Dataset Information

Distance sampling surveys reveal 17 million vertebrates directly killed by the 2020's wildfires in the Pantanal, Brazil.

ABSTRACT: Anthropogenic factors have significantly influenced the frequency, duration, and intensity of meteorological drought in many regions of the globe, and the increased frequency of wildfires is among the most visible consequences of human-induced climate change. Despite the fire role in determining biodiversity outcomes in different ecosystems, wildfires can cause negative impacts on wildlife. We conducted ground surveys along line transects to estimate the first-order impact of the 2020 wildfires on vertebrates in the Pantanal wetland, Brazil. We adopted the distance sampling technique to estimate the densities and the number of dead vertebrates in the 39,030 square kilometers affected by fire. Our estimates indicate that at least 16.952 million vertebrates were killed immediately by the fires in the Pantanal, demonstrating the impact of such an event in wet savanna ecosystems. The Pantanal case also reminds us that the cumulative impact of widespread burning would be catastrophic, as fire recurrence may lead to the impoverishment of ecosystems and the disruption of their functioning. To overcome this unsustainable scenario, it is necessary to establish proper biomass fuel management to avoid cumulative impacts caused by fire over biodiversity and ecosystem services.

SUBMITTER: Tomas WM

PROVIDER: S-EPMC8677733 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Distance sampling surveys reveal 17 million vertebrates directly killed by the 2020's wildfires in the Pantanal, Brazil.

Tomas Walfrido Moraes WM Berlinck Christian Niel CN Chiaravalloti Rafael Morais RM Faggioni Gabriel Paganini GP Strüssmann Christine C Libonati Renata R Abrahão Carlos Roberto CR do Valle Alvarenga Gabriela G de Faria Bacellar Ana Elisa AE de Queiroz Batista Flávia Regina FR Bornato Thainan Silva TS Camilo André Restel AR Castedo Judite J Fernando Adriana Maria Espinóza AME de Freitas Gabriel Oliveira GO Garcia Carolina Martins CM Gonçalves Henrique Santos HS de Freitas Guilherme Mariella Butti MB Layme Viviane Maria Guedes VMG Lustosa Ana Paula Gomes APG De Oliveira Ailton Carneiro AC da Rosa Oliveira Maxwell M de Matos Martins Pereira Alexandre A Rodrigues Julia Abrantes JA Semedo Thiago Borges Fernandes TBF de Souza Rafael Augusto Ducel RAD Tortato Fernando Rodrigo FR Viana Diego Francis Passos DFP Vicente-Silva Luciana L Morato Ronaldo R

Scientific reports 20211216 1

Anthropogenic factors have significantly influenced the frequency, duration, and intensity of meteorological drought in many regions of the globe, and the increased frequency of wildfires is among the most visible consequences of human-induced climate change. Despite the fire role in determining biodiversity outcomes in different ecosystems, wildfires can cause negative impacts on wildlife. We conducted ground surveys along line transects to estimate the first-order impact of the 2020 wildfires ...[more]

PMID: 34916541

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Project description:Among other approaches, camera trap distance sampling (CTDS) is used to estimate animal abundance from unmarked populations. It was formulated for videos and observation distances are measured at predetermined 'snapshot moments'. Surveys recording still images with passive infrared motion sensors suffer from frequent periods where animals are not photographed, either because of technical delays before the camera can be triggered again (i.e. 'camera recovery time') or because they remain stationary and do not immediately retrigger the camera following camera recovery time (i.e. 'retrigger delays'). These effects need to be considered when calculating temporal survey effort to avoid downwardly biased abundance estimates. Here, we extend the CTDS model for passive infrared motion sensor recording of single images or short photo series. We propose estimating 'mean time intervals between triggers' as combined mean camera recovery time and mean retrigger delays from the time interval distribution of pairs of consecutive pictures, using a Gamma and Exponential function, respectively. We apply the approach to survey data on red deer, roe deer and wild boar. Mean time intervals between triggers were very similar when estimated empirically and when derived from the model-based approach. Depending on truncation times (i.e. the time interval between consecutive pictures beyond which data are discarded) and species, we estimated mean time intervals between retriggers between 8.28 and 15.05 s. Using a predefined snapshot interval, not accounting for these intervals, would lead to underestimated density by up to 96% due to overestimated temporal survey effort. The proposed approach is applicable to any taxa surveyed with camera traps. As programming of cameras to record still images is often preferred over video recording due to reduced consumption of energy and memory, we expect this approach to find broad application, also for other camera trap methods than CTDS.

Project description:Estimating population density and population dynamics is essential for understanding primate ecology and relies on robust methods. While distance sampling theory provides a robust framework for estimating animal abundance, implementing a constrained, non-systematic transect design could bias density estimates. Here, we assessed potential bias associated with line distance sampling surveys along roads based on a case study with olive baboons (Papio anubis) in Lake Manyara National Park (Tanzania). This was achieved by comparing density estimates of olive baboons derived from road transect surveys with density estimates derived from estimating the maximum number of social groups (via sleeping site counts) and multiplying this metric with the estimated average size of social groups. From 2011 to 2019, we counted olive baboons along road transects, estimated survey-specific densities in a distance sampling framework, and assessed temporal population trends. Based on the fitted half-normal detection function, the mean density was 132.5 baboons km-2 (95% CI: 110.4-159.2), however, detection models did not fit well due to heaping of sightings on and near the transects. Density estimates were associated with relatively wide confidence intervals that were mostly caused by encounter rate variance. Based on a generalized additive model, baboon densities were greater during the rainy seasons compared to the dry seasons but did not show marked annual trends. Compared to estimates derived from the alternative method (sleeping site survey), distance sampling along road transects overestimated the abundance of baboons more than threefold. Possibly, this overestimation was caused by the preferred use of roads by baboons. While being a frequently used technique (due to its relative ease of implementation compared to spatially randomized survey techniques), inferring population density of baboons (and possibly other species) based on road transects should be treated with caution. Beyond these methodological concerns and considering only the most conservative estimates, baboon densities in LMNP are among the highest across their geographic distribution range.

Dataset Information

Distance sampling surveys reveal 17 million vertebrates directly killed by the 2020's wildfires in the Pantanal, Brazil.

Publications

Distance sampling surveys reveal 17 million vertebrates directly killed by the 2020's wildfires in the Pantanal, Brazil.

Similar Datasets

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