No General Trend in Functional Diversity in Bird and Mammal Communities Despite Compositional Change - Kari E. A. Norman; Perry de Valpine; Carl Boettiger (Global Ecology and Biogeography, 2025)
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 species identity to the characteristics that underpin ecosystem processes. Here we present the first multitaxa, multisystem analysis of functional diversity and composition through time. Location Global, with a North American focus. Time Period 1923–2014. Major Taxa Studied Mammals, Birds. Methods We paired thousands of bird and mammal assemblage time series from the BioTIME database with existing trait data representative of species' functional roles to reconstruct time series of functional diversity and composition metrics. Our dataset included 2432 time series of yearly observations from 50 studies with a maximum spatial extent of 95 km 2 . Using generalised linear mixed models, we estimated general and study‐level trends for those metrics. Results We found no overall temporal trends in functional diversity metrics. Study characteristics such as taxa, realm, biome, or protection status did not distinguish between types of change exhibited by communities. We found evidence of a temporal increase in fish consumption across all communities but no evidence to support multiple predictions for specific traits, including decreasing body size, dietary shifts or changes in bird foraging strata. Main Conclusions General temporal trends indicate that on the aggregate, studies do not exhibit consistent changes in functional diversity across many taxa, biomes and realms. At the study level, the majority of studies showed no temporal trends in species or functional diversity, with the remainder of the studies falling into broad categories of functional diversity change independent of species richness, functional redundancy loss (species richness declines with functional richness maintained) and increasing species richness sometimes accompanied by increasing functional richness.

Threats to the persistence of sugar pine (Pinus lambertiana) in the western USA - Daniel E. Foster; Scott S. Stephens; Perry de Valpine; John J. Battles (Forest Ecology and Management, 2024)

A cloudy forecast for species distribution models: Predictive uncertainties abound for California birds after a century of climate and land‐use change - John D. J. Clare; Perry de Valpine; Diana A. Moanga; Morgan W. Tingley; Steven R. Beissinger (Global Change Biology, 2024)
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 model structures can embed restrictive assumptions about key sources of forecast uncertainty into an analysis. To evaluate forecast uncertainties and our ability to explain community change, we fit and compared 39 candidate multi‐ or joint species occupancy models to avian incidence data collected at 320 sites across California during the early 20th century and resurveyed a century later. We found massive (>20,000 LOOIC) differences in within‐time information criterion across models. Poorer fitting models omitting multivariate random effects predicted less variation in species richness changes and smaller contemporary communities, with considerable variation in predicted spatial patterns in richness changes across models. The top models suggested avian environmental associations changed across time, contemporary avian occupancy was influenced by previous site‐specific occupancy states, and that both latent site variables and species associations with these variables also varied over time. Collectively, our results recapitulate that simplified model assumptions not only impact predictive fit but may mask important sources of forecast uncertainty and mischaracterize the current state of system understanding when seeking to describe or project community responses to global change. We recommend that researchers seeking to make long‐term forecasts prioritize characterizing forecast uncertainty over seeking to present a single best guess. To do so reliably, we urge practitioners to employ models capable of characterizing the key sources of forecast uncertainty, where predictors, parameters and random effects may vary over time or further interact with previous occurrence states.

Estimating spatially variable and density‐dependent survival using open‐population spatial capture–recapture models - Cyril Milleret; Soumen Dey; Pierre Dupont; Henrik Brøseth; Daniel Turek; Perry de Valpine; Richard Bischof (Ecology, 2023)
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 used nor thoroughly tested. We developed a Bayesian OPSCR model that not only accounts for spatial variation in survival using spatial covariates but also estimates local density‐dependent effects on survival within a unified framework. Using simulations, we show that OPSCR models provide sound inferences on the effect of spatial covariates on survival, including multiple competing sources of mortality, each with potentially different spatial determinants. Estimation of local density‐dependent survival was possible but required more data due to the greater complexity of the model. Not accounting for spatial heterogeneity in survival led to up to 10% positive bias in abundance estimates. We provide an empirical demonstration of the model by estimating the effect of country and density on cause‐specific mortality of female wolverines ( Gulo gulo ) in central Sweden and Norway. The ability to make population‐level inferences on spatial variation in survival is an essential step toward a fully spatially explicit OPSCR model capable of disentangling the role of multiple spatial drivers of population dynamics.

A flexible and efficient B ayesian implementation of point process models for spatial capture–recapture data - Wei Zhang; Joseph D. Chipperfield; Janine B. Illian; Pierre Dupont; Cyril Milleret; Perry de Valpine; Richard Bischof (Ecology, 2023)
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 place in continuous space, but there is a lack of accessible and efficient tools to fit such models in a Bayesian paradigm. Here, we describe a set of custom functions and distributions to achieve this. Our work allows for more efficient model fitting with spatial covariates on population density, offers the option to fit SCR models using the semi‐complete data likelihood (SCDL) approach instead of data augmentation, and better reflects the spatially continuous detection process in SCR studies that use area searches. In addition, the SCDL approach is more efficient than data augmentation for simple SCR models while losing its advantages for more complicated models that account for spatial variation in either population density or detection. We present the model formulation, test it with simulations, quantify computational efficiency gains, and conclude with a real‐life example using non‐invasive genetic sampling data for an elusive large carnivore, the wolverine ( Gulo gulo ) in Norway.

Proximity of restored hedgerows interacts with local floral diversity and species' traits to shape long‐term pollinator metacommunity dynamics - Lauren C. Ponisio; Perry de Valpine; Leithen K. M’Gonigle; Claire Kremen (Ecology Letters, 2019)
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 restored hedgerows and unrestored field margins within an intensive agricultural landscape, we integrate occupancy modelling with network theory to examine the interaction between local and landscape characteristics, habitat selection and dispersal in shaping pollinator metacommunity dynamics. We show that surrounding hedgerows and remnant habitat patches interact with the local floral diversity, bee diet breadth and bee body size to influence site occupancy, via colonisation and persistence dynamics. Florally diverse sites and generalist, small‐bodied species are most important for maintaining metacommunity connectivity. By providing the first in‐depth assessment of how a network of restored habitat influences long‐term population dynamics, we confirm the conservation benefit of hedgerows for pollinator populations and demonstrate the importance of restoring and maintaining habitat networks within an inhospitable matrix.

A century of climate and land‐use change cause species turnover without loss of beta diversity in California’s Central Valley - Sarah A. MacLean; Andrea F. Rios Dominguez; Perry de Valpine; Steven R. Beissinger (Global Change Biology, 2018)
Climate and land‐use changes 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 examine changes in bird distributions in California's Central Valley, one of the most intensively modified agricultural zones in the world and a region of heterogeneous climate change. We analyzed species‐ and community‐level occupancy using multispecies occupancy models that explicitly accounted for imperfect detection probability, and developed a novel, simulation‐based method to compare the relative influences of climate and land‐use covariates on site‐level species richness and beta diversity (measured by Jaccard similarity). Surprisingly, we show that mean occupancy, species richness and between‐site similarity have remained remarkably stable over the past century. Stability in community‐level metrics masked substantial changes in species composition; occupancy declines of some species were equally matched by increases in others, predominantly species with generalist or human‐associated habitat preferences. Bird occupancy, richness and diversity within each era were driven most strongly by water availability (precipitation and percent water cover), indicating that both climate and land‐use are important drivers of species distributions. Water availability had much stronger effects than temperature, urbanization and agricultural cover, which are typically thought to drive biodiversity decline.

Warming and provenance limit tree recruitment across and beyond the elevation range of subalpine forest - Lara M. Kueppers; Erin Conlisk; Cristina Castanha; Andrew B. Moyes; Matthew J. Germino; Perry de Valpine; Margaret S. Torn; Jeffry B. Mitton (Global Change Biology, 2017)
Climate niche models project that subalpine forest ranges will extend upslope with climate warming. These projections assume that the climate suitable for adult trees will be adequate for forest regeneration, ignoring climate requirements for seedling recruitment, a potential demographic bottleneck. Moreover, local genetic adaptation is expected to facilitate range expansion, with tree populations at the upper forest edge providing the seed best adapted to the alpine. Here, we test these expectations using a novel combination of common gardens, seeded with two widely distributed subalpine conifers, and climate manipulations replicated at three elevations. Infrared heaters raised temperatures in heated plots, but raised temperatures more in the forest than at or above treeline because strong winds at high elevation reduced heating efficiency. Watering increased season‐average soil moisture similarly across sites. Contrary to expectations, warming reduced Engelmann spruce recruitment at and above treeline, as well as in the forest. Warming reduced limber pine first‐year recruitment in the forest, but had no net effect on fourth‐year recruitment at any site. Watering during the snow‐free season alleviated some negative effects of warming, indicating that warming exacerbated water limitations. Contrary to expectations of local adaptation, low‐elevation seeds of both species initially recruited more strongly than high‐elevation seeds across the elevation gradient, although the low‐provenance advantage diminished by the fourth year for Engelmann spruce, likely due to small sample sizes. High‐ and low‐elevation provenances responded similarly to warming across sites for Engelmann spruce, but differently for limber pine. In the context of increasing tree mortality, lower recruitment at all elevations with warming, combined with lower quality, high‐provenance seed being most available for colonizing the alpine, portends range contraction for Engelmann spruce. The lower sensitivity of limber pine to warming indicates a potential for this species to become more important in subalpine forest communities in the coming centuries.

Phylogenetic occupancy models integrate imperfect detection and phylogenetic signal to analyze community structure - Luke O. Frishkoff; Perry de Valpine; Leithen K. M'Gonigle (Ecology, 2017)
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 recent years, numerous statistical methods have been developed that aim to overcome deficiencies in the species detection process. However, these methods do not allow investigators to assess phylogenetic community structure. Here, we introduce the phylogenetic occupancy model (POM), which accounts for imperfect species detection while assessing phylogenetic patterns in community structure. Using simulated data sets we show that the POM grants less biased estimates of phylogenetic structure than models without imperfect detection, and can correctly ascertain the effects of species traits on community composition while accounting for evolutionary non‐independence of taxa. Integrating phylogenetic methods into widely used occupancy models will help clarify how evolutionary history influences modern day communities.

Efficient Markov chain Monte Carlo sampling for hierarchical hidden Markov models - Daniel Turek; Perry de Valpine; Christopher J. Paciorek (Environmental and Ecological Statistics, 2016)

Climate and edaphic controllers influence rhizosphere community assembly for a wild annual grass - Erin E. Nuccio; James Anderson‐Furgeson; Katerina Y. Estera; Jennifer Pett‐Ridge; Perry de Valpine; Eoin L. Brodie; Mary K. Firestone (Ecology, 2016)
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 shape rhizosphere microbial communities, such as local soil characteristics and the regional climate, and to determine the relative influence of the rhizosphere on microbial community assembly compared to the pressures imposed by the local and regional environment. We identified the bacteria present in the soil immediately adjacent to the roots of wild oat ( Avena spp .) in three California grasslands using deep Illumina 16S sequencing. Rhizosphere communities were more similar to each other than to the surrounding soil communities from which they were derived, despite the fact that the grasslands studied were separated by hundreds of kilometers. The rhizosphere was the dominant factor structuring bacterial community composition (38% variance explained), and was comparable in magnitude to the combined local and regional effects (22% and 21%, respectively). Rhizosphere communities were most influenced by factors related to the regional climate (soil moisture and temperature), while background soil communities were more influenced by soil characteristics ( pH , CEC , exchangeable cations, clay content). The Avena core microbiome was strongly phylogenetically clustered according to the metrics NRI and NTI , which indicates that selective processes likely shaped these communities. Furthermore, 17% of these taxa were not detectable in the background soil, even with a robust sequencing depth of approximately 70,000 sequences per sample. These results support the hypothesis that roots select less abundant or possibly rare populations in the soil microbial community, which appear to be lineages of bacteria that have made a physiological tradeoff for rhizosphere competence at the expense of their competitiveness in non‐rhizosphere soil.

Monte Carlo estimation of stage structured development from cohort data - Jonas Knape; Perry de Valpine (Ecology, 2016)
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 stage duration distributions and mortality rates. In this paper, we present a new method for fitting models of stage‐duration distributions and mortality to cohort data. The method is based on a Monte Carlo within MCMC algorithm and provides Bayesian estimates of parameters of stage‐structured cohort models. The algorithm is computationally demanding but allows for flexible specifications of stage‐duration distributions and mortality rates. We illustrate the algorithm with an application to data from a previously published experiment on the development of brine shrimp from Mono Lake, California, through nine successive stages. In the experiment, three different food supply and temperature combination treatments were studied. We compare the mean duration of the stages among the treatments while simultaneously estimating mortality rates and among‐individual variance of stage durations. The method promises to enable more detailed studies of development of both natural and experimental cohorts. An R package implementing the method and which allows flexible specification of stage duration distributions is provided.

The importance of individual developmental variation in stage‐structured population models - Perry de Valpine; Katherine Scranton; Jonas Knape; Karthik Ram; Nicholas J. Mills (Ecology Letters, 2014)
Population stage structure is fundamental to ecology, and models of this structure have proven useful in many different systems. Many ecological variables other than stage, such as habitat type, site occupancy and metapopulation status are also modelled using transitions among discrete states. Transitions among life stages can be characterised by the distribution of time spent in each stage, including the mean and variance of each stage duration and within‐individual correlations among multiple stage durations. Three modelling traditions represent stage durations differently. Matrix models can be derived as a long‐run approximation from any distribution of stage durations, but they are often interpreted directly as a Markov model for stage transitions. Statistical stage‐duration distribution models accommodate the variation typical of cohort development data, but such realism has rarely been incorporated in population theory or statistical population models. Delay‐differential equation models include lags but no variation, except in limited cases. We synthesise these models in one framework and illustrate how individual variation and correlations in development can impact population growth. Furthermore, different development models can yield the same long‐term matrix transition rates but different sensitivities and elasticities. Finally, we discuss future directions for estimating realistic stage duration models from data.

An approximate Bayesian computation approach to parameter estimation in a stochastic stage‐structured population model - Katherine Scranton; Jonas Knape; Perry de Valpine (Ecology, 2014)
Complex population processes may require equally complex models, which can lead to analytically intractable estimation problems. Approximate Bayesian computation (ABC) is a computational tool for parameter estimation in situations where likelihoods cannot be computed. Instead of using likelihoods, ABC methods quantify the similarities between an observed data set and repeated simulations from a model. A practical obstacle to implementing an ABC algorithm is selecting summary statistics and distance metrics that accurately capture the main features of the data. We demonstrate the application of a sequential Monte Carlo ABC sampler (ABC SMC) to parameter estimation of a general stochastic stage‐structured population model with ongoing reproduction and heterogeneity in development and mortality. Individual variation in demographic traits has considerable consequences for population dynamics in many systems, but including it in a population model by explicitly allowing stage durations to follow a realistic distribution creates a complex model. We applied the ABC SMC to fit the model to a simulated representative data set with known underlying parameters to evaluate the performance of the algorithm. We also introduced a systematic method for selecting summary statistics and distance metrics, using simulated data and receiver operating characteristic (ROC) curves from classification theory. Evaluations suggest that the approach is promising for model inference in our example of realistic stage‐structured population models.

The common sense of P values - Perry de Valpine (Ecology, 2014)

General models for resource use or other compositional count data using the Dirichlet‐multinomial distribution - Perry de Valpine; Alexandra N. Harmon-Threatt (Ecology, 2013)
Many ecological studies investigate how organisms use resources, such as habitats or foods, in relation to availability or other variables. Related statistical problems include analysis of proportions of species or genotypes in a community or population. These require statistical modeling of compositional count data: data on relative proportions of each category collected as counts. Common methods for analyzing compositional count data lack one or more important considerations. Some methods lack explicit accommodation of count data, dealing instead with proportions. Others do not handle between‐sample heterogeneity for overdispersed data. Yet others do not allow general types of relationships between explanatory variables and resource use. All three components have been combined in a Bayesian framework, but for frequentist hypothesis tests and AIC model selection, maximum‐ likelihood estimation is needed. Here we propose the Dirichlet‐multinomial distribution to accommodate overdispersed compositional count data. This approach can be used flexibly in combination with explanatory models, but the only correlations among compositional proportions that it can accommodate are the negative correlations due to the fact that proportions must sum to 1. Many existing models can be generalized to use the Dirichlet‐multinomial distribution for residual variation, and the flexibility of the approach allows new hypotheses that have often not been considered in resource preference analysis, including that availability has no relation to use. We also highlight a new design for resource use studies, with multiple individual‐use data sets from each of multiple sites, with different explanatory data for each site. We illustrate the approach with three examples. For two previously published habitat use data sets, we support the original conclusions and show that use is not unrelated to availability. For a data set of pollen collected by multiple bees from each of two sites, pollen use differs between the sites. Using bootstrap goodness‐of‐fit tests, we illustrate that the Dirichlet‐multinomial is acceptable for two of the examples but unsuitable for one of the habitat use examples.

Using uncertainty estimates in analyses of population time series - Jonas Knape; Panagiotis Besbeas; Perry de Valpine (Ecology, 2013)
Recording and monitoring wildlife is crucial for the conservation of wild species and the protection of their environment. The most common type of information reported from a monitoring scheme is a time series of population abundance estimates, but the potential of such data for analyzing population dynamics is limited due to lack of information on sampling error. Recent work has shown that replicating the sampling process and analyzing replicates jointly in a dynamical model can considerably increase estimation efficiency compared to analyzing population estimates alone. This method requires that independent replicates are available, and model fitting can be complex in general. Often, however, population estimates are accompanied by standard errors, or standard errors may be estimated from raw data using a sampling model. We evaluate a method where standard errors are used in combination with population estimates to account for sampling variability in state‐space models of population dynamics. The method is simple and lends itself readily to data derived from many sampling procedures but ignores uncertainty in the standard errors themselves. We simulate data from a Gaussian state‐space model where several observations, which may come from different sites, are available for the population at each time. Fitting the simulated data, we show that the method yields similar or even better results than a method utilizing all observations, even when there are few observations at each time. This holds under a range of simulation settings involving heteroscedastic observation error, site effects, and correlation among observations. We illustrate the approach on real data from the North American Breeding Bird Survey and show that it performs well in comparison to a more difficult maximum‐likelihood analysis of the full data under non‐Gaussian sampling error.

Frequentist analysis of hierarchical models for population dynamics and demographic data - Perry de Valpine (Journal of Ornithology, 2012)

Fitting complex population models by combining particle filters with Markov chain Monte Carlo - Jonas Knape; Perry de Valpine (Ecology, 2012)
We show how a recent framework combining Markov chain Monte Carlo (MCMC) with particle filters (PFMCMC) may be used to estimate population state‐space models. With the purpose of utilizing the strengths of each method, PFMCMC explores hidden states by particle filters, while process and observation parameters are estimated using an MCMC algorithm. PFMCMC is exemplified by analyzing time series data on a red kangaroo ( Macropus rufus ) population in New South Wales, Australia, using MCMC over model parameters based on an adaptive Metropolis‐Hastings algorithm. We fit three population models to these data; a density‐dependent logistic diffusion model with environmental variance, an unregulated stochastic exponential growth model, and a random‐walk model. Bayes factors and posterior model probabilities show that there is little support for density dependence and that the random‐walk model is the most parsimonious model. The particle filter Metropolis‐Hastings algorithm is a brute‐force method that may be used to fit a range of complex population models. Implementation is straightforward and less involved than standard MCMC for many models, and marginal densities for model selection can be obtained with little additional effort. The cost is mainly computational, resulting in long running times that may be improved by parallelizing the algorithm.

Are patterns of density dependence in the Global Population Dynamics Database driven by uncertainty about population abundance? - Jonas Knape; Perry de Valpine (Ecology Letters, 2012)
Ecology Letters (2011) 14 : 17–23 Abstract Density dependence in population growth rates is of immense importance to ecological theory and application, but is difficult to estimate. The Global Population Dynamics Database (GPDD), one of the largest collections of population time series available, has been extensively used to study cross‐taxa patterns in density dependence. A major difficulty with assessing density dependence from time series is that uncertainty in population abundance estimates can cause strong bias in both tests and estimates of strength. We analyse 627 data sets in the GPDD using Gompertz population models and account for uncertainty via the Kalman filter. Results suggest that at least 45% of the time series display density dependence, but that it is weak and difficult to detect for a large fraction. When uncertainty is ignored, magnitude of and evidence for density dependence is strong, illustrating that uncertainty in abundance estimates qualitatively changes conclusions about density dependence drawn from the GPDD.

Stochastic development in biologically structured population models - Perry de Valpine (Ecology, 2009)
Variation in organismal development is ubiquitous in nature but omitted from most age‐ and stage‐structured population models. I give a general approach for formulating and analyzing its role in density‐independent population models using the framework of integral projection models. The approach allows flexible assumptions, including correlated development times among multiple life stages. I give a new Monte Carlo numerical integration approach to calculate long‐term growth rate, its sensitivities, stable age–stage distributions and reproductive value. This method requires only simulations of individual life schedules, rather than iteration of full population dynamics, and has practical and theoretical appeal because it ties easily implemented simulations to numerical solution of demographic equations. I show that stochastic development is demographically important using two examples. For a desert cactus, many stochastic development models, with independent or correlated stage durations, can generate the same stable stage distribution (SSD) as the real data, but stable age‐within‐stage distributions and sensitivities of growth rate to demographic rates differ greatly among stochastic development scenarios. For Mediterranean fruit flies, empirical variation in maturation time has a large impact on population growth. The systematic model formulation and analysis approach given here should make consideration of variable development models widely accessible and readily extendible.

Likelihood ridges and multimodality in population growth rate models - Leo Polansky; Perry de Valpine; James O. Lloyd-Smith; Wayne M. Getz (Ecology, 2009)
A central problem in population ecology is to use time series data to estimate the form of density dependence in the per capita growth rate (pgr). This is often accomplished with phenomenological models such as the theta‐Ricker or generalized Beverton‐Holt. Using the theta‐Ricker model as a simple but flexible description of density dependence, we apply theory and simulations to show how multimodality and ridges in the likelihood surface can emerge even in the absence of model misspecification or observation error. The message for model fitting of real data is to consider the likelihood surface in detail, check whether the best‐fit model is located on a likelihood ridge and, if so, evaluate predictive differences of biologically plausible models along the ridge. We present a detailed analysis of a focal data set showing how multimodality and ridges emerge in practice for fits of several parametric models, including a state–space model with explicit accommodation of observation error. Best‐fit models for these data are biologically dubious beyond the range of the data, and likelihood ratio confidence regions include a wide range of more biologically plausible models. We demonstrate the broad relevance of these findings by presenting analyses of 25 additional data sets spanning a wide range of taxa. The results here are relevant to information‐theoretic and Bayesian methods, which also rely on likelihoods. Beyond presentation of best‐fit models and confidence regions around individual parameters, effort toward understanding features of the likelihood surface will help ensure the most robust translation from statistical analysis to biological interpretation.

The fire frequency analysis branch of the pyrostatistics tree: sampling decisions and censoring in fire interval data - Max A. Moritz; Tadashi J. Moody; Lori J. Miles; Matthew M. Smith; Perry de Valpine (Environmental and Ecological Statistics, 2009)

Parameter estimation in a generalized discrete-time model of density dependence - Leo Polansky; Perry de Valpine; James O. Lloyd-Smith; Wayne M. Getz (Theoretical Ecology, 2008)

IS SPREAD OF INVASIVE SPECIES REGULATED? USING ECOLOGICAL THEORY TO INTERPRET STATISTICAL ANALYSIS - Perry de Valpine; Kim Cuddington; Martha F. Hoopes; Julie L. Lockwood (Ecology, 2008)
We investigate a recent proposal that invasive species display patterns of spatial “spread regulation” analogous to density‐dependent regulation of population abundances. While invasive species do offer valuable tests of ecological theories about spatial spread, we argue that the statistical approach used in the study is not useful, and that the proposed definition of “spread regulation” is likely to be confusing. While concepts of negative feedbacks in spatial spread may be reasonable, the proposed definition of “spread regulation” encompasses accelerating, constant, or decelerating spread. There is no compelling biological or practical reason to adopt such a definition. Moreover, we show that the statistical patterns (from time series of ratios of newly to recently invaded sites) proposed as evidence of spread regulation are predictable from basic diffusion models or other common models of constant spread with some stochasticity in dynamics and/or observations. Because such a wide range of processes would generate the observed patterns, no clear biological conclusions emerge from the proposed approach to spread analysis. When regarded in the context of the impacts and management of invasive species, the proposed regulation concept has the potential to create costly misunderstandings.