Geology topics

Qing Zhao

Publications and source records attributed to Qing Zhao.

7 recordsLinked to original sources

USGS2024

Designing count-based studies in a world of hierarchical models

Advances in hierarchical modeling have improved estimation of ecological parameters from count data, especially those quantifying population abundance, distribution, and dynamics by explicitly accounting for observation processes, particularly incomplete detection. Even hierarchical models that account for incomplete detection, however, cannot compensate for data limitations stemming from poorly planned sampling. Ecologists therefore need guidance for planning count-based studies that follow established sampling theory, collect appropriate data, and apply current modeling approaches to answer their research questions. We synthesize available literature relevant to guiding count-based studies. Considering the central historical and ongoing contributions of avian studies to ecological knowledge, we focus on birds as a case study for this review, but the basic principles apply to all populations whose members are sufficiently observable to be counted. The sequence of our review represents the thought process in which we encourage ecologists to engage 1) the research question(s) and population parameters to measure, 2) sampling design, 3) analytical framework, 4) temporal design, and 5) survey protocol. We also provide 2 hypothetical demonstrations of these study plan components representing different research questions and study systems. Mirroring the structure of hierarchical models, we suggest researchers primarily focus on the ecological processes of interest when designing their approach to sampling, and wait to consider logistical constraints of data collection and observation processes when developing the survey protocol. We offer a broad framework for researchers planning count-based studies, while pointing to relevant literature elaborating on particular tools and concepts.

Journal of Wildlife Management↗

USGS2022

Spatial dynamic N-mixture models with interspecific interactions

Interspecific interactions and movement are key factors that drive the coexistence of metapopulations in heterogenous landscapes. Yet, it is challenging to understand these factors because separating movement from local population processes relied on capture-based data that are difficult to collect. Recent development of spatial dynamic N-mixture models (SDNMs) made it possible to draw inference on local population growth and movement using count data of unmarked populations. However, no SDNMs have been developed to account for interspecific interactions and double counting observation errors. In this study we further developed SDNMs to account for interspecific interactions and both false negative and double counting observation errors. We conducted simulation studies to evaluate the inferential performance of these models under different ecological systems (competition, predator-prey), observation processes (binomial, Poisson), and sampling situations including the number of surveyed sites, detection, and the adoption of robust sampling design. We then illustrated the applications of these models with two case studies, one representing a competition system (mallard Anas platyrhynchos , northern pintail Anas acuta ) with binomial observations and the other representing a predator-prey system (bobcat Lynx rufus , wild turkey Meleagris gallopavo ) with Poisson observations. The results of the simulation studies showed that the models provide unbiased parameter estimates regardless of the ecological system, observation process, and sampling situation. Case studies further demonstrated the capabilities of these models in revealing important ecological processes. More specifically, the first case study revealed a negative effect of the superior competitor, Mallard, on the population growth of the inferior competitor, pintail as well as different movement patterns between these species, while the second case study reveal a top-down effect of bobcat abundance on wild turkey population growth and their differential habitat preferences. The models developed in this study could be used by themselves on count data or serve as population sub-models in integrated modeling frameworks to provide insights about metapopulation dynamics of interacting species in heterogeneous landscapes. The flexible structures of these models made them highly adaptive and relevant in population and community ecology.

Methods in Ecology and Evolution↗

USGS2019

Integrated modeling reveals shifts in waterfowl population dynamics under climate change

1. Climate change has been identified as one of the most important drivers of wildlife populations. The development of appropriate conservation strategies relies on reliable predictions of population responses to climate change, which require in-depth understanding of the complex relationships between climate and population dynamics through density dependent demographic processes. Integrated population models (IPMs) are a type of modeling approach that unify the analyses of demography and abundance data, providing opportunities to understand and predict population demography and dynamics under climate change. 2. In this study we developed dynamic N-mixture models for large scale population estimates, which became an important component of the IPM we used in data analysis. We then analyzed four decades (1974-2014) of Mallard ( Anas platyrhynchos ) breeding population survey, band-recovery, and climate data covering a large spatial extent from North American prairies through boreal habitat to Alaska. Our goals were to examine the complex relationships among climate, density dependent processes, waterfowl population demography and dynamics, identify the key demographic parameters that are sensitive to climate change and are influential to population growth, and forecast population responses to climate change. 3. Our results revealed the interactive effects of temperature and density dependent processes on Mallard recruitment and to a less extent apparent survival. We also found that recruitment explained more variance of population growth than apparent survival. We then forecasted a decrease in Mallard breeding population density in the Northern Prairie Potholes and an increase in Mallard breeding population density in the northern part of our study area, indicating potential shifts in Mallard population dynamics under future climate change. 4. Synthesis and applications Different strategies need to be considered across regions to conserve waterfowl populations under climate change. Strategies that facilitate recruitment are essential for high-density populations that are relatively vulnerable to climate change. By contrast, low-density populations are relatively resilient to climate change and their habitats may serve as future climate refugia. Adaptive management is essential for evaluating management consequences. Our modelling framework approach can be easily adapted for other species and thus has wide applications in ecology and conservation.

Ecography↗

USGS2019

Dynamic N-mixture models with temporal variability in detection probability

In theory parameters of dynamic N-mixture models can be estimated with multiple years of data without the robust design under the assumption of constant detection probability. However, such an assumption can rarely be met in long-term studies, and the consequences of violating this assumption in the inferences of dynamic N-mixture models have not been assessed. In this study we used simulation studies to evaluate inferences of the original dynamic N-mixture model and two of its spatial extensions in the face of temporal variability in detection probability. We first evaluated the dynamic N-mixture models when detection probability that varied temporally was wrongly treated as a constant. We then evaluated if the robust design was necessary for dynamic N-mixture models to provide valid parameter estimates when detection probability was correctly assumed to vary temporally. Our results showed that, when detection probability that varied temporally was wrongly treated as a constant, biases were introduced in the parameter estimates of dynamic N-mixture models. When detection probability was correctly assumed to vary temporally, the models could provide valid parameter estimates with the robust design. The model could also provide valid parameter estimates when detection probability was a random effect, even without the robust design. Based on our results, we strongly recommended considering temporal variability in detection probability when using dynamic N-mixture models to analyze long-term data and adopting the robust design in long-term surveys. Our work here is not only useful for data analysis but also important for research design, and thus are relevant to a wide range of studies.

Ecological Modelling↗

USGS2018

The non-linear, interactive effects of population density and climate drive the geographical patterns of waterfowl survival

On-going climate change has major impacts on ecological processes and patterns. Understanding the impacts of climate on the geographical patterns of survival can provide insights to how population dynamics r espond to climate change and provide important information for the development of appropriate conservation strategies at regional scales. It is challenging to understand the impacts of climate on survival, however, due to the fact that the non-linear relationship between survival and climate can be modified by density-dependent processes. In this study we extended the Brownie model to partition hunting and non-hunting mortalities and linked non-hunting survival to covariates. We applied this model to four decades (1972–2014) of waterfowl band-recovery, breeding population s urvey, and precipitation and temperature data covering multiple ecological regions to examine the non-linear, interactive effects of population density and climate on waterfowl non-hunting survival at a regional scale. Our results showed that the non-linear effect of temperature on waterfowl non-hunting survival was modified by breeding population density. The concave relationship between non-hunting survival and temperature suggested that the effects of warming on waterfowl survival might be multifaceted. Furthermore, the relationship between non-hunting survival and temperature was stronger when population density was higher, suggesting that high-density populations may be less buffered against warming than low-density populations. Our study revealed distinct relationships between waterfowl non-hunting survival and climate across and within ecological regions, highlighting the importance of considering different conservation strategies according to region-specific population and climate conditions. Our findings and associated novel modelling approach have wide implications in conservation practice.

Biological Conservation↗

USGS2017

Spatially explicit dynamic N-mixture models

Knowledge of demographic parameters such as survival, reproduction, emigration, and immigration is essential to understand metapopulation dynamics. Traditionally the estimation of these demographic parameters requires intensive data from marked animals. The development of dynamic N-mixture models makes it possible to estimate demographic parameters from count data of unmarked animals, but the original dynamic N-mixture model does not distinguish emigration and immigration from survival and reproduction, limiting its ability to explain important metapopulation processes such as movement among local populations. In this study we developed a spatially explicit dynamic N-mixture model that estimates survival, reproduction, emigration, local population size, and detection probability from count data under the assumption that movement only occurs among adjacent habitat patches. Simulation studies showed that the inference of our model depends on detection probability, local population size, and the implementation of robust sampling design. Our model provides reliable estimates of survival, reproduction, and emigration when detection probability is high, regardless of local population size or the type of sampling design. When detection probability is low, however, our model only provides reliable estimates of survival, reproduction, and emigration when local population size is moderate to high and robust sampling design is used. A sensitivity analysis showed that our model is robust against the violation of the assumption that movement only occurs among adjacent habitat patches, suggesting wide applications of this model. Our model can be used to improve our understanding of metapopulation dynamics based on count data that are relatively easy to collect in many systems.

Population Ecology↗

USGS2016

Cross-seasonal effects on waterfowl productivity: Implications under climate change

Previous efforts to relate winter-ground precipitation to subsequent reproductive success as measured by the ratio of juveniles to adults in the autumn failed to account for increased vulnerability of juvenile ducks to hunting and uncertainty in the estimated age ratio. Neglecting increased juvenile vulnerability will positively bias the mean productivity estimate, and neglecting increased vulnerability and estimation uncertainty will positively bias the year-to-year variance in productivity because raw age ratios are the product of sampling variation, the year-specific vulnerability, and year-specific reproductive success. Therefore, we estimated the effects of cumulative winter precipitation in the California Central Valley and the Mississippi Alluvial Valley on pintail ( Anas acuta ) and mallard ( Anas platyrhnchos ) reproduction, respectively, using hierarchical Bayesian methods to correct for sampling bias in productivity estimates and observation error in covariates. We applied the model to a hunter-collected parts survey implemented by the United States Fish and Wildlife Service and band recoveries reported to the United States Geological Survey Bird Banding Laboratory using data from 1961 to 2013. We compared our results to previous estimates that used simple linear regression on uncorrected age ratios from a smaller subset of years in pintail (1961–1985). Like previous analyses, we found large and consistent effects of population size and wetland conditions in prairie Canada on mallard productivity, and large effects of population size and mean latitude of the observed breeding population on pintail productivity. Unlike previous analyses, we report a large amount of uncertainty in the estimated effects of wintering-ground precipitation on pintail and mallard productivity, with considerable uncertainty in the sign of the estimated main effect, although the posterior medians of precipitation effects were consistent with past studies. We found more consistent estimates in the sign of an interaction effect between population size and precipitation, suggesting that wintering-ground precipitation has a larger effect in years of high population size, especially for pintail. When we used the estimated effects in a population model to derive a sustainable harvest and population size projection (i.e., a yield curve), there was considerable uncertainty in the effect of increased or decreased wintering-ground precipitation on sustainable harvest potential and population size. These results suggest that the mechanism of cross-seasonal effects between winter habitat and reproduction in ducks occurs through a reduction in the strength of density dependence in years of above-average wintering-ground precipitation. We suggest additional investigation of the underlying mechanisms and that habitat managers and decision-makers consider the level of uncertainty in these estimates when attempting to integrate habitat management and harvest management decisions. Collection of annual data on the status of wintering-ground habitat in a rigorous sampling framework would likely be the most direct way to improve understanding of mechanisms and inform management.

Journal of Wildlife Management↗