Preprints
Filtering by Subject: Applied Statistics
New technology for an ancient fish: A lamprey life cycle modeling tool with an R Shiny application
Published: 2024-11-25
Subjects: Applied Mathematics, Applied Statistics, Ecology and Evolutionary Biology, Life Sciences, Marine Biology, Natural Resources and Conservation, Natural Resources Management and Policy, Population Biology, Systems Biology, Terrestrial and Aquatic Ecology
Lampreys (Petromyzontiformes) are an ancient group of fishes with complex life histories. We created a life cycle model that includes an R Shiny interactive web application interface to simulate abundance by life stage. This will allow scientists and managers to connect available demographic information in a framework that can be applied to questions regarding lamprey biology and conservation. We [...]
Adaptive sampling for ecological monitoring using biased data: A stratum-based approach
Published: 2024-09-10
Subjects: Applied Statistics, Ecology and Evolutionary Biology, Life Sciences, Other Ecology and Evolutionary Biology
Indicators of biodiversity change across large extents of geographic, temporal and taxonomic space are frequent products of various types of ecological monitoring and other data collection efforts. Unfortunately, many such indicators are based on data that are highly unlikely to be representative of the intended statistical populations: they are biased with respect to their estimands. Where there [...]
An integrated population modelling workflow for supporting mesopredator management
Published: 2024-08-01
Subjects: Applied Statistics, Biodiversity, Biostatistics, Ecology and Evolutionary Biology, Longitudinal Data Analysis and Time Series, Natural Resources and Conservation, Population Biology, Statistical Methodology, Statistical Models, Survival Analysis, Terrestrial and Aquatic Ecology, Zoology
Expanding populations of mesopredators threaten biodiversity and human health in many ecosystems across the world. Lethal control through harvest is commonly implemented as a mitigation measure, yet the effects of harvest and its interaction with environmental conditions on mesopredator population dynamics have rarely been assessed quantitatively due to data constraints. Recent advances [...]
Designing causal mediation analyses to quantify intermediary processes in ecology
Published: 2024-05-13
Subjects: Applied Statistics, Longitudinal Data Analysis and Time Series, Other Ecology and Evolutionary Biology, Statistical Methodology
Ecologists seek to understand the intermediary ecological processes through which changes in one attribute in a system affect other attributes. Yet, quantifying the causal effects of these mediating processes in ecological systems is challenging. We must define what we mean by a “mediated effect”, determine what assumptions are required to estimate mediation effects without bias, and assess [...]
Large-scale spatio-temporal variation in vital rates and population dynamics of an alpine bird
Published: 2024-02-01
Subjects: Applied Statistics, Ecology and Evolutionary Biology, Population Biology, Statistical Models
Quantifying temporal and spatial variation in animal population size and demography is a central theme in ecological research and important for directing management and policy. However, this requires field sampling at large spatial extents and over long periods of time, which is not only prohibitively costly but often politically untenable. Participatory monitoring programs (also called citizen [...]
A big data and machine learning approach for monitoring the condition of ecosystems
Published: 2024-01-16
Subjects: Applied Statistics, Biodiversity, Bioinformatics, Earth Sciences, Ecology and Evolutionary Biology, Environmental Sciences, Forest Biology, Forest Sciences, Life Sciences, Other Ecology and Evolutionary Biology, Physical Sciences and Mathematics, Statistical Methodology, Statistical Models, Terrestrial and Aquatic Ecology
Ecosystems are highly valuable as a source of goods and services and as a heritage for future generations. Knowing their condition is extremely important for all management and conservation activities and public policies. Until now, the evaluation of ecosystem condition has been unsatisfactory and thus lacks practical implementation for most countries. We propose that ecosystem integrity is a [...]
Multimodel approaches are not the best way to understand multifactorial systems
Published: 2023-07-23
Subjects: Applied Statistics, Ecology and Evolutionary Biology
Information-theoretic (IT) and multi-model averaging (MMA) statistical approaches are widely used but suboptimal tools for pursuing a multifactorial approach (also known as the method of multiple working hypotheses) in ecology. (1) Conceptually, IT encourages ecologists to perform tests on sets of artificial models. (2) MMA improves on IT model selection by implementing a simple form of [...]
The missing link: discerning true from false negatives when sampling species interaction networks
Published: 2023-01-18
Subjects: Applied Statistics, Biodiversity, Ecology and Evolutionary Biology, Environmental Monitoring
Ecosystems are composed of networks of interacting species. These interactions allow communities of species to persist through time through both neutral and adaptive processes. Despite their importance, a robust understanding of (and ability to predict and forecast) interactions among species remains elusive. This knowledge-gap is largely driven by a shortfall of data—although species occurrence [...]
Interpolation of temporal biodiversity change, loss, and gain across scales: a machine learning approach
Published: 2022-03-15
Subjects: Applied Statistics, Biodiversity, Ecology and Evolutionary Biology, Life Sciences, Physical Sciences and Mathematics, Statistics and Probability
1. Estimates of temporal change of biodiversity, and its components loss and gain, are needed at local and geographical scales. However, we lack them because of data in-completeness, heterogeneity, and lack of temporal replication. Hence, we need a tool to integrate heterogeneous data and to account for their incompleteness. 2. We introduce spatiotemporal machine learning interpolation that can [...]