldwide. Consequently, alternative urban development styles are required for an eco-friendlier urbanization process. Thus, two development styles have been suggested: land-sharing (buildings mixed with dispersed green space) and land-sparing (buildings interspersed with large green patches). We assessed differences in species diversity and composition of bird assemblages between both development styles in two Argentinian cities: Santa Fe and Buenos Aires. We surveyed birds in land-sharing and land-sparing areas during the breeding and non-breeding seasons. As a control, we also surveyed birds in areas dominated by impervious surfaces. At a local scale, we also measured the environmental noise and pedestrian traffic. At a landscape scale, we measured the percent vegetation cover surrounding development styles and their distance to the main river. In Buenos Aires, species richness was higher in land-sparing than in land-sharing. However, the Shannon diversity and Simpson diversity were higher in land-sharing. In Santa Fe, both urban development styles supported similar species richness and diversity. Species composition varied between land-sharing and land-sparing in both cities during the breeding season. The pedestrian traffic was negatively associated with species diversity. Therefore, both development styles and strategies to reduce pedestrian traffic should be taken into account to enhance different components of species diversity and composition within the urban matrix.

Determining the spatial distribution of environmental and socio-economic suitability for human leptospirosis in the face of limited epidemiological data.

BACKGROUND: Leptospirosis is among the leading zoonotic causes of morbidity and mortality worldwide. Knowledge about spatial patterns of diseases and their underlying processes have the potential to guide intervention efforts. However, leptospirosis is often an underreported and misdiagnosed disease and consequently, spatial patterns of the disease remain unclear. In the absence of accurate epidemiological data in the urban agglomeration of Santa Fe, we used a knowledge-based index and cluster analysis to identify spatial patterns of environmental and socioeconomic suitability for the disease and potential underlying processes that shape them. METHODS: We geocoded human leptospirosis cases derived from the Argentinian surveillance system during the period 2010 to 2019. Environmental and socioeconomic databases were obtained from satellite images and publicly available platforms on the web. Two sets of human leptospirosis determinants were considered according to the level of their support by the literature and expert knowledge. We used the Zonation algorithm to build a knowledge-based index and a clustering approach to identify distinct potential sets of determinants. Spatial similarity and correlations between index, clusters, and incidence rates were evaluated. RESULTS: We were able to geocode 56.36% of the human leptospirosis cases reported in the national epidemiological database. The knowledge-based index showed the suitability for human leptospirosis in the UA Santa Fe increased from downtown areas of the largest cities towards peri-urban and suburban areas. Cluster analysis revealed downtown areas were characterized by higher levels of socioeconomic conditions. Peri-urban and suburban areas encompassed two clusters which differed in terms of environmental determinants. The highest incidence rates overlapped areas with the highest suitability scores, the strength of association was low though (CSc r = 0.21, P < 0.001 and ESc r = 0.19, P < 0.001). CONCLUSIONS: We present a method to analyze the environmental and socioeconomic suitability for human leptospirosis based on literature and expert knowledge. The methodology can be thought as an evolutive and perfectible scheme as more studies are performed in the area and novel information regarding determinants of the disease become available. Our approach can be a valuable tool for decision-makers since it can serve as a baseline to plan intervention measures.

Species distribution models and empirical test: Comparing predictions with well-understood geographical distribution of Bothrops alternatus in Argentina.

Species distribution models (SDMs) estimate the geographical distribution of species although with several limitations due to sources of inaccuracy and biases. Empirical tests arose as the most important steps in scientific knowledge to assess the efficiency of model predictions, which are poorly rigorous in SDMs. A good approach to the empirical distribution (ED) of a species can be obtained from comprehensive empirical knowledge, that is, well-understood distributions gathered from large amount of data generated with appropriate spatial and temporal samples coverage. The aims of this study were to (a) compare different SDMs predictions with an ED; and (b) evaluate if fuzzy global matching (FGM) could be used as an index to compare SDMs predictions and ED. Six algorithms with 5 and 20 variables were used to assess their accuracy in predicting the ED of the venomous snake Bothrops alternatus (Viperidae). Its entire distribution is known, thanks to thorough field surveys across Argentina, with 1,767 records. ED was compared with SDMs predictions using Map Comparison Kit. SDMs predictions showed important biases in all methods used, from 70% sub-estimation to 40% over-estimation of ED. BIOCLIM predicted ≈31% of B. alternatus ED. DOMAIN predicted 99% of ED, but over-estimated 40% of the area. GLM with five variables calculated 75% of ED, while Genetic Algorithm for Rule-set Prediction showed ≈60% of ED; the last two presenting overpredictions in areas with favorable climatic conditions but not inhabited by the species. MaxEnt and RF were the only methods to detect isolated populations in the southern distribution of B. alternatus. Although SDMs proved useful in making predictions about species distribution, predictions need validation with expert maps knowledge and ED. Moreover, FGM showed a good performance as an index with values similar to True Skill Statistic, so that it could be used to relate ED and SDMs predictions.