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Camera trap surveys are useful to understand animal species population trends, distribution, habitat preference, behavior, community dynamics, periods of activity, and species associations with environmental conditions. This information is ecologically important, because many species play important roles in local ecosystems as predators, herbivores, seed dispersers, and disease vectors. Additionally, many of the larger wildlife species detected by camera traps are economically important through hunting, trapping, or ecotourism. Here we present a data set of camera-trap surveys from 6,043 locations across all 100 counties of North Carolina, USA from 2009 to 2019. These data come from 26 survey initiatives and contain 215,108 records of 36 mammal species and three species of terrestrial birds. This large data set increases the geographical distribution data for these 39 mammal and bird species by >500% over what is available for North Carolina in the Global Biodiversity Information Facility (GBIF). These data can be used to conduct inquiries about species, populations, communities, or ecosystems, and to produce useful information on wildlife behavior, distribution, and interactions. There are no copyright restrictions. Please cite this paper when using the data for publication.

Arbuscular mycorrhizal (AM) fungi in the soil may influence tropical tree dynamics and forest succession. The mechanisms are poorly understood, because the functional characteristics and abundances of tree species and AM fungi are likely to be codependent. We used generalized joint attribute modeling to evaluate if AM fungi are associated with three forest community metrics for a sub-tropical montane forest in Puerto Rico. The metrics chosen to reflect changes during forest succession are the abundance of seedlings of different successional status, the amount of foliar damage on seedlings of different successional status, and community-weighted mean functional trait values (adult specific leaf area [SLA], adult wood density, and seed mass). We used high-throughput DNA sequencing to identify fungal operational taxonomic units (OTUs) in the soil. Model predictions showed that seedlings of mid- and late-successional species had less leaf damage when the 12 most common AM fungi were abundant compared to when these fungi were absent. We also found that seedlings of mid-successional species were predicted to be more abundant when the 12 most common AM fungi were abundant compared to when these fungi were absent. In contrast, early-successional tree seedlings were predicted to be less abundant when the 12 most common AM fungi were abundant compared to when these fungi were absent. Finally, we showed that, among the 12 most common AM fungi, different AM fungi were correlated with functional trait characteristics of early- or late-successional species. Together, these results suggest that early-successional species might not rely as much as mid- and late-successional species on AM fungi, and AM fungi might accelerate forest succession.

Intensifying drought is increasingly linked to global forest diebacks. Improved understanding of drought impacts on individual trees has provided limited insight into drought vulnerability in part because tree moisture access and depletion is difficult to quantify. In forests, moisture reservoir depletion occurs through water use by the trees themselves. Here, we show that drought impacts on tree fitness and demographic performance can be predicted by tracking the moisture reservoir available to trees as a mass balance, estimated in a hierarchical state-space framework. We apply this model to multiple seasonal droughts with tree transpiration measurements to demonstrate how species and size differences modulate moisture availability across landscapes. The depletion of individual moisture reservoirs can be tracked over the course of droughts and linked to biomass growth and reproductive output. This mass balance approach can predict individual moisture deficit, tree demographic performance, and drought vulnerability throughout forest stands based on measurements from a sample of trees.

Trait analysis aims to understand relationships between traits, species diversity, and the environment. Current methods could benefit from a model-based probabilistic framework that accommodates covariance between traits and quantifies contributions from inherent trait syndromes, species interactions, and responses to the environment. I develop a model-based approach that separates these effects on trait diversity. Application to USDA Forest Inventory and Analysis (FIA) data in the eastern United States demonstrates an apparent paradox, that the analysis of species better explains and predicts traits than does direct analysis of the traits themselves; trait data contain less, not more, information than species on environmental responses. Whereas variation in some traits is dominated by inherent syndromes (tendency for certain traits to be associated with others within an individual and species), others are strongly controlled by variation in species diversity. There is substantial variation in environmental control on trait patterns, between traits and regionally. In terms of environmental response traits do not aggregate into defined plant functional types, as would be desirable for models.

Tree growth response across environmental gradients is fundamental to understanding species distributional ecology and forest ecosystem ecology and to predict future ecosystem services. Cross-sectional patterns of ecosystem properties with respect to climatic gradients are often used to predict ecosystem responses to global change. Across sites in the tropics, primary productivity increases with temperature, suggesting that forest ecosystems will become more productive as temperature rises. However, this trend is confounded with a shift in species composition and so may not reflect the response of in situ forests to warming. In this study, we simultaneously studied tree diameter growth across the altitudinal ranges of species within a single genus across a geographically compact temperature gradient, to separate the direct effect of temperature on tree growth from that of species compositional turnover. Using a Bayesian state space modeling framework we combined data from repeated diameter censuses and dendrometer measurements from across a 1700-m altitudinal gradient collected over six years on over 2400 trees in Weinmannia, a dominant and widespread genus of cloud forest trees in the Andes. Within species, growth showed no consistent trend with altitude, but higher-elevation species had lower growth rates than lower-elevation species, suggesting that species turnover is largely responsible for the positive correlation between productivity and temperature in tropical forests. Our results may indicate a significant difference in how low- and high-latitude forests will respond to climate change, since temperate and boreal tree species are consistently observed to have a positive relationship between growth and temperature. If our results hold for other tropical species, a positive response in ecosystem productivity to increasing temperatures in the Andes will depend on the altitudinal migration of tree species. The rapid pace of climate change, and slow observed rates of migration, suggest a slow, or even initially negative response of ecosystem productivity to warming. Finally, this study shows how the observed scale of biological organization can affect conclusions drawn from studies of ecological phenomena across environmental gradients, and calls into question the common practice in tropical ecology of lumping species at higher taxonomic levels.

Inequality in reproductive success has important implications for ecological and evolutionary dynamics, but lifetime reproductive success is challenging to measure in long-lived species such as forest trees. While seed production is often used as a proxy for overall reproductive success, high mortality of seeds and the potential for trade-offs between seed number and quality draw this assumption into question. Parentage analyses of established seedlings can bring us one step closer to understanding the causes and consequences of variation in reproductive success. In this paper we demonstrate a new method for estimating individual seedling production and average percentage germination, using data from two mixed-species populations of red oaks (Quercus rubra, Q. velutina, Q. falcata, and Q. coccinea). We use these estimates to examine the distribution of female reproductive success and to test the relationship between seedling number and individual seed production, age, and growth rate. We show that both seed and seedling production are highly skewed, roughly conforming to zero-inflated lognormal distributions, rather than to the Poisson or negative-binomial distributions often assumed by population genetics analyses. While the number of established offspring is positively associated with mean annual seed production, a lower proportion of seeds from highly fecund individuals become seedlings. Our red oak populations also show evidence of trade-offs between growth rate and reproductive success. The high degree of inequality in seedling production shown here for red oaks, and by previous studies in other species, suggests that many trees may be more vulnerable to genetic drift than previously thought, if immigration in limited by fragmentation or other environmental changes.

Density dependence could maintain diversity in forests, but studies continue to disagree on its role. Part of the disagreement results from the fact that different studies have evaluated different responses (survival, recruitment, or growth) of different stages (seeds, seedlings, or adults) to different inputs (density of seedlings, density or distance to adults). Most studies are conducted on a single site and thus are difficult to generalize. Using USDA Forest Service's Forest Inventory and Analysis data, we analyzed over a million seedling-to-sapling recruitment observations of 50 species from the eastern United States, controlling for the effects of climate. We focused on the per-seedling recruitment rate, because it is most likely to promote diversity and to be identified in observational or experimental data. To understand the prevalence of density dependence, we quantified the number of species with significant positive or negative effects. To understand the strength of density dependence, we determined the magnitude of effects among con- and heterospecifics, and how it changes with overall species abundance. We found that density dependence is pervasive among the 50 species, as the majority of them have significant effects and mostly negative. Density-dependence effects are stronger from conspecific than heterospecfic adult neighbors, consistent with the predictions of the Janzen-Connell hypothesis. Contrary to recent reports, density-dependence effects are more negative for common than rare species, suggesting disproportionately stronger population regulation in common species. We conclude that density dependence is pervasive, and it is strongest from conspecific neighbors of common species. Our analysis provides direct evidence that density dependence reaulates opulation dynamics of tree species in eastern U.S. forests.

Efforts to anticipate threats to biodiversity take the form of species richness predictions (SRPs) based on simple correlations with current climate and habitat area. We review the major approaches that have been used for SRP, species-area curves and climate envelopes, and suggest that alternative research efforts may provide more understanding and guidance for management. Extinction prediction suffers from a number of limitations related to data and the novelty of future environments. We suggest additional attention to (1) identification of variables related to biodiversity that are diagnostic and potentially more predictable than extinction, (2) constraints on species dispersal and reproduction that will determine population persistence and range shifts, including limited sources or potential immigrants for many regions, and (3) changes in biotic interactions and phenology. We suggest combinations of observational and experimental approaches within a framework available for ingesting heterogeneous data sources. Together, these recommendations amount to a shift in emphasis from prediction of extinction numbers to identification of vulnerabilities and leading indicators of change, as well as suggestions for surveillance tools needed to evaluate important variables and the experiments likely to provide most insight.

Understanding population dynamics requires models that admit the complexity of natural populations and the data ecologists obtain from them. Populations possess structure, which may be defined as “fixed” stages through which individuals pass, with superimposed variability among individuals and groups. Data contain missing values and inaccurate censuses. From limited data ecologists attempt to predict life history schedules and population growth. We extend the “missing value” framework for Bayesian analysis of structured populations to admit the heterogeneity in demography and the limitations of data that are typical of ecological populations. Our hierarchical treatment of capture–recapture data allows inference on demographic rates contained in matrix transition models for populations that may be stratified by location and by other variables. Simulations with artificial data sets demonstrate the ability of the Bayesian model to successfully estimate underlying parameters, even with incomplete census data. In contrast, traditional nonhierarchical models may lead to biased parameter estimates because of variation in recapture rates of individuals. Analyses of published demographic data on Common Terns and Taitu Hills rats illustrate the utility of the model. Predictive distributions of maturation age, survivorship, and population growth demonstrate profound impacts of population and data complexity.

Recent models and analyses of paleoecological records suggest that tree populations are capable of rapid migration when climate warms. Fossil pollen is commonly interpreted as suggesting that the range of many temperate tree species expanded at rates of 100–1000 m/yr during the early Holocene. We used chloroplast DNA surveys to show that the geography of postglacial range expansion in two eastern North American tree species differs from that expected from pollen-based reconstructions and from patterns emerging from European molecular studies. Molecular evidence suggests that American beech (Fagus grandifolia) and red maple (Acer rubrum) persisted during the late glaciation as low-density populations, perhaps within 500 km of the Laurentide Ice Sheet. Because populations were closer to modern range limits than previously thought, postglacial migration rates may have been slower than those inferred from fossil pollen. Our estimated rates of

Seed dormancy is assumed to be unimportant for population dynamics of temperate woody species, because seeds occur at low densities and are short lived in forest soils. However, low soil seed densities may result from low seed production, and even modest seed longevity can buffer against fluctuating seed production, potentially limiting density-dependent mortality and ensuring that seeds are available for germination when recruitment success is likely. To investigate whether seed banking affects woody seedling dynamics in the southern Appalachians, we monitored seed rain, seed bank, and seedling densities to (1) determine the prevalence of seed banking among southern Appalachian woody species, (2) quantify annual seed mortality rates for three seed-banking species using a Bayesian statistical approach, and (3) assess whether or not the ability to seed bank affects recruitment rates. We found that the seeds of eight woody taxa ( Acer rubrum, Betula spp., Liriodendron tulipifera, Nyssa sylvatica, Robinia pseudoacacia, Rubus spp., Sassafras al- bidum, and Vitis sp.) remain viable in the soil for more than one year. Seeds of six taxa (Amelanchier spp., Acer pennsylvanicum, Carya spp., Quercus prinus, Quercus rubra, and Tsuga canadensis) were never found in the soil seed bank, despite high seed production and germination. For three species, a substantial proportion of seeds available for germi- nation came from dispersal events two or more years in the past (Acer rubrum 12-37%, Betula spp. 59-73%, Liriodendron tulipifera 40-76%), even though annual seed mortality was high (Acer rubrum 70-98%, Betula spp. 21-81%, Liriodendron tulipifera 12-59%). In years when no seeds fall in local microsites (approximately one in five years), seed banks are the only source of seedling recruitment for these species. Comparing our results to those of previous studies led to valuable insights: first, that seeds of Acer rubrum and Betula spp. suffer high mortality while being incorporated into the seed bank; and second, that seed decay varies greatly over relatively small spatial scales (i.e., within a watershed). Taken together, these results demonstrate that seed banking may play a critical role during woody seedling recruitment in temperate forests.

Population sample data are complex; inference and prediction require proper accommodation of not only the nonlinear interactions that determine the expected future abundance, but also the stochasticity inherent in data and variable (often unobserved) environmental factors. Moreover, censuses may occur sporadically, and observation errors change with sample methods and effort. The state variable (usually density or abundance) may be hidden from view and known only through highly indirect observational schemes (such as public health records, hunting reports, or fossil/archeological surveys). We extend the basic state-space model for time-series analysis to accommodate these dominant sources of variability that influence population data. Using examples, we show how different types of process error and observation error, unequal sample intervals, and missing values can be accounted for within the flexible framework of Bayesian state-space models. We provide algorithms based on Gibbs sampling that can be used to obtain posterior estimates of population states and of model parameters. For models that can be linearized, results can be used for direct sampling of the posterior, including those with missing values and unequal sample intervals. For nonlinear models, we make use of Metropolis-Hastings within the Gibbs sampling framework. Examples derive from long-term census and population data. We illustrate the extension to discrete state variables with multiple stages using a Time- series Susceptible-Infected-Recovered (TSIR) model for mid 20th-century measles infec- tion in London, where birth rates are assumed known, but susceptibles and infected indi- viduals arise from imperfect reporting.

Recent literature on plant population spread advocates quantification of long- distance dispersal (LDD). These estimates could provide insights into rates of migration in response to climate change and rates of alien invasions. LDD information is not available for parameterization of current models because it is hard to obtain. We combine a new stochastic model with a flexible framework that permits assimilation of evidence that might be derived from a range of sources. Results are consistent with the prediction of traditional diffusion that population spread has a finite asymptotic velocity. Unlike traditional diffusion, spread is not well described by the mean; it is erratic. In contrast with deterministic models, our results show that inherent uncertainty, rather than parameter sensitivity, thwarts informative forecasts of spread velocity. Analysis shows that, because LDD is inherently unpredictable, even full knowledge of LDD parameters might not provide informative estimates of velocity for populations characterized by LDD. Although predictive distributions are too broad to provide precise estimates of spread rate, they are valuable for comparing spread potential among species and for identifying potential for invasion. Using combinations of dispersal data and the estimates provided by dispersal biologists that derive from multiple sources, the model predicts spread rates that are much slower than those from traditional (deterministic) fat-tailed models and from simulation models of spread, but for different reasons. Deterministic fat-tailed models overestimate spread rate, because they assume that fractions of individuals can rapidly occupy distant sites. Stochastic models recognize that distant colonization is limited to discrete individuals. Stochastic simulations of plant migration overestimate migration of trees, because they typically assume values of R 0 that are too large.

Spatial heterogeneity in microenvironments may provide unique regeneration niches for trees and may promote forest diversity. We examined how heterogeneity in understory cover, mineral nutrients, and moisture and their interactions with canopy gaps contribute to the coexistence of three common, co-occuring tree species. We measured survival and height growth of 1080 seedlings of Acer rubrum (red maple), Liriodendron tulipifera (yellow poplar), and Quercus rubra (red oak) that were planted in one of five understory treatments: removal of understory vegetation, trenched, trenched plus removal of understory vegetation, fertilization, and a control. Understory treatments were replicated in 12 paired gap and canopy environments. Survivorship varied among species, with Q. rubra having the highest probability of surviving beyond the 1135-day experiment (probability = 0.64), followed by A. rubrum (probability = 0.27) and L. tulipifera (probability = 0.07). Although canopy gaps and understory treatments had large effects on survivorship, species survival rankings changed little across microenvironments; Q. rubra had the highest survival in all microenvironments. In contrast to survival, L. tulipifera had a relative growth rate for height that was three times greater than that of A. rubrum and Q. rubra in high-resource microenvironments. There was broad overlap among species in relative growth rates in the remaining seven microenvironments, with no clear top-ranked species. Differences in seedling growth and survival across these 10 microenvironments may contribute to the coexistence of two of the three species studied, L. tulipifera and Q. rubra, but not A. rubrum. Q. rubra had higher survival than A. rubrum and L. tulipifera in all microenvironments, but L. tulipifera tended to grow faster than A. rubrum and Q. rubra in high-resource microenvironments. Despite the generally poor performance of A. rubrum, it was the only surviving species in some quadrats at the end of the experiment, indicating that stochastic effects, in conjunction with broad niche overlap, may also contribute to species coexistence. The importance of stochastic effects will probably increase when differential fecundity across these three species is considered because the high fecundity of A. rubrum offsets survival and growth disadvantages of its seedlings through their greater total abundance.

Estimates of uncertainty are the basis for inference of population risk. Uncertainty is estimated from models fitted to data that typically include a deterministic model (e.g., population growth) and stochastic elements, which should accommodate errors in sampling and any sources of variability that affect observations. Prediction from fitted models (of, say, demography) to new variables (say, population growth) requires propagation of these stochastic elements. Ecological models ignore most forms of variability, because they make statistical models complex, and they pose computational challenges. Variability associated with space, time, and among individuals that is not accommodated by demographic models can make parameter estimates and growth rate predictions unrealistic. I adapt a hierarchical approach to the problem of estimating population growth rates and their uncertainties when individuals vary and that variability cannot be assigned to specific causes. In contrast to an overfitted model that would assign a different parameter value to each individual, hierarchical models accommodate individual differences, but assume that those differences derive from an underlying distribution—they belong to a “population.” The hierarchical model can be implemented in classical (frequentist) and Bayesian frameworks (I demonstrate both) and analyzed using Markov chain Monte Carlo simulation. Results show that population growth models that rely on standard propagation of estimation error but ignore variability among individuals can misrepresent uncertainties in ways that erode credibility. Corresponding Editor: O. N. Bjornstad.

Many standard statistical models used to examine population dynamics ignore significant sources of stochasticity. Usually only process error is included, and uncertainty due to errors in data collection is omitted or not directly specified in the model. We show how standard time-series models for population dynamics can be extended to include both observational and process error and how to perform inference on parameters in these models in the Bayesian setting. Using simulated data, we show how ignoring observation error can be misleading. We argue that the standard Bayesian techniques used to perform inference, including freely available software, are generally applicable to a variety of time-series models. Corresponding Editor: O. N. Bjornstad.

Analyses of growth response to resource availability are the basis for inter- preting whether trophic trade-offs contribute to diversity. If different species respond most to resources that are limiting at different times, then those differences may trade off with other trophic or life-history traits that, together, help to maintain diversity. The statistical models used to infer trophic differences do not accommodate uncertainty in resources and variability in how individuals use resources. We provide hierarchical models for resource- growth responses that accommodate stochasticity in parameters and in data, despite the fact that causes are typically unknown. A complex joint posterior distribution taken over > 102 parameters is readily integrated to provide a comprehensive accounting of uncertainty in the growth response, together with a small number of hyperparameters that summarize the population response. An application involving seedling growth response to light avail- ability shows that large trophic differences among species suggested by traditional models can be an artifact of the assumption that all individuals respond identically. The hierarchical analysis indicates broad trophic overlap, with the implication that slow dynamics play a more important role in preserving diversity than is widely believed.

Widespread drought is among the most likely and devastating consequences of future global change. Assessment of drought impacts forecast by atmospheric models requires an understanding of natural drought variability, especially under conditions more arid than today. Using high-resolution lake-sediment records from the northern Great Plains, we show pronounced 100- to 130-yr drought cycles during the arid middle Holocene (8000 calendar yr BP). During drought phases, grass productivity declined, erosion and forbs increased, and fuel limitation reduced fire importance. Intervening humid decades saw grass production rise, with stabilization of soils and renewed fire as fuels became abundant. Although both C3 and C4 grasses declined during droughts, a lasting shift to C3 dominance occurred during a single drought -8200 calendar yr BP. During the more humid Late Holocene (2800 calendar yr BP), climate was less variable and without evident drought cyclicity. Consequently, drought severity during past, and possibly future, arid phases cannot be anticipated from the attenuated climate variability evident during contemporary humid phases. Our study demonstrates that agriculturally important grassland ecosystems respond sensitively to drought variability, uncertainty in which has profound implications for the future of these ecosystems.

To determine how grassland, woodland, and bordering forests respond to increased aridity, we used paleoecological methods to examine past responses along a transect of three sites at the eastern boundary of the Northern Plains of North America. Our study region corresponds to the confluence of three air streams that control central North American climates and, hence, should be sensitive to climate change. Sediment cores were analyzed for evidence of Holocene vegetation and fire from tall-grass prairie in eastern North Dakota (Moon Lake), from mixed forest near the prairie border in northwestern Minnesota (Deming Lake), and from mixed forest more remote from prairie in western Wisconsin (Dark Lake). Together with pollen and charcoal analysis, we present a new method for determining δ13C of terrestrial (charred) organic matter and, thus, the relative importance of C3 and C4 photosynthetic pathways in past vegetation. Paleorecords were supplemented with surface charcoal accumulation and δ13C from 21 North American lakes that span boreal, deciduous, pine, and mixed forest to tall- and mixed-grass prairie. Surface charcoal and δ13C follow vegetation and climate gradients, with high charcoal accumulation and δ13C (−20‰) in the Plains (Dakotas, Nebraska, and southwest Minnesota) and decreases to the east, west, and north. The δ13C pattern is consistent with observed patterns of C3:C4 dominance across the region. Sediment, pollen, charcoal, and terrestrial δ13C show that vegetation response to climate change varied substantially among tall-grass prairie, bordering woodland, and forest. During maximum aridity (8000;nd4000 yr BP) prairie vegetation in eastern North Dakota showed a demise of woody vegetation followed by a fluctuating dominance of grasses (40% C4) and forbs. Meanwhile, prairie expanded eastward into northwestern Minnesota, where it produced a shifting dominance between mostly C4 grasses and woody vegetation until more humid conditions and mixed forest developed after 4000 yr BP. Mixed forest in southwestern Wisconsin showed little response to mid-Holocene aridity. Elevated δ13C values from 5000 to 3000 yr BP suggest that composition of grasses changed (to increased C4), although pollen data indicate that the total abundance of grasses remained constant. The increase in C4 grasses at this time is consistent with previous studies suggesting a delayed dry interval in eastern Iowa. Reduced aridity of the last 2000 yr brought increased fire to tall-grass prairie as higher primary productivity led to increased fuel load. Meanwhile, forest expanded in northwestern Minnesota, leading to decreased ignition and fine fuels, in turn resulting in decreased fire at the woodland margin.

Dispersal affects community dynamics and vegetation response to global change. Understanding these effects requires descriptions of dispersal at local and regional scales and statistical models that permit estimation. Classical models of dispersal describe local or long-distance dispersal, but not both. The lack of statistical methods means that models have rarely been fitted to seed dispersal in closed forests. We present a mixture model of dispersal that assumes a range of disperal patterns, both local and long distance. The bivariate Student’s t or “2Dt” follows from an assumption that the distance parameter in a Gaussian model varies randomly, thus having a density of its own. We use an inverse approach to “compete” our mixture model against classical alternatives, using seed rain databases from temperate broadleaf, temperate mixed-conifer, and tropical floodplain forests. For most species, the 2Dt model fits dispersal data better than do classical models. The superior fit results from the potential f...

Author Posting. © Ecological Society of America, 2008. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecology 89 (2008): 585–587, doi:10.1890/07-0660.1.

Annual records of charcoal and sedimentation rate were compared with fossil pollen to investigate the role of fire in eastern deciduous forest around Devil's Bathtub, New York, USA. Changes in peak and background charcoal suggest that changes in fire regime have accompanied the principal vegetation and climatic changes of the last 10400 yr. A distribution of return times (50-200-yr intervals) similar to parts of modern boreal Canada prevailed when late-Glacial spruce woodland dominated the site. Expansion of Pinus banksiana appears to have altered the fire regime to one of crown fires with high particulate emissions, but return intervals similar to those of the preceding Picea forest. Expansion of Pinus strobus might be linked to change in fire occurrence, but the broad dispersal of Pinus pollen makes interpretation difficult. If Pinus strobus expansion around the site is reflected in its pollen curve, then that expansion coincides with a time of frequent fire. Alternatively, if increasing pollen abundance precedes the local expansion of trees, as has been observed elsewhere, then local expansion might correspond to an abrupt decline in fire frequency and in regional importance of fire. An abrupt decline in background charcoal follows a fire and coincides (? 100 yr) with the expansion of hardwood taxa such as Fagus. The decline in background charcoal occurs over several years, suggesting that it may be linked to effects of hardwood expansion on fuels. Fires do not appear to have occurred during the time of hardwood dominance, suggesting that fire may not be an explanation for maintenance of species diversity in this deciduous forest. However, frequent occurrence of thick varves during the latter half of the Holocene suggests that the frequency of other types of disturbance may have increased.

Host-specific mortality driven by natural enemies is a widely discussed mechanism for explaining plant diversity. In principle, populations of plant species can be regulated by distinct host-specific natural enemies that have weak or nonexistent effects on heterospecific competitors, preventing any single species from becoming dominant and thus promoting diversity. Two of the first steps in exploring the role of natural enemies in diversity regulation are to (1) identify potential enemies and (2) evaluate their levels of host specificity by determining if interactions between any one host and its enemy have equivalent survival impacts on co-occurring host species. We developed a bioinformatics framework to evaluate impacts of potential pathogens on seedling survival, for both single and multiple infections. Importantly, we consider scenarios not only if there are specialist pathogens for each plant, but also when generalist pathogens have differential effects on multiple host species, and when co-infection has species-specific effects. We then applied this analytical framework to a field experiment using molecular techniques to detect potential fungal pathogens on co-occurring tree seedling hosts. Combinatorial complexity created by 160 plant-fungus interactions was reduced to eight combinations that affect seedling survival. Potential fungal pathogens had broad host ranges, but seedling species were each regulated by different combinations of fungi or by generalist fungi that had differential effects on multiple plant species. Soil moisture can have the potential to shift the nature of the interactions in some plant-fungal combinations from neutral to detrimental. Reassessing the assumption of single-enemy-single-host interactions broadens the mechanisms through which natural enemies can influence plant diversity.

Species employ diverse strategies to cope with natural disturbance, but the importance of these strategies for maintaining tree species diversity in forests has been debated. Mechanisms that have the potential to promote tree species coexistence in the context of repeated disturbance include life history trade-offs in colonization and competitive ability or in species' ability to survive at low resource conditions and exploit the temporary resource-rich conditions often generated in the wake of disturbance (successional niche). Quantifying these trade-offs requires long-term forest monitoring and modeling. We developed a hierarchical Bayes model to investigate the strategies tree species employ to withstand and recover from hurricane disturbance and the life history trade-offs that may facilitate species coexistence in forests subject to repeated hurricane disturbance. Unlike previous approaches, our model accommodates temporal variation in process error and observations from multiple sources. We parameterized the model using growth and mortality data from four censuses of a 16-ha plot taken every five years (1990–2005), together with damage data collected after two hurricanes and annual seed production data (1992–2005). Species' susceptibilities to hurricane damage as reflected by changes in diameter growth and fecundity immediately following a storm were weak, highly variable, and unpredictable using traditional life history groupings. The lower crowding conditions (e.g., high light) generated in the wake of storms, however, led to greater gains in growth and fecundity for pioneer and secondary-forest species than for shade-tolerant species, in accordance with expectation of life history. We found moderate trade-offs between survival in high crowding conditions, a metric of competitive ability, and long-distance colonization. We also uncovered a strong trade-off between mean species fecundity in low crowding conditions, a metric of recovery potential, and competitive ability. Trade-offs in competitive and colonization ability, in addition to successional niche processes, are likely to contribute to species persistence in these hurricane-impacted forests. The strategies species employ to cope with hurricane damage depend on the degree to which species rely on sprouting, repair of adult damage, changes in demographic rates in response to enhanced resource availability after storms, or long-distance dispersal as recovery mechanisms.

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The development of an analytical theory of plant demography has been frustrated by problems of complexity related to spatial heterogeneity in population structure and limiting resource (Schaffer and Leigh 1976, Pacala and Silander 1985, Pacala 1988), asymmetric competition (large individuals control a disproportionate share of the resource base) (Harper 1977, Weiner 1985, 1986), and time-dependent parameters such as birth and death rates (Clark 1991a). The problem of applying to plants the population models developed for mobile organisms, such as animals or plankton in chemostats, has long been recognized (Schaffer and Leigh 1976, Pacala 1989). Plant recruitment, growth, and mortality respond to extremely localized resource levels rather than to landscape averages.

An analytical model of disturbance and plant population dynamics is developed to explore the optimal life history for a plant within a "shifting mosaic" meta—population. The population dynamics consist of short—lived recruitment events followed by longer intervals of thinning. Plants balance costs and benefits of delayed maturation time that result from cohort thinning, a correlation between maturation time and longevity, and the distribution of recruitment events in space and time. Two fundamentally different responses to disturbance are explored: (1) the plant is killed by the disturbance that allows for new recruitment (type A response), and (2) the plant may survive many such disturbances (type B response). Species maximize either the probability of being reproductively mature at the time of the next recruitment opportunity (type A) or the total number of recruitment opportunities to occur during the period of reproductive maturity (type B). Predictions of the theory are compared with the actual life histories of trees that occur in different disturbance regimes. The costs and benefits associated with delayed maturation from an energy standpoint must be weighed against the probability that a recruitment opportunity (disturbance) will occur at a particular age. Trees subject to low thinning rates should reach reproductive maturity t1 at t1 ° 0.4 x (expected disturbance interval in years). At high thinning rates, this optimum is t1 ° 0.4/(mortality rate per year). Disturbance probabilities that increase with time since the last disturbance select for maturation times that are greater than these values. Species that are not killed by disturbances have optimal maturation times that are independent of disturbance frequency. However, when such species are susceptible as juveniles, optimal maturation time does depend on disturbance frequency. This optimum maturation time is still greater than it is for the case of a mortality response (type A) to disturbance, but less than the case of no susceptibility period (type B). Application of the theory to real—world disturbance regimes results in predictions that closely match the life histories of species that actually occur there. The optimal maturation time for a gap species in temperate North American forests is 30—60 yr, a value that agrees with observed maturation time. A second test involved fire regimes where two species having very different responses to fire and life histories co—occur, Pinus resinosa and P. banksiana. The maturation times of these species both match the predicted optima for a species that survives fire (P. resinosa) vs. one that is killed by fire (P. banksiana) subjected to identical fire regimes. Different modes of dispersal are predicted to have important effects on reproductive potential, but little effect on the optimal maturation time. Application of the models to these actual cases is consistent with that prediction. The "intermediate" disturbance is predicted to be that which implies the optimum life history that coincides with the life histories of the greatest number of species.