Geology topics

Boyang Liu

Publications and source records attributed to Boyang Liu.

3 recordsLinked to original sources

USGS2022

Nest remains are insufficient to identify predators of waterfowl nests

Context: Nest predation is a leading cause of nest failure for most ground-nesting birds. Methods that allow for accurate classification of fate and identification of predators are important for understanding productivity and conservation strategies. Past studies have used a visual inspection of nest remains to determine nest fate and predict predator identity. Most formal assessments of these methods have addressed small-bodied birds nesting in trees or shrubs, and have revealed that use of evidence at nests can be relatively accurate for determining nest fate but may lead to incorrect conclusions regarding predator identity. However, few have tested the latter hypothesis for larger ground-nesting birds with precocial young. Aim: We aimed to evaluate a classification system developed for determining nest fate and identifying predators of waterfowl nests, at both the scale of individual nests and across the study area. Methods: From 2016 to 2020, we located 989 blue-winged teal ( Spatula discors ), mallard ( Anas platyrhynchos ) and gadwall ( Mareca strepera ) nests in central North Dakota. We placed cameras at a subset of 249 nests and recorded evidence of nest remains at depredated nests. Key results: The most common predators were American badgers ( Taxidea taxus ), followed by striped skunks ( Mephitis mephitis ), raccoons ( Procyon lotor ) and red foxes ( Vulpes vulpes ). Using evidence of nest remains, we determined nest fates with high accuracy (98.0%). However, evidence of nest remains was only sufficient for identifying predators at 50% of nests, and the classification system was correct only 69.7% of the time. The predicted proportion of predators across the study area differed between the classification system and our video evidence as well. Conclusions: The accuracy of predator identifications based upon the classification system that we evaluated was not supported at any scale. Implications: Our results suggest that evidence of nest remains can be used to determine nest fate for large-bodied precocial, ground-nesting birds, but accurate identification of nest predators will require alternative methods such as nest cameras.

Wildlife Research↗

USGS2020

Ecological prediction at macroscales using big data: Does sampling design matter?

Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially-extensive dataset to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8 million km2 area. We estimated model predictive performance for different subsets of the dataset to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non-random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in datasets from some forms of targeted sampling may limit predictive performance, compiling existing spatially-extensive datasets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.

Ecological Applications↗

USGS2019

Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data

Aquatic scientists require robust, accurate information about nutrient concentrations and indicators of algal biomass in unsampled lakes in order to understand and predict the effects of global climate and land-use change. Historically, lake and landscape characteristics have been used as predictor variables in regression models to generate nutrient predictions, but often with significant uncertainty. An alternative approach to improve predictions is to leverage the observed relationship between water clarity and nutrients, which is possible because water clarity is more commonly measured than lake nutrients. We used a joint-nutrient model that conditioned predictions of total phosphorus, nitrogen, and chlorophyll a on observed water clarity. Our results demonstrated substantial reductions (8–27%; median = 23%) in prediction error when conditioning on water clarity. These models will provide new opportunities for predicting nutrient concentrations of unsampled lakes across broad spatial scales with reduced uncertainty.

Limnology and Oceanography Letters↗