RESUMO
Saimiri cassiquiarensis cassiquiarensis (Cebidae) is a primate subspecies with a wide distribution in the Amazonian region of Brazil, Colombia, and Venezuela. However, the boundaries of its geographic range remain poorly defined. This study presents new occurrence localities for this subspecies and updates its distribution using a compiled data set of 140 occurrence records based on literature, specimens vouchered in scientific collections, and new field data to produce model-based range maps. After cleaning our data set, we updated the subspecies' extent of occurrence, which was used in model calibration. We then modeled the subspecies' range using a maximum entropy algorithm (MaxEnt). The final model was adjusted using a fixed threshold, and we revised this polygon based on known geographic barriers and parapatric congeneric ranges. Our findings indicate that this subspecies is strongly associated with lowland areas, with consistently high daily temperatures. We propose modifications to all range boundaries and estimate that 3% of the area of occupancy (AOO, as defined by IUCN) has already been lost due to deforestation, resulting in a current range of 224,469 km2. We also found that 54% of their AOO is currently covered by protected areas (PAs). Based on these results, we consider that this subspecies is currently properly classified as Least Concern, because it occupies an extensive range, which is relatively well covered by PAs, and is currently experiencing low rates of deforestation.
Saimiri cassiquiarensis cassiquiarensis (Cebidae) é uma subespécie de primata com ampla distribuição na região amazônica do Brasil, Colômbia e Venezuela. No entanto, os limites de sua distribuição geográfica permanecem mal definidos. Este estudo apresenta novas localidades de ocorrência para essa subespécie e atualiza sua distribuição usando 140 registros de ocorrência compilados com base na literatura, espécimes depositados em coleções científicas e novos registros de campo para produzir mapas de distribuição baseados em modelos. Após a limpeza do nosso banco de dados, atualizamos a extensão de ocorrência da subespécie, que foi usada na calibração do modelo. Em seguida, modelamos a área de distribuição da subespécie usando um algoritmo de entropia máxima (MaxEnt). O modelo final foi ajustado usando um limiar fixo e revisamos esse polígono com base em barreiras geográficas conhecidas e na distribuição de congêneres parapátricas. Nosso modelo sugere que a espécie é fortemente associada a áreas planas, com temperaturas diárias consistentemente altas. Propomos modificações em todos os limites da área de distribuição e estimamos que 3% da área de ocupação (AOO, conforme definida pela IUCN) da subespécie já foi perdida devido ao desmatamento, resultando em uma área de distribuição atual de 224,469 km2. Também estimamos que 54% de sua AOO encontrase atualmente coberta por áreas protegidas. Com base nesses resultados, consideramos que a subespécie está apropriadamente classificada como Pouco Preocupante, pois ocupa uma área extensa, que é relativamente bem coberta por áreas protegidas e atualmente apresenta baixas taxas de desmatamento.
Assuntos
Distribuição Animal , Saimiri , Animais , Saimiri/fisiologia , Venezuela , Brasil , Colômbia , Conservação dos Recursos Naturais , EcossistemaRESUMO
BACKGROUND Kissing bugs are the vectors of Trypanosoma cruzi, the etiological agent of Chagas disease (CD). Despite their epidemiological relevance, kissing bug species are under sampled in terms of their diversity and it is unclear what biases exist in available kissing bug data. Under climate change, range maps for kissing bugs may become less accurate as species shift their ranges to track climatic tolerance. OBJECTIVES Quantify inventory completeness in available kissing bug data. Assess how well range maps are at conveying information about current distributions and potential future distributions subject to shift under climate change. Intersect forecasted changes in kissing bug distributions with contemporary sampling gaps to identify regions for future sampling of the group. Identify whether a phylogenetic signal is present in expert range knowledge as more closely related species may be similarly well or lesser understood. METHODS We used species distribution models (SDM), specifically constructed from Bayesian additive regression trees, with Bioclim variables, to forecast kissing bug distributions into 2100 and intersect these with current sampling gaps to identify priority regions for sampling. Expert range maps were assessed by the agreement between the expert map and SDM generated occurrence probability. We used classical hypothesis testing methods as well as tests of phylogenetic signal to meet our objectives. FINDINGS Expert range maps vary in their quality of depicting current kissing bug distributions. Most expert range maps decline in their ability to convey information about kissing bug occurrence over time, especially in under sampled areas. We found limited evidence for a phylogenetic signal in expert range map performance. MAIN CONCLUSIONS Expert range maps are not a perfect account of species distributions and may degrade in their ability to accurately convey distribution knowledge under future climates. We identify regions where future sampling of kissing bugs will be crucial for completing biodiversity inventories.
RESUMO
The COVID-19 pandemic is a catastrophic event that marked the history of humanity. The virus's transmissibility has primarily prevented the control of the pandemic, so it has become vital to determine and control the dynamics of the population mobility to reduce the epidemiological impact. Considering the above, this paper uses an exposure indicator based on the movement ranges provided by Facebook to determine the dynamics of population mobility in Popayán city for the period after the appearance of COVID-19. Using statistical analysis techniques, it then contrasts the data obtained with the public circulation reports provided by Google and Apple. The results suggest that the exposure indicator is reliable and presents moderate to strong linear relationships for the public data, which implies that it can be an additional resource for decision-making to curb the spread of the virus.