Perry de Valpine

University of California, Berkeley | UCB · Department of Environmental Science, Policy, and Management

Aim Despite unprecedented environmental change due to anthropogenic pressure, recent work has found increasing dissimilarity due to turnover but no overall trend in species diversity through time at the local scale. Functional diversity provides a potentially powerful alternative approach for understanding community composition by linking shifts in...

Over 20 years ago, ecologists were introduced to the site occupancy model (SOM) for estimating occupancy rates from detection‐nondetection data. In the ensuing decades, the SOM and its hierarchical modeling extensions have become mainstays of quantitative ecology, and estimating occupancy rates has become one of the most common applications of ecol...

Aim As climate change increases the frequency and severity of droughts in many regions, conservation during drought is becoming a major challenge for ecologists. Droughts are multidimensional climate events whose impacts may be moderated by changes in temperature, water availability or food availability, or some combination of these. Simultaneously...

Correlative species distribution models are widely used to quantify past shifts in ranges or communities, and to predict future outcomes under ongoing global change. Practitioners confront a wide range of potentially plausible models for ecological dynamics, but most specific applications only consider a narrow set. Here, we clarify that certain mo...

Strong parametric assumptions are often made when formulating statistical models in practice. In the field of ecology, these assumptions have sparked repeated debates about identifiability of species distribution and abundance models. We leverage econometrics literature to broaden the view of the problem. Nonparametric identifiability exists when a...

Posterior predictive p-values (ppps) have become popular tools for Bayesian model criticism, being general-purpose and easy to use. However, their interpretation can be difficult because their distribution is not uniform under the hypothesis that the model did generate the data. To address this issue, procedures to obtain calibrated ppps (cppps) ha...

Climate and land-use change could exhibit concordant effects that favor or disfavor the same species, which would amplify their impacts, or species may respond to each threat in a divergent manner, causing opposing effects that moderate their impacts in isolation. We used early 20th century surveys of birds conducted by Joseph Grinnell paired with...

Open‐population spatial capture–recapture (OPSCR) models use the spatial information contained in individual detections collected over multiple consecutive occasions to estimate not only occasion‐specific density, but also demographic parameters. OPSCR models can also estimate spatial variation in vital rates, but such models are neither widely use...

Item response theory (IRT) models typically rely on a normality assumption for subject-specific latent traits, which is often unrealistic in practice. Semiparametric extensions based on Dirichlet process mixtures (DPMs) offer a more flexible representation of the unknown distribution of the latent trait. However, the use of such models in the IRT l...

Spatial capture–recapture (SCR) is now routinely used for estimating abundance and density of wildlife populations. A standard SCR model includes sub‐models for the distribution of individual activity centers (ACs) and for individual detections conditional on the locations of these ACs. Both sub‐models can be expressed as point processes taking pla...

To analyze species count data when detection is imperfect, ecologists need models to estimate relative abundance in the presence of unknown sources of heterogeneity. Two candidate models are generalized linear mixed models (GLMMs) and hierarchical N-mixture models. GLMMs are computationally robust but do not explicitly separate detection from abund...

Significance Conservation outreach has long depended on an intuitive sense of which species are more “charismatic” or engaging, for example, placing focus on certain charismatic megafauna in advertising materials. Online community science databases like eBird and iNaturalist provide records of how people engage with different birds under differing...

The increasing prevalence of high‐severity wildfire in forests in the US state of California is connected to past forest management, but uncertainty remains regarding the differential effects of land ownership on these trends. To determine whether differing forest management regimes, inferred from land ownership, influence high‐severity fire incide...

State‐space models are an increasingly common and important tool in the quantitative ecologists’ armoury, particularly for the analysis of time‐series data. This is due to both their flexibility and intuitive structure, describing the different individual processes of a complex system, thus simplifying the model specification step. State‐space mode...

Population dynamics are functions of several demographic processes including survival, reproduction, somatic growth, and maturation. The rates or probabilities for these processes can vary by time, by location, and by individual. These processes can co-vary and interact to varying degrees, e.g., an animal can only reproduce when it is in a particul...

Open-population spatial capture-recapture (OPSCR) models use the spatial information contained in individual detections collected over multiple consecutive occasions to estimate occasion-specific density, but also demographic parameters. OPSCR models can also estimate spatial variation in vital rates, but such models are neither widely used nor tho...

Motivation: Microbiome datasets provide rich information about microbial communities. However, vast library size variations across samples present great challenges for proper statistical comparisons. To deal with these challenges, rarefaction is often used in practice as a normalization technique, although there has been debate whether rarefaction...

Population dynamics are functions of several demographic processes including survival, reproduction, somatic growth, and maturation. The rates or probabilities for these processes can vary by time, by location, and by individual. These processes can co-vary and interact to varying degrees, e.g., an animal can only reproduce when it is in a particul...

Effective stewardship of ecosystems to sustain current ecological status or mitigate impacts requires nuanced understanding of how conditions have changed over time in response to anthropogenic pressures and natural variability. Detecting and appropriately characterizing changes requires accurate and flexible trend assessment methods that can be re...

A bstract Identifying which species are perceived as charismatic can improve the impact and efficiency of conservation outreach, as charismatic species receive more conservation funding and have their conservation needs prioritized (9; 17; 13). Sociological experiments studying animal charisma have relied on stated preferences to find correlations...

Over 70% of the 62 million hectares of cropland in the Midwestern United States is grown in corn-based rotations. These crop rotations are caught in a century-long simplification trend despite robust evidence demonstrating yield and soil benefits from diversified rotations. Our ability to explore and explain this trend will come in part from observ...

Capture–recapture methods are a common tool in ecological statistics, which have been extended to spatial capture–recapture models for data accompanied by location information. However, standard formulations of these models can be unwieldy and computationally intractable for large spatial scales, many individuals, and/or activity center movement. W...

Item response theory (IRT) models are widely used to obtain interpretable inference when analyzing data from questionnaires, scaling binary responses into continuous constructs. Typically, these models rely on a normality assumption for the latent trait characterizing individuals in the population under study. However, this assumption can be unreal...

Significance We are experiencing the accelerated loss and reconfiguration of biological diversity. Meanwhile, those charged with natural resource management are struggling to meet the challenges of monitoring and managing wildlife populations across vast areas crisscrossed by political borders. What if, akin to weather maps, we could track and fore...

Spatial capture-recapture (SCR) is a popular method for estimating the abundance and density of wildlife populations. A standard SCR model consists of two sub-models: one for the activity centers of individuals and the other for the detections of each individual conditional on its activity center. So far, the detection sub-model of most SCR models...

Accurate estimation of forest biomass is important for scientists and policymakers interested in carbon accounting, nutrient cycling, and forest resilience. Estimates often rely on the allometry of trees; however, limited datasets, uncertainty in model form, and unaccounted-for sources of variation warrant a reexamination of allometric relationship...

The estimation of population size remains one of the primary goals and challenges in ecology and provides a basis for debate and policy in wildlife management. Despite the development of efficient noninvasive sampling methods and robust statistical tools to estimate abundance, the maintenance of field sampling is still subject to economic and logis...

A key challenge in estimating the infection fatality rate (IFR) of COVID-19 is determining the total number of cases. The total number of cases is not known because not everyone is tested but also, more importantly, because tested individuals are not representative of the population at large. We refer to the phenomenon whereby infected individuals...

Capture-recapture methods are a common tool in ecological statistics, which have been extended to spatial capture-recapture models for data accompanied by location information. However, standard formulations of these models can be unwieldy and computationally intractable for large spatial scales, many individuals, and/or activity center movement. W...

Improved efficiency of Markov chain Monte Carlo facilitates all aspects of statistical analysis with Bayesian hierarchical models. Identifying strategies to improve MCMC performance is becoming increasingly crucial as the complexity of models, and the run times to fit them, increases. We evaluate different strategies for improving MCMC efficiency u...

The estimation of population size remains one of the primary goals and challenges in ecology and provides a basis for debate and policy in wildlife management. Despite the development of efficient non-invasive sampling methods and robust statistical tools to estimate abundance, maintenance of field sampling is still subject to economic and logistic...

Disconnected habitat fragments are poor at supporting population and community persistence; restoration ecologists, therefore, advocate for the establishment of habitat networks across landscapes. Few empirical studies, however, have considered how networks of restored habitat patches affect metacommunity dynamics. Here, using a 10‐year study on re...

We describe a new pathway for multivariate analysis of data consisting of counts of species abundances that includes two key components: copulas, to provide a flexible joint model of individual species, and dissimilarity‐based methods, to integrate information across species and provide a holistic view of the community. Individual species are chara...

Background Continued exploration of the performance of the recently proposed cross-validation-based approach for delimiting home ranges using the Time Local Convex Hull (T-LoCoH) method has revealed a number of issues with the original formulation. Main text Here we replace the ad hoc cross-validation score with a new formulation based on the tota...

Climate and land‐use change are thought to be the greatest threats to biodiversity, but few studies have directly measured their simultaneous impacts on species distributions. We used a unique historic resource – early 20th century bird surveys conducted by Joseph Grinnell and colleagues – paired with contemporary resurveys a century later to exami...

Plant secondary metabolites play important ecological and evolutionary roles, most notably in the deterrence of natural ene-mies. The classical theory explaining the evolution of plant chemical diversity is that new defences arise through a pairwise co-evolutionary arms race between plants and their specialized natural enemies. However, plant speci...

Markov chain Monte Carlo (MCMC) methods are ubiquitous tools for simulation-based inference in many fields but designing and identifying good MCMC samplers is still an open question. This paper introduces a novel MCMC algorithm, namely, Auto Adapt MCMC. For sampling variables or blocks of variables, we use two levels of adaptation where the inner a...

nimble is an R package for constructing algorithms and conducting inference on hierarchical models. The nimble package provides a unique combination of flexible model specification and the ability to program model-generic algorithms -- specifically, the package allows users to code models in the BUGS language, and it allows users to write algorithm...

nimble is an R package for constructing algorithms and conducting inference on hierarchical models. The nimble package provides a unique combination of flexible model specification and the ability to program model-generic algorithms. Specifically, the package allows users to code models in the BUGS language, and it allows users to write algorithms...

Biological communities are structured phylogenetically—closely related species are typically more likely to be found at the same sites. This may be, in part, because they respond similarly to environmental gradients. Accurately surveying biological communities is, however, made difficult by the fact that detection of species is not perfect. In rece...

Traditional Markov chain Monte Carlo (MCMC) sampling of hidden Markov models (HMMs) involves latent states underlying an imperfect observation process, and generates posterior samples for top-level parameters concurrently with nuisance latent variables. When potentially many HMMs are embedded within a hierarchical model, this can result in prohibit...

Identifying resource preference is considered essential for developing targeted conservation plans but, for many species, questions remain about the best way to estimate preference. Resource preferences for bees are particularly difficult to determine as the resources they collect, nectar and pollen, are challenging to estimate availability and col...

The interface between roots and soil, known as the rhizosphere, is a dynamic habitat in the soil ecosystem. Unraveling the factors that control rhizosphere community assembly is a key starting point for understanding the diversity of plant-microbial interactions that occur in soil. The goals of this study were to determine how environmental factors...

The interface between roots and soil, known as the rhizosphere, is a dynamic habitat in the soil ecosystem. Unraveling the factors that control rhizosphere community assembly is a key starting point for understanding the diversity of plant-microbial interactions that occur in soil. The goals of this study were to determine how environmental factors...

Cohort data are frequently collected to study stage-structured development and mortalities of many organisms, particularly arthropods. Such data can provide information on mean stage durations, among-individual variation in stage durations, and on mortality rates. Current statistical methods for cohort data lack flexibility in the specification of...

We classified land cover types from 1940s historical aerial imagery using Object Based Image Analysis (OBIA) and compared these maps with data on recent cover. Few studies have used these kinds of maps to model drivers of cover change, partly due to two statistical challenges: 1) appropriately accounting for spatial autocorrelation and 2) appropria...

Forest decline is a widespread, well-recognized problem, but studies reporting decreases in tree survival have been largely limited to relatively rare old-growth forests or low-diversity systems, and to models which are species-aggregated or cannot easily accommodate yearly climate variables. We created survival models for a multispecies second-gro...

Cohort data are frequently collected to study stage-structured development and mortalities of many organisms, particularly arthropods. Such data can provide information on mean stage durations, among-individual variation in stage durations, and on mortality rates. Current statistical methods for cohort data lack flexibility in the specification of...

We describe NIMBLE, a system for programming statistical algorithms within R for general model structures. NIMBLE is designed to meet three challenges: flexible model specification, a language for programming algorithms that can use different models, and a balance between high-level programmability and execution efficiency. For model specification,...

Markov chain Monte Carlo (MCMC) sampling is an important and commonly used tool for the analysis of hierarchical models. Nevertheless, practitioners generally have two options for MCMC: utilize existing software that generates a black-box "one size fits all" algorithm, or the challenging (and time consuming) task of implementing a problem-specific...

Agriculture today places great strains on biodiversity, soils, water and the atmosphere, and these strains will be exacerbated if current trends in population growth, meat and energy consumption, and food waste continue. Thus, farming systems that are both highly productive and minimize environmental harms are critically needed. How organic agricul...

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate...

Many systems involve progression through a series of distinct stages, such as disease or developmental stages. In ecological studies, often individuals such as small arthropods cannot be marked, so data are collected on cohort development. Multistage models for unmarked cohort data use a distribution for each stage duration and possibly stage-speci...