Biology subjects

Ferraz, G.

Publications and source records attributed to Ferraz, G..

3 recordsLinked to original sources

bioRxiv2024

Multi-Temporal Remote Sensing of Inland Surface Waters: A Fusion of Sentinel-1 Data Applied to Small Seasonal Ponds in Semiarid Environments

Inland freshwater resources in semiarid environments play a key role in maintaining ecological systems and supporting human development. Space-based remote sensing spatiotemporal data have emerged as a new paradigm for understanding ecohydrological processes and trends, particularly in water-stressed areas. However, comprehensive cataloging is still lacking, especially in semi-arid regions and for small-sized water bodies (i.e., ponds), which are often overlooked despite their ecological relevance. In this study, high-resolution optical and radar Sentinel data (Sentinel-1 and Sentinel-2) were used to construct Sentinel-1&2-based local surface water (SLSW) models, to infer surface water occurrence and extent. To assess the reliability of this model, the results were compared with verification data, and separately with Landsat-based global water (LGSW) models. Three distinct semiarid regions were selected in SW Iberia, within a Mediterranean climate, each encompassing special protection areas for conservation and subjected to marked seasonality and bioclimatic changes. Surface water attributes were modeled using Random Forests for SLSW time series forecasting, which included the period from January 1, 2020, to December 31, 2021. During this period, the completeness of the archived information was compared between SLSW and LGSW, considering both intra-annual and inter-annual variations. The predictive performance of these models was then compared for specific periods (dry and wet), and each was independently validated with verification data. The results showed that SLWM achieved satisfactory predictive performances in detecting surface water occurrence ({approx}72%), with far greater completeness and reconstructed seasonality patterns compared to LGSW. The relatedness between SLSW and LGSW was stronger during wet periods (R2=0.38) than dry periods (R2=0.05), and SLSW related much better with the verification data (R2=0.66) than when compared to LGSW (R2=0.24). The proposed SLSW approach may therefore provide advantages in the delineation of dynamic surface water characteristics (occurrence and extent) in very small-sized water bodies (i.e., <0.5 ha), allowing for uninterrupted surface water time series forecasting at high spatiotemporal detail, and over extensive areas. Given the water constraints in semiarid regions and water resources vulnerability to climate change, our results show high potential for supporting a variety of activities underlying rural development and biodiversity conservation. Additionally, the socio-ecological applications of this research may help identify surface water anomalies (e.g., drought events) and enhance sustainable water supply governance, a particular priority in climate change hotspots. HighlightsO_LISurface water occurrence and extent was modeled across three semiarid regions C_LIO_LISentinel-1&2 data was compared with Landsat for characterizing very small water bodies C_LIO_LIModels based on Sentinel-1&2 resulted in a satisfactory classification precision C_LIO_LIVery high series completeness across seasons was found using Sentinel-1&2 data C_LIO_LISentinel-1&2 data was more reliable than Landsat when compared to verification data C_LI

ecology↗

bioRxiv2021

Improving estimation of species distribution from citizen-science records using data-integration models

Mapping species distributions is a crucial but challenging requirement of wildlife management. The frequent need to sample vast expanses of potential habitat increases the cost of planned surveys and rewards accumulation of opportunistic observations. In this paper, we integrate planned survey data from roost counts with opportunistic samples from eBird, WikiAves and Xeno-canto citizen-science platforms to map the geographic range of the endangered Vinaceous-breasted Parrot. We demonstrate the estimation and mapping of species occurrence based on data integration while accounting for specifics of each data set, including observation technique and uncertainty about the observations. Our analysis illustrates 1) the incorporation of sampling effort, spatial autocorrelation, and site covariates in a joint-likelihood, hierarchical, data-integration model; 2) the evaluation of the contribution of each data set, as well as the contribution of effort covariates, spatial autocorrelation, and site covariates to the predictive ability of fitted models using a cross-validation approach; and 3) how spatial representation of the latent occupancy state (i.e. realized occupancy) helps identify areas with high uncertainty that should be prioritized in future field work. Our results reveal a Vinaceous-breasted Parrot geographic range of 434,670 km2, which is three times larger than the Extant area previously reported in the IUCN Red List. The exclusion of one data set at a time from the analyses always resulted in worse predictions by the models of truncated data than by the full model, which included all data sets. Likewise, exclusion of spatial autocorrelation, site covariates, or sampling effort resulted in worse predictions. The integration of different data sets into one joint-likelihood model produced a more reliable representation of the species range than any individual data set taken on its own improving the use of citizen science data in combination with planned survey results.

ecology↗

bioRxiv2018

Addressing multiple sources of uncertainty in the estimation of parrot abundance from roost counts: a case study with the Vinaceous-breasted Parrot (Amazona vinacea)

Population size is a key predictor of extinction risk and is critical to listing species in IUCN threat categories. The population size of parrots--one of the most threatened bird families--is often assessed using roost counts, which suffer from multiple sources of uncertainty that need to be addressed in monitoring efforts. To improve estimates of abundance for endangered Vinaceous-breasted Parrot (Amazona vinacea), we compared extensive roost counts over the whole range of the species (Argentina, Paraguay, Brazil) with an intensive regional survey designed to address five sources of uncertainty about parrot abundance in western Santa Catarina state (WSC), Brazil, in 2016 and 2017. We estimated regional-scale abundance using a sampling design that minimizes double counting and an N-mixture model of replicated count data, which accounts for imperfect detection, implemented in a Bayesian framework. The whole-range counts amounted to 3,888 and 4,084 individuals in 2016 and 2017, respectively; regional estimates were 945 {+/-} 50 and 1,393 {+/-} 40 individuals, for the same two years. We found no evidence of population growth because the increase in numbers matched an increase in observation effort on both spatial scales. When extrapolating the WSC abundance estimate to three hypothetical geographical range areas of the species, under the simplifying assumption of homogenous density, we obtained values above the whole-range counts, but within the same order of magnitude, putting the global population size of Vinaceous-breasted Parrot in the thousands of individuals. Although our estimates of abundance and geographic range are larger than those currently reported by the IUCN, we suggest that Vinaceous-breasted Parrot remain in the Endangered IUCN threat category pending further investigation of population trends. We recommend that roost-monitoring programs for parrots consider and address sources of uncertainty through field protocols and statistical analysis, to better inform assessments of population size, trends, and threat status.

ecology↗