ATLANTIC-CAMTRAPS: a dataset of medium and large terrestrial mammal communities in the Atlantic Forest of South America.

Our understanding of mammal ecology has always been hindered by the difficulties of observing species in closed tropical forests. Camera trapping has become a major advance for monitoring terrestrial mammals in biodiversity rich ecosystems. Here we compiled one of the largest datasets of inventories of terrestrial mammal communities for the Neotropical region based on camera trapping studies. The dataset comprises 170 surveys of medium to large terrestrial mammals using camera traps conducted in 144 areas by 74 studies, covering six vegetation types of tropical and subtropical Atlantic Forest of South America (Brazil and Argentina), and present data on species composition and richness. The complete dataset comprises 53,438 independent records of 83 species of mammals, includes 10 species of marsupials, 15 rodents, 20 carnivores, eight ungulates and six armadillos. Species richness averaged 13 species (±6.07 SD) per site. Only six species occurred in more than 50% of the sites: the domestic dog Canis familiaris, crab-eating fox Cerdocyon thous, tayra Eira barbara, south American coati Nasua nasua, crab-eating raccoon Procyon cancrivorus and the nine-banded armadillo Dasypus novemcinctus. The information contained in this dataset can be used to understand macroecological patterns of biodiversity, community, and population structure, but also to evaluate the ecological consequences of fragmentation, defaunation, and trophic interactions.

Incorporating animal spatial memory in step selection functions.

Memory is among the most important and neglected forces that shapes animal movement patterns. Research on the movement-memory interface is crucial to understand how animals use spatial learning to navigate across space because memory-based navigation is directly linked to animals' space use and home range behaviour; however, because memory cannot be measured directly, it is difficult to account for. Here, we incorporated spatial memory into step selection functions (SSF) to understand how resource selection and spatial memory affect space use of feral hogs (Sus scrofa). We used Biased Random Bridge kernel estimates linked to residence time as a surrogate for memory and tested four conceptually different dynamic maps of spatial memory. We applied this memory-based SSF to a data set of hog relocations to evaluate the importance of land cover type, time of day and spatial memory on the animals' space use. Our approach has shown how the incorporation of spatial memory into animal movement models can improve estimates of habitat selection. Memory-based SSF provided a feasible way to gain insight into how animals use spatial learning to guide their movement decisions. We found that while hogs selected forested areas and water bodies and avoided grasslands during the day (primarily at noon), they had a strong tendency to select previously visited areas, mainly those held in recent memory. Beyond actively updating their memory with recent experiences, hogs were able to discriminate among spatial memories encoded at different circadian phases of their activity. Even though hogs are thought to have long memory retention, they likely relied on recent experiences because the local food resources are quickly depleted and slowly renewed, yielding an uncertain spatial distribution of resources.

Bridging the gap between movement data and connectivity analysis using the Time-Explicit Habitat Selection (TEHS) model.

BACKGROUND: Understanding how to connect habitat remnants to facilitate the movement of species is a critical task in an increasingly fragmented world impacted by human activities. The identification of dispersal routes and corridors through connectivity analysis requires measures of landscape resistance but there has been no consensus on how to calculate resistance from habitat characteristics, potentially leading to very different connectivity outcomes. METHODS: We propose a new model, called the Time-Explicit Habitat Selection (TEHS) model, that can be directly used for connectivity analysis. The TEHS model decomposes the movement process in a principled approach into a time and a selection component, providing complementary information regarding space use by separately assessing the drivers of time to traverse the landscape and the drivers of habitat selection. These models are illustrated using GPS-tracking data from giant anteaters (Myrmecophaga tridactyla) in the Pantanal wetlands of Brazil. RESULTS: The time model revealed that the fastest movements tended to occur between 8 p.m. and 5 a.m., suggesting a crepuscular/nocturnal behavior. Giant anteaters moved faster over wetlands while moving much slower over forests and savannas, in comparison to grasslands. We also found that wetlands were consistently avoided whereas forest and savannas tended to be selected. Importantly, this model revealed that selection for forest increased with temperature, suggesting that forests may act as important thermal shelters when temperatures are high. Finally, using the spatial absorbing Markov chain framework, we show that the TEHS model results can be used to simulate movement and connectivity within a fragmented landscape, revealing that giant anteaters will often not use the shortest-distance path to the destination patch due to avoidance of certain habitats. CONCLUSIONS: The proposed approach can be used to characterize how landscape features are perceived by individuals through the decomposition of movement patterns into a time and a habitat selection component. Additionally, this framework can help bridge the gap between movement-based models and connectivity analysis, enabling the generation of time-explicit connectivity results.

Epidemiological Investigation of Meningeal Worm-Induced Mortalities in Small Ruminants and Camelids Over a 19 Year Period.

Meningeal worm, or Parelaphostrongylus tenuis (P. tenuis) is a nematode parasite that can invade the nervous system of small ruminant and camelid species such as alpaca, llama, goats and sheep. Limited reports exist on the epidemiology of disease caused by the nematode in susceptible livestock. We examined archived necropsy reports from small ruminant and camelid mortalities that were submitted, post mortem, to the University of Minnesota Veterinary Diagnostic Laboratory (MNVDL) during 2001-2019 for gross necropsy, histopathology, and pathogen screening. We estimated P. tenuis-induced mortality over time and developed temporal models to better understand patterns and drivers of P. tenuis-induced mortalities in these animals. During the period under examination, 5,617 goats, sheep, llamas and alpacas were necropsied, revealing an overall P. tenuis-induced mortality rate of 1.14% in the necropsy submission pool for these species. P. tenuis-induced mortality rates were highest in llamas (9.91%) and alpacas (5.33%) compared to sheep and goats (<1%), with rates in llamas and alpacas significantly higher than in sheep and goats. P. tenuis-induced mortalities exhibited one seasonal peak, around October to December. P. tenuis-induced mortality rates varied greatly between years, and have significantly increased over time. We also observed a positive correlation between summer temperature (range 20.4-22.4°C) and P. tenuis-induced mortality rates (range 0-3.9%), but not precipitation. This study demonstrates seasonal patterns and differences in mortality between alpacas, goats, llamas and sheep and helps us to better understand the epidemiology of P. tenuis mortality.

Wildfires disproportionately affected jaguars in the Pantanal.

The Pantanal wetland harbours the second largest population of jaguars in the world. Alongside climate and land-use changes, the recent mega-fires in the Pantanal may pose a threat to the jaguars' long-term survival. To put these growing threats into perspective, we addressed the reach and intensity of fires that have affected jaguar conservation in the Pantanal ecoregion over the last 16 years. The 2020 fires were the most severe in the annual series, burned 31% of the Pantanal and affected 45% of the estimated jaguar population (87% of these in Brazil); 79% of the home range areas, and 54% of the protected areas within home ranges. Fires consumed core habitats and injured several jaguars, the Pantanal's apex predator. Displacement, hunger, dehydration, territorial defence, and lower fecundity are among the impacts that may affect the abundance of the species. These impacts are likely to affect other less mobile species and, therefore, the ecological stability of the region. A solution to prevent the recurrence of mega-fires lies in combating the anthropogenic causes that intensify drought conditions, such as implementing actions to protect springs, increasing the number and area of protected areas, regulating fire use, and allocating fire brigades before dry seasons.