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Viviana Ruiz-Gutierrez

Publications and source records attributed to Viviana Ruiz-Gutierrez.

9 recordsLinked to original sources
USGS2025

Same view through a different lens: Comparing population trends for North American birds using eBird and the Breeding Bird Survey

Confidently estimating population trends is of vital importance for a wide range of ecological, conservation, and management applications. North America has 2 major data sources for estimating population trends of breeding birds—the North American Breeding Bird Survey (BBS) and the global participatory science project eBird. Because the surveys differ in protocols, coverage, and data analysis, their trend estimates are expected to vary in magnitude, direction, and/or precision for at least some species and regions. Here, we compare independently derived estimates of population change between 2012 and 2022 for every combination of species and bird conservation region (BCR) covered by both surveys ( n = 5,577 combinations) as well as aggregated across entire ranges or within the U.S. or Canada. Uncertainty was substantial for both surveys, though more prevalent for BBS (81% of credibility intervals for estimates included zero) than eBird (34% of confidence intervals overlapped zero). We found agreement of trend directions between the 2 surveys. Only 1.3% of estimated trends were significant in opposite directions between the 2 surveys for all species/BCR combinations, with the median difference in trend magnitude being –0.02% (BBS minus eBird trend). Correlations between the 2 were strongest for estimates that were graded as being high credibility compared to estimates judged to have medium or low credibility. Both surveys were subject to species, taxonomic, and regional effects that influenced agreement. Overall, we show where trend estimates derived from BBS and eBird agree, explore where they diverge, present several comparisons to assist in interpreting results from both surveys, and inform efforts to integrate information from each.

Ornithological Applications↗
USGS2023

Leveraging the strengths of citizen science and structured surveys to achieve scalable inference on population size

Population size is a key metric for management and policy decisions, yet wildlife monitoring programmes are often limited by the spatial and temporal scope of surveys. In these cases, citizen science data may provide complementary information at higher resolution and greater extent. We present a case study demonstrating how data from the eBird citizen science programme can be combined with regional monitoring efforts by the US Fish and Wildlife Service to produce high-resolution estimates of golden eagle abundance. We developed a model that uses aerial survey data from the western United States to calibrate high-resolution annual estimates of relative abundance from eBird. Using this model, we compared regional population size estimates based on the calibrated eBird information with those based on aerial survey data alone. Population size estimates based on the calibrated eBird information had strong correspondence to estimates from aerial survey data in two out of four regions, and population trajectories based on the two approaches showed high correlations. We demonstrate how the combination of citizen science data and targeted surveys can be used to (a) increase the spatial resolution of population size estimates, (b) extend the spatial extent of inference and (c) predict population size beyond the temporal period of surveys. Findings based on this case study can be used to refine policy metrics used by the US Fish and Wildlife Service and inform permitting regulations (e.g. mortality/harm associated with wind energy development). Policy implications : Our results demonstrate the ability of citizen science data to complement targeted monitoring programmes and improve the efficacy of decision frameworks that require information on population size or trajectory. After validating citizen science data against survey-based benchmarks, agencies can harness strengths of citizen science data to supplement information needs and increase the resolution and extent of population size predictions.

Journal of Applied Ecology↗
USGS2022

The potential of semi-structured citizen science data as a supplement for conservation decision-making: Validating the performance of eBird against targeted avian monitoring efforts

Methods are being developed to capitalize on citizen science data for research and monitoring, but these data are rarely used within established decision-making frameworks of wildlife agencies. Citizen science data are often collected at higher resolution and extent than targeted monitoring programs, and may provide complementary information. Here, we demonstrate that carefully filtered semi-structured citizen science observations, when paired with targeted survey data, can produce ecological predictions at higher resolution and extent than targeted surveys alone, and both datasets can represent complementary aspects of species' ecology. We present case studies demonstrating how citizen science data can enhance or supplement decision-making of government and conservation organizations. First, we show how the continuous spatial coverage of citizen science projects, when coupled with targeted surveys, can improve estimates of metrics used by the U.S. Fish and Wildlife Service in regulatory processes to estimate population size, and inform take limits of federally managed species nationwide. Second, we show that the spatial coverage of citizen science accommodates dynamic avian space use patterns during key times of the year, relative to standardized monitoring protocols carried out by the Illinois Natural History Survey. Lastly, we demonstrate that citizen science information can replicate estimates of migratory chronologies for the Illinois Natural History Survey and the U.S. Fish and Wildlife Service for some waterfowl species, and in some contexts can supplement missing data on abundance. These findings illustrate the value of integrating validated information from semi-structured citizen science into the current evidence base used to justify, inform, and evaluate conservation decision-making.

California, Illinois, Iowa, Missouri↗
USGS2021

Integrating tracking and resight data enables unbiased inferences about migratory connectivity and winter range survival from archival tags

Archival geolocators have transformed the study of small, migratory organisms but analysis of data from these devices requires bias correction because tags are only recovered from individuals that survive and are re-captured at their tagging location. We show that integrating geolocator recovery data and mark–resight data enables unbiased estimates of both migratory connectivity between breeding and nonbreeding populations and region-specific survival probabilities for wintering locations. Using simulations, we first demonstrate that an integrated Bayesian model returns unbiased estimates of transition probabilities between seasonal ranges. We also used simulations to determine how different sampling designs influence the estimability of transition probabilities. We then parameterized the model with tracking data and mark–resight data from declining Painted Bunting ( Passerina ciris ) populations breeding in the eastern United States, hypothesized to be threatened by the illegal pet trade in parts of their Caribbean, nonbreeding range. Consistent with this hypothesis, we found that male buntings wintering in Cuba were 20% less likely to return to the breeding grounds than birds wintering elsewhere in their range. Improving inferences from archival tags through proper data collection and further development of integrated models will advance our understanding of the full annual cycle ecology of migratory species.

Ornithological Applications↗
USGS2016

Overwintering strategies of migratory birds: a novel approach for estimating seasonal movement patterns of residents and transients

Our understanding of movement patterns in wildlife populations has played an important role in current ecological knowledge and can inform landscape conservation decisions. Direct measures of movement can be obtained using marked individuals, but this requires tracking individuals across a landscape or multiple sites. We demonstrate how movements can be estimated indirectly using single-site, capture–mark–recapture (CMR) data with a multi-state open robust design with state uncertainty model (MSORD-SU). We treat residence and transience as two phenotypic states of overwintering migrants and use time- and state-dependent probabilities of site entry and persistence as indirect measures of movement. We applied the MSORD-SU to data on eight species of overwintering Neotropical birds collected in 14 countries between 2002 and 2011. In addition to entry and persistence probabilities, we estimated the proportions of residents at a study site and mean residence times. We identified overwintering movement patterns and residence times that contrasted with prior categorizations of territoriality. Most species showed an evidence of residents entering sites at multiple time intervals, with transients tending to enter between peak resident movement times. Persistence and the proportion of residents varied by latitude, but were not always positively correlated for a given species. Synthesis and applications . Our results suggest that migratory songbirds commonly move among habitats during the overwintering period. Substantial proportions of populations appear to be comprised of transient individuals, and residents tend to persist at specific sites for relatively short periods of time. This information on persistence and movement patterns should be explored for specific habitats to guide landscape management on the wintering grounds, such as determining which habitats are conserved or restored as part of certification programmes of tropical agroforestry crops. We suggest that research and conservation efforts on Neotropical migrant songbirds focus on identifying landscape configurations and regional habitat networks that support these diverse overwintering strategies to secure full life cycle conservation.

Journal of Applied Ecology↗
USGS2016

Uncertainty in biological monitoring: a framework for data collection and analysis to account for multiple sources of sampling bias

Biological monitoring programmes are increasingly relying upon large volumes of citizen-science data to improve the scope and spatial coverage of information, challenging the scientific community to develop design and model-based approaches to improve inference. Recent statistical models in ecology have been developed to accommodate false-negative errors, although current work points to false-positive errors as equally important sources of bias. This is of particular concern for the success of any monitoring programme given that rates as small as 3% could lead to the overestimation of the occurrence of rare events by as much as 50%, and even small false-positive rates can severely bias estimates of occurrence dynamics. We present an integrated, computationally efficient Bayesian hierarchical model to correct for false-positive and false-negative errors in detection/non-detection data. Our model combines independent, auxiliary data sources with field observations to improve the estimation of false-positive rates, when a subset of field observations cannot be validated a posteriori or assumed as perfect. We evaluated the performance of the model across a range of occurrence rates, false-positive and false-negative errors, and quantity of auxiliary data. The model performed well under all simulated scenarios, and we were able to identify critical auxiliary data characteristics which resulted in improved inference. We applied our false-positive model to a large-scale, citizen-science monitoring programme for anurans in the north-eastern United States, using auxiliary data from an experiment designed to estimate false-positive error rates. Not correcting for false-positive rates resulted in biased estimates of occupancy in 4 of the 10 anuran species we analysed, leading to an overestimation of the average number of occupied survey routes by as much as 70%. The framework we present for data collection and analysis is able to efficiently provide reliable inference for occurrence patterns using data from a citizen-science monitoring programme. However, our approach is applicable to data generated by any type of research and monitoring programme, independent of skill level or scale, when effort is placed on obtaining auxiliary information on false-positive rates.

Methods in Ecology and Evolution↗
USGS2011

Detection biases yield misleading patterns of species persistence and colonization in fragmented landscapes

Species occurrence patterns, and related processes of persistence, colonization and turnover, are increasingly being used to infer habitat suitability, predict species distributions, and measure biodiversity potential. The majority of these studies do not account for observational error in their analyses despite growing evidence suggesting that the sampling process can significantly influence species detection and subsequently, estimates of occurrence. We examined the potential biases of species occurrence patterns that can result from differences in detectability across species and habitat types using hierarchical multispecies occupancy models applied to a tropical bird community in an agricultural fragmented landscape. Our results suggest that detection varies widely among species and habitat types. Not incorporating detectability severely biased occupancy dynamics for many species by overestimating turnover rates, producing misleading patterns of persistence and colonization of agricultural habitats, and misclassifying species into ecological categories (i.e., forest specialists and generalists). This is of serious concern, given that most research on the ability of agricultural lands to maintain current levels of biodiversity by and large does not correct for differences in detectability. We strongly urge researchers to apply an inferential framework which explicitly account for differences in detectability to fully characterize species-habitat relationships, correctly guide biodiversity conservation in human-modified landscapes, and generate more accurate predictions of species responses to future changes in environmental conditions.

Las Cruces Biological Station↗
USGS2010

Occupancy dynamics in a tropical bird community: Unexpectedly high forest use by birds classified as non‐forest species

1. Worldwide loss of biodiversity necessitates a clear understanding of the factors driving population declines as well as informed predictions about which species and populations are at greatest risk. The biggest threat to the long-term persistence of populations is the reduction and changes in configuration of their natural habitat. 2. Inconsistencies have been noted in the responses of populations to the combined effects of habitat loss and fragmentation. These have been widely attributed to the effects of the matrix habitats in which remnant focal habitats are typically embedded. 3. We quantified the potential effects of the inter-patch matrix by estimating occupancy and colonization of forest and surrounding non-forest matrix (NF). We estimated species-specific parameters using a dynamic, multi-species hierarchical model on a bird community in southwestern Costa Rica. 4. Overall, we found higher probabilities of occupancy and colonization of forest relative to the NF across bird species, including those previously categorized as open habitat generalists not needing forest to persist. Forest dependency was a poor predictor of occupancy dynamics in our study region, largely predicting occupancy and colonization of only non-forest habitats. 5. Our results indicate that the protection of remnant forest habitats is key for the long-term persistence of all members of the bird community in this fragmented landscape, including species typically associated with open, non-forest habitats. 6. Synthesis and applications. We identified 39 bird species of conservation concern defined by having high estimates of forest occupancy, and low estimates of occupancy and colonization of non-forest. These species survive in forest but are unlikely to venture out into open, non-forested habitats, therefore, they are vulnerable to the effects of habitat loss and fragmentation. Our hierarchical community-level model can be used to estimate species-specific occupancy dynamics for focal and inter-patch matrix habitats to identify which species within a community are likely to be impacted most by habitat loss and fragmentation. This model can be applied to other taxa (i.e. amphibians, mammals and insects) to estimate species and community occurrence dynamics in response to current environmental conditions and to make predictions in response to future changes in habitat configurations.

Journal of Applied Ecology↗