Cory Merow

Advancing population ecology with integral projection models: a practical guide

Summary 1. Integral projection models (IPMs) use information on how an individual’s state influences its vital rates – survival, growth and reproduction – to make population projections. IPMs are constructed from regression models predicting vital rates from state variables (e.g. size or age) and covariates (e.g. environment). By combining regressions of vital rates, an IPM provides mechanistic insight into emergent ecological patterns such as population dynamics, species geographic distributions or life-history strategies. 2. Here, we review important resources for building IPMs and provide a comprehensive guide, with extensive R code, for their construction. IPMs can be applied to any stage-structured population; here, we illustrate IPMs for a series of plant life histories of increasing complexity and biological realism, highlighting the utility of various regression methods for capturing biological patterns. We also present case studies illustrating how IPMs can be used to predict species’ geographic distributions and life-history strategies. 3. IPMs can represent a wide range of life histories at any desired level of biological detail. Much of the strength of IPMs lies in the strength of regression models. Many subtleties arise when scaling from vital rate regressions to population-level patterns, so we provide a set of diagnostics and guidelines to ensure that models are biologically plausible. Moreover, IPMs can exploit a large existing suite of analytical tools developed for matrix projection models.

Developing Dynamic Mechanistic Species Distribution Models: Predicting Bird-Mediated Spread of Invasive Plants across Northeastern North America

Species distribution models are a fundamental tool in ecology, conservation biology, and biogeography and typically identify potential species distributions using static phenomenological models. We demonstrate the importance of complementing these popular models with spatially explicit, dynamic mechanistic models that link potential and realized distributions. We develop general grid-based, pattern-oriented spread models incorporating three mechanisms—plant population growth, local dispersal, and long-distance dispersal—to predict broadscale spread patterns in heterogeneous landscapes. We use the model to examine the spread of the invasive Celastrus orbiculatus (Oriental bittersweet) by Sturnus vulgaris (European starling) across northeastern North America. We find excellent quantitative agreement with historical spread records over the last century that are critically linked to the geometry of heterogeneous landscapes and each of the explanatory mechanisms considered. Spread of bittersweet before 1960 was primarily driven by high growth rates in developed and agricultural landscapes, while subsequent spread was mediated by expansion into deciduous and coniferous forests. Large, continuous patches of coniferous forests may substantially impede invasion. The success of C. orbiculatus and its potential mutualism with S. vulgaris suggest troubling predictions for the spread of other invasive, fleshy-fruited plant species across northeastern North America.

A comparison of Maxlike and Maxent for modelling species distributions

Summary Understanding species spatial occurrence patterns and their environmental dependence is one of the fundamental goals in ecology and evolution. Often, occurrence models are built with presence-only data because absence data are unavailable. We compare the strengths and limitations of the recently developed presence-only modelling method, Maxlike, with the more widely used Maxent. In spite of disparities highlighted by the developers of Maxlike and Maxent, we show approximate formal relationships between the parameters of Maxlike and Maxent for two scenarios to illustrate their similarity. Using case studies based on real and simulated data, we show how these similarities manifest in practice. We find more similarities than differences between Maxlike and Maxent, including coefficient values, predicted spatial distributions, similarity to presence–absence models, predictive performance and ranking the predicted suitability of cells. Maxlike reliably predicted absolute occurrence probabilities for very large data sets on landscapes where occurrence probability approximately spanned [0,1]. For smaller data sets, the uncertainty in predicted occurrence probability by Maxlike was very large, due to the inherent limitations of presence-only data. In contrast, Maxent is constrained to predicting relative occurrence probabilities or relative occurrence rates unless it is provided with additional information from presence–absence data. Both models can reliably predict relative differences in occurrence probability. The choice of which model to use depends partly on sampling assumptions, which we discuss in detail. Due to limitations of presence-only data, ecologists should typically focus on interpretations relying on relative differences in occurrence probability or relative occurrence rates. We discuss how to remedy a number of concerns about the use of Maxent and how to avoid some potential pitfalls with Maxlike – particularly related to high variance predictions. We conclude that both methods are similarly valuable for understanding and predicting species’ distributions in terms of relative differences in occurrence probability when the models are specified carefully.

Speeding Up Ecological and Evolutionary Computations in R; Essentials of High Performance Computing for Biologists

Computation has become a critical component of research in biology. A risk has emerged that computational and programming challenges may limit research scope, depth, and quality. We review various solutions to common computational efficiency problems in ecological and evolutionary research. Our review pulls together material that is currently scattered across many sources and emphasizes those techniques that are especially effective for typical ecological and environmental problems. We demonstrate how straightforward it can be to write efficient code and implement techniques such as profiling or parallel computing. We supply a newly developed R package (aprof) that helps to identify computational bottlenecks in R code and determine whether optimization can be effective. Our review is complemented by a practical set of examples and detailed Supporting Information material (S1–S3 Texts) that demonstrate large improvements in computational speed (ranging from 10.5 times to 14,000 times faster). By improving computational efficiency, biologists can feasibly solve more complex tasks, ask more ambitious questions, and include more sophisticated analyses in their research.

Resonances and quantum scattering for the Morse potential as a barrier

Quantum scattering in the presence of a potential valley followed by a barrier is examined for a Morse potential, for which exact analytical solutions are known. For our application the sign of the potential is reversed, and the wave function is required to vanish at the origin. This condition requires a special combination of hypergeometric functions, and can lead to resonances for incident energies below the top of the barrier. Numerical values for the analytical phase shifts are presented in and outside the resonant regions, and the corresponding properties of the scattering S matrix are examined in the complex momentum plane. The validity of the Breit–Wigner approximation to the resonant part of the phase shifts is tested, and a new method for finding the location of narrow resonances is described. The time decay of a resonant wave packet slowly leaking out of the valley region (on a time scale proportional to the inverse of the width of the resonance) is compared with theoretical predictions, and com...

Intensifying postfire weather and biological invasion drive species loss in a Mediterranean-type biodiversity hotspot

Significance Changing interactions between climate and fire are impacting biodiversity. We examined the longest vegetation survey record in the Fynbos, South Africa, a fire-prone Mediterranean-type ecosystem and Global Biodiversity Hotspot, finding significant impacts of prolonged hot and dry postfire weather and invasive plants on species diversity. Graminoids, herbs, and species that sprout after fire declined in diversity, whereas the climatic niches of species unique to each survey showed a 0.5 °C increase in maximum temperature. The consequences of these changes for the structure and function of this ecosystem are largely unknown. This interaction between fire and changing climate is cause for concern in fire-prone ecosystems subject to severe summer droughts and temperature extremes, such as southern Australia, California, and South Africa. Prolonged periods of extreme heat or drought in the first year after fire affect the resilience and diversity of fire-dependent ecosystems by inhibiting seed germination or increasing mortality of seedlings and resprouting individuals. This interaction between weather and fire is of growing concern as climate changes, particularly in systems subject to stand-replacing crown fires, such as most Mediterranean-type ecosystems. We examined the longest running set of permanent vegetation plots in the Fynbos of South Africa (44 y), finding a significant decline in the diversity of plots driven by increasingly severe postfire summer weather events (number of consecutive days with high temperatures and no rain) and legacy effects of historical woody alien plant densities 30 y after clearing. Species that resprout after fire and/or have graminoid or herb growth forms were particularly affected by postfire weather, whereas all species were sensitive to invasive plants. Observed differences in the response of functional types to extreme postfire weather could drive major shifts in ecosystem structure and function such as altered fire behavior, hydrology, and carbon storage. An estimated 0.5 °C increase in maximum temperature tolerance of the species sets unique to each survey further suggests selection for species adapted to hotter conditions. Taken together, our results show climate change impacts on biodiversity in the hyperdiverse Cape Floristic Region and demonstrate an important interaction between extreme weather and disturbance by fire that may make flammable ecosystems particularly sensitive to climate change.

The bien r package: A tool to access the Botanical Information and Ecology Network (BIEN) database

There is an urgent need for large-scale botanical data to improve our understanding of community assembly, coexistence, biogeography, evolution, and many other fundamental biological processes. Understanding these processes is critical for predicting and handling human-biodiversity interactions and global change dynamics such as food and energy security, ecosystem services, climate change, and species invasions. The Botanical Information and Ecology Network (BIEN) database comprises an unprecedented wealth of cleaned and standardised botanical data, containing roughly 81 million occurrence records from c. 375,000 species, c. 915,000 trait observations across 28 traits from c. 93,000 species, and co-occurrence records from 110,000 ecological plots globally, as well as 100,000 range maps and 100 replicated phylogenies (each containing 81,274 species) for New World species. Here, we describe an r package that provides easy access to these data. The bien r package allows users to access the multiple types of data in the BIEN database. Functions in this package query the BIEN database by turning user inputs into optimised PostgreSQL functions. Function names follow a convention designed to make it easy to understand what each function does. We have also developed a protocol for providing customised citations and herbarium acknowledgements for data downloaded through the bien r package. The development of the BIEN database represents a significant achievement in biological data integration, cleaning and standardization. Likewise, the bien r package represents an important tool for open science that makes the BIEN database freely and easily accessible to everyone.

Climate change both facilitates and inhibits invasive plant ranges in New England

Significance Invasive species are often expected to benefit from novel conditions encountered with global change. Our range models based on demography show that invasive Alliaria petiolata (garlic mustard) may have much lower establishment in New England under future climate, despite prolific success under current climate, whereas other invasive and native plants may expand their ranges. Forecasts suggest that management should focus on inhibiting northward spread of A. petiolata into unoccupied areas and understanding source–sink population dynamics and how community dynamics might respond to loss of A. petiolata (it modifies soil properties). Our methods illustrate inadequacy of current approaches to forecasting invasions in progress, which are based on correlations between species’ occurrence and environment and illustrate critical need for mechanistic studies. Forecasting ecological responses to climate change, invasion, and their interaction must rely on understanding underlying mechanisms. However, such forecasts require extrapolation into new locations and environments. We linked demography and environment using experimental biogeography to forecast invasive and native species’ potential ranges under present and future climate in New England, United States to overcome issues of extrapolation in novel environments. We studied two potentially nonequilibrium invasive plants’ distributions, Alliaria petiolata (garlic mustard) and Berberis thunbergii (Japanese barberry), each paired with their native ecological analogs to better understand demographic drivers of invasions. Our models predict that climate change will considerably reduce establishment of a currently prolific invader (A. petiolata) throughout New England driven by poor demographic performance in warmer climates. In contrast, invasion of B. thunbergii will be facilitated because of higher growth and germination in warmer climates, with higher likelihood to establish farther north and in closed canopy habitats in the south. Invasion success is in high fecundity for both invasive species and demographic compensation for A. petiolata relative to native analogs. For A. petiolata, simulations suggest that eradication efforts would require unrealistic efficiency; hence, management should focus on inhibiting spread into colder, currently unoccupied areas, understanding source–sink dynamics, and understanding community dynamics should A. petiolata (which is allelopathic) decline. Our results—based on considerable differences with correlative occurrence models typically used for such biogeographic forecasts—suggest the urgency of incorporating mechanism into range forecasting and invasion management to understand how climate change may alter current invasion patterns.

Processes of community assembly in an environmentally heterogeneous, high biodiversity region

Despite decades of study, the relative importance of niche-based versus neutral processes in community assembly remains largely ambiguous. Recent work suggests niche-based processes are more easily detectable at coarser spatial scales, while neutrality dominates at finer scales. Analyses of functional traits with multi-year multi-site biodiversity inventories may provide deeper insights into assembly processes and the effects of spatial scale. We examined associations between community composition, species functional traits, and environmental conditions for plant communities in the Kouga-Baviaanskloof region, an area within South Africas Cape Floristic Region (CFR) containing high α and β diversity. This region contains strong climatic gradients and topographic heterogeneity, and is comprised of distinct vegetation classes with varying fire histories, making it an ideal location to assess the role of niche-based environmental filtering on community composition by examining how traits vary with environment. We combined functional trait measurements for over 300 species with observations from vegetation surveys carried out in 1991/1992 and repeated in 2011/2012. We applied redundancy analysis, quantile regression, and null model tests to examine trends in species turnover and functional traits along environmental gradients in space and through time. Functional trait values were weakly associated with most spatial environmental gradients and only showed trends with respect to vegetation class and time since fire. However, survey plots showed greater compositional and functional stability through time than expected based on null models. Taken together, we found clear evidence for functional distinctions between vegetation classes, suggesting strong environmental filtering at this scale, most likely driven by fire dynamics. In contrast, there was little evidence of filtering effects along environmental gradients within vegetation classes, suggesting that assembly processes are largely neutral at this scale, likely the result of very high functional redundancy among species in the regional species pool.

Inferring forest fate from demographic data: from vital rates to population dynamic models

As population-level patterns of interest in forests emerge from individual vital rates, modelling forest dynamics requires making the link between the scales at which data are collected (individual stems) and the scales at which questions are asked (e.g. populations and communities). Structured population models (e.g. integral projection models (IPMs)) are useful tools for linking vital rates to population dynamics. However, the application of such models to forest trees remains challenging owing to features of tree life cycles, such as slow growth, long lifespan and lack of data on crucial ontogenic stages. We developed a survival model that accounts for size-dependent mortality and a growth model that characterizes individual heterogeneity. We integrated vital rate models into two types of population model; an analytically tractable form of IPM and an individual-based model (IBM) that is applied with stochastic simulations. We calculated longevities, passage times to, and occupancy time in, different life cycle stages, important metrics for understanding how demographic rates translate into patterns of forest turnover and carbon residence times. Here, we illustrate the methods for three tropical forest species with varying life-forms. Population dynamics from IPMs and IBMs matched a 34 year time series of data (albeit a snapshot of the life cycle for canopy trees) and highlight differences in life-history strategies between species. Specifically, the greater variation in growth rates within the two canopy species suggests an ability to respond to available resources, which in turn manifests as faster passage times and greater occupancy times in larger size classes. The framework presented here offers a novel and accessible approach to modelling the population dynamics of forest trees.