[Show abstract][Hide abstract] ABSTRACT: Understanding how ecological communities are organized and how they change through time is critical to predicting the effects of climate change. Recent work documenting the co-occurrence structure of modern communities found that most significant species pairs co-occur less frequently than would be expected by chance. However, little is known about how co-occurrence structure changes through time. Here we evaluate changes in plant and animal community organization over geological time by quantifying the co-occurrence structure of 359,896 unique taxon pairs in 80 assemblages spanning the past 300 million years. Co-occurrences of most taxon pairs were statistically random, but a significant fraction were spatially aggregated or segregated. Aggregated pairs dominated from the Carboniferous period (307 million years ago) to the early Holocene epoch (11,700 years before present), when there was a pronounced shift to more segregated pairs, a trend that continues in modern assemblages. The shift began during the Holocene and coincided with increasing human population size and the spread of agriculture in North America. Before the shift, an average of 64% of significant pairs were aggregated; after the shift, the average dropped to 37%. The organization of modern and late Holocene plant and animal assemblages differs fundamentally from that of assemblages over the past 300 million years that predate the large-scale impacts of humans. Our results suggest that the rules governing the assembly of communities have recently been changed by human activity.
[Show abstract][Hide abstract] ABSTRACT: There is an urgent need to understand species and community responses to climatic and ecological changes to predict biodiversity patterns given anticipated global change. The current distribution of species and the environment provide a limited perspective to study and predict ecological responses; therefore, biodiversitv responses to past environmental changes must be examined The rapid development of ecological niche models (ENMs) and their use in reconstructing past species distributions has facilitated inclusion of past observations into predictive models. Paleodata offer an opportunity to test the predictive ability of ENMs and their underlying assumptions. However, paleodata remain underutilized despite the rapidly growing field of paleoinformatics. New modeling methods that incorporate species associations, coupled with paleodata, provide more robust approaches to studying species and community responses, especially given the predicted emergence of no-analog climates and communities in the future.
No preview · Article · Dec 2015 · Annual Review of Ecology Evolution and Systematics
[Show abstract][Hide abstract] ABSTRACT: Community ecology and paleoecology are both concerned with the composition and structure of biotic assemblages but are largely disconnected. Community ecology focuses on existing species assemblages and recently has begun to integrate history (phylogeny and continental or intercontinental dispersal) to constrain community processes. This division has left a "missing middle": Ecological and environmental processes occurring on timescales from decades to millennia are not yet fully incorporated into community ecology. Quaternary paleoecology has a wealth of data documenting ecological dynamics at these timescales, and both fields can benefit from greater interaction and articulation. We discuss ecological insights revealed by Quaternary terrestrial records, suggest foundations for bridging between the disciplines, and identify topics where the disciplines can engage to mutual benefit.
Preview · Article · Apr 2015 · Proceedings of the National Academy of Sciences
[Show abstract][Hide abstract] ABSTRACT: Aim: Fossil records are being increasingly used to help understand the consequences of climate change for biodiversity. Pollen records from the late Quaternary are among the most commonly used fossil data, but pollen-based inferences of biodiversity can potentially be confounded by spatial and taxonomic uncertainties and the influence of non-climatic abiotic factors such as soils on vegetation–climate relationships. Using paired pollen and vegetation inventories, we assess the fidelity of pollen-based estimates of compositional turnover of vegetation along environmental gradients given various sources of uncertainty.
Location: Eastern United States.
Methods: We used modern pollen records and forest composition data from Forest Inventory and Analysis (FIA) plots to fit generalized dissimilarity models. To address how uncertainties in pollen records affect estimates of turnover, we coarsened the vegetation data spatially from individual plots to 10- and 30-arcmin resolution and taxonomically from species to genus. To determine whether soil properties influenced turnover, we used deviance partitioning between models including climate or soil variables versus models with a combination of both.
Results: Pollen-based estimates of turnover were highly correlated with those based on FIA data, but tended to be lower, mainly due to differences in taxonomic resolution and secondarily to differences in spatial resolution. Neither spatial nor taxonomic uncertainty substantially reduced the correlation between pollen- and FIA-based estimates of turnover. FIA data best matched pollen records when they were aggregated to genus and 30-arcmin resolution. Vegetation–climate relationships were similar across datasets, although models sometimes differed. The influence of soil variables was negligible compared with climate variables and did not improve model fit. Pollen thresholds did not greatly affect the form and strength of pollen–vegetation relationships.
Main conclusions: Pollen can act as a robust proxy for vegetation turnover, thereby supporting the use of pollen-based estimates of turnover to predict temporal changes in vegetation.
No preview · Article · Mar 2015 · Global Ecology and Biogeography
[Show abstract][Hide abstract] ABSTRACT: Background/Question/Methods
Background: Marine lakes are variously isolated bodies of water that formed after the Last Glacial Maximum as rising seas flooded inland valleys. Marine lakes provide novel opportunities for testing island theory that was developed primarily in terrestrial settings, such as the species-area relationship of the classical Equilibrium Model, and patterns of evolution such as the Island Rule. They also offer two perspectives on the emerging General Dynamic Model of oceanic island biogeography. On the one hand, marine lakes of different depths are different ages and therefore may represent modern analogs of stages in the formation of individual lakes; modern shallow lakes representing inception and early-developmental stages, and modern deep lakes representing mature stages; some shallow lakes may be filling-in and represent senescent stages. On the other hand, the sediment deposited in each lake may hold a record of thousands of years of community assembly, dynamics, and disassembly.
Questions: We are exploring how local and regional, biotic and abiotic, deterministic and stochastic processes, influence taxonomic, genetic, and functional diversity, and how these culminate in shared or unique attributes of modern communities.
Methods: Between 2003-2013 we inventoried microbes, macroinvertebrates, phytoplankton and fishes, and measured abiotic characteristics of marine lakes in Palau. We also collected sediment cores of up to 12 m length from 8 of these lakes, for which we are constructing age models and analyzing biotic proxies, biolipids, and micro- and macro-fossils. Here, we report on patterns in modern community diversity across 16 lakes, and preliminary analyses of community similarity through time within several lakes.
Species diversity in modern marine lakes is, in general, consistent with species-richness relationships such as the SAR. However, the relationship breaks down when considering lakes that are far inland and stratified: microbial diversity is elevated, and teleost diversity is reduced. These lakes appear to provide  novel categories of dysoxic and anoxic environments for microbes and  dramatically less oxygenated habitat for fishes than would be suggested simply by area or other metrics of overall lake-size. We discuss the potential for observing these transitions and corresponding effects on diversity through time using a combination of ITRAX, lipid biomarkers, and benthic macrofossils.
[Show abstract][Hide abstract] ABSTRACT: Background/Question/Methods
Predicting the response of biotic systems to environmental change remains one of the greatest challenges in ecology. During the last decade, studies have emphasized the use of species distribution models (SDMs) to predict climate-driven shifts in species distributions and extinction risk. SDMs usually are fit using only abiotic factors, even though other factors can modify the strength and/or the direction of abiotic drivers. Among these factors, biotic interactions are known to play a key role in determining species’ responses to climate change but the implementation of such interactions in SDMs remains limited. Alternative approaches to SDMs are multivariate tools that simultaneously model community structure and composition. These community level models (CLMs) can provide more reliable predictions for species distributions and community structure by accounting for patterns of co-occurrence (and ostensibly biotic interactions) between species in the model. However, this capacity remains largely unexplored. Using observed changes in plant associations (as recorded in fossil pollen records) in eastern North America and independent paleoclimate simulations, we tested the ability of five CLMs to forecast species distributions, species associations, and macroecological patterns across time. Specifically, we fit CLMs with current presence-absence data and hindcasted at 500-year intervals until 21 ka BP.
Among the five CLMs, vector generalized additive model (VGAM) and vector Generalized Linear Model (VGLM) best predicted pollen taxon distribution, community composition and taxon richness (mean AUC: 0.85; mean Jaccard index between observed and predicted communities: 0.42; mean correlation between predicted and observed species richness: 0.75). In contrast, neural network (NNET) and classification and regression trees (CARTs) performed the worst (AUC: 0.75; Jaccard index: 0.55; species richness correlation: 0.5). However, multivariate adaptive regression spline (MARS) and NNET offered the best estimates of beta diversity (Sorensen index). All CLMs predictions were independent of species prevalence and their predictive ability decreased backwards through time. Projections remained reliable (AUC > 0.75) until the mid-Holocene (7 ka BP), decreased from 7 to 11 ka BP, and then remained very unreliable (AUC ~ 0.6) through the whole Pleistocene (from 11 ka to 21 ka BP). Our results show that predictive performance of CLMs differed between algorithms and consistently declined as climate and community dissimilarity with present increased. Ongoing research is comparing these results to SDMs to determine the extent to which CLM may complement, or represent an alternative to, species-level modeling.
[Show abstract][Hide abstract] ABSTRACT: Background/Question/Methods
Potential negative impacts of the high rate and magnitude of future climate change on biodiversity are of increasing concern to the conservation and biogeography communities. In response, many studies aim to predict the distributions of species based on scenarios of future climate change, and from these predictions, estimate other biodiversity properties such as species richness or extinction risk. Many such models use only climatic or habitat variables as predictors, but other factors such as dispersal lags and interactions between species can greatly influence the distributions of species and communities through time and across space. However, quantifying the relative influence of climate or habitat, dispersal limitation, and biotic interactions on species and communities is not straightforward. Here, I use species lists from late Quaternary fossil localities in North America paired with downscaled paleoclimate simulations to assess potential causes of species and community changes across space and time. Using complementary analyses such as generalized dissimilarity modeling, analyses of species pairs, and species distribution modeling, I focus in particular on disentangling the relative contributions of climate versus other mechanisms of change.
Climate strongly structures both species distributions and community attributes across space and time at broad spatial and temporal scales. Generalized dissimilarity models show that climate influences dissimilarity within fossil pollen assemblages across eastern North America and that communities are structured similarly across spatial and temporal climate gradients. Additionally, most of the non-randomly associated species pairs can be explained by climatic or spatial attributes of sites (mean = 83% of the aggregated pairs and 93% of the segregated pairs across all time slices), and biotic interaction is not the most parsimonious explanation of the non-random species associations. However, the influence of climate is variable across space and time. In the latest Pleistocene, climate explains less variation in community dissimilarity than in the Holocene, indicating that dispersal limitation or species interactions may be more important. Additionally, most non-random species associations that are potentially attributed to a biotic interaction occur during the latest Pleistocene. This implies that, at least at broad scales, climate-based models are relatively good for predicting changes in species and communities. However, care needs to be taken when predicting changes far into the future or across large magnitude climate changes, when there is greater potential for no-analog conditions.
[Show abstract][Hide abstract] ABSTRACT: Climate refugia, locations where taxa survive periods of regionally adverse climate, are thought to be critical for maintaining biodiversity through the glacial–interglacial climate changes of the Quaternary. A critical research need is to better integrate and reconcile the three major lines of evidence used to infer the existence of past refugia – fossil records, species distribution models and phylogeographic surveys – in order to characterize the complex spatiotemporal trajectories of species and populations in and out of refugia. Here we review the complementary strengths, limitations and new advances for these three approaches. We provide case studies to illustrate their combined application, and point the way towards new opportunities for synthesizing these disparate lines of evidence. Case studies with European beech, Qinghai spruce and Douglas-fir illustrate how the combination of these three approaches successfully resolves complex species histories not attainable from any one approach. Promising new statistical techniques can
capitalize on the strengths of each method and provide a robust quantitative reconstruction of species history. Studying past refugia can help identify contemporary refugia and clarify their conservation significance, in particular by elucidating the fine-scale processes and the particular geographic locations that buffer species against rapidly changing climate.
[Show abstract][Hide abstract] ABSTRACT: Environmental conditions, dispersal lags, and interactions among species are major factors structuring communities through time and across space. Ecologists have emphasized the importance of biotic interactions in determining local patterns of species association. In contrast, abiotic limits, dispersal limitation, and historical factors have commonly been invoked to explain community structure patterns at larger spatiotemporal scales, such as the appearance of late Pleistocene no-analog communities or latitudinal gradients of species richness in both modern and fossil assemblages. Quantifying the relative influence of these processes on species co-occurrence patterns is not straightforward. We provide a framework for assessing causes of species associations by combining a null-model analysis of co-occurrence with additional analyses of climatic differences and spatial pattern for pairs of pollen taxa that are significantly associated across geographic space.We tested this framework with data on associations among 106 fossil pollen taxa and paleoclimate simulations from eastern North America across the late Quaternary. The number and proportion of significantly associated taxon pairs increased over time, but only 449 of 56 194 taxon pairs were significantly different from random. Within this significant subset of pollen taxa, biotic interactions were rarely the exclusive cause of associations. Instead, climatic or spatial differences among sites were most frequently associated with significant patterns of taxon association. Most taxon pairs that exhibited co-occurrence patterns indicative of biotic interactions at one time did not exhibit significant associations at other times. Evidence for environmental filtering and dispersal limitation was weakest for aggregated pairs between 16 and 11 kyr BP, suggesting enhanced importance of positive species interactions during this interval. The framework can thus be used to identify species associations that may reflect biotic interactions because these associations are not tied to environmental or spatial differences. Furthermore, temporally repeated analyses of spatial associations can reveal whether such associations persist through time.
[Show abstract][Hide abstract] ABSTRACT: As the earth system moves to a novel state, model systems (experimental, observational, paleoecological) are needed to assess and improve the predictive accuracy of ecological models under environments with no contemporary analog. In recent years, we have intensively studied the no-analog plant associations and climates in eastern North America during the last deglaciation to better constrain their spatiotemporal distribution, test hypotheses about climatic and megaherbivory controls, and assess the accuracy of species-and community-level models. The formation of no-analog plant associations was asynchronous, beginning first in the south-central United States; at sites in the north-central United States, it is linked to declining megafaunal abundances. Insolation and temperature were more seasonal than present, creating climates currently nonexistent in North America, and shifting species–climate relationships for some taxa. These shifts pose a common challenge to empirical paleoclimatic reconstructions, species distribution models (SDMs), and conservation–optimization models based on SDMs. Steps forward include combining recent and paleoecological data to more fully describe species' fundamental niches, employing community-level models to model shifts in species interactions under no-analog climates, and assimilating paleoecological data with mechanistic ecosystem models. Accurately modeling species interactions under novel environments remains a fundamental challenge for all forms of ecological models.
Full-text · Article · Sep 2013 · Annals of the New York Academy of Sciences
[Show abstract][Hide abstract] ABSTRACT: Biotic interactions drive key ecological and evolutionary processes and mediate ecosystem responses to climate change. The direction, frequency, and intensity of biotic interactions can in turn be altered by climate change. Understanding the complex interplay between climate and biotic interactions is thus essential for fully anticipating how ecosystems will respond to the fast rates of current warming, which are unprecedented since the end of the last glacial period. We highlight episodes of climate change that have disrupted ecosystems and trophic interactions over time scales ranging from years to millennia by changing species' relative abundances and geographic ranges, causing extinctions, and creating transient and novel communities dominated by generalist species and interactions. These patterns emerge repeatedly across disparate temporal and spatial scales, suggesting the possibility of similar underlying processes. Based on these findings, we identify knowledge gaps and fruitful areas for research that will further our understanding of the effects of climate change on ecosystems.
[Show abstract][Hide abstract] ABSTRACT: "Space-for-time" substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption-that drivers of spatial gradients of species composition also drive temporal changes in diversity-rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as "time-for-time" predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.
Full-text · Article · May 2013 · Proceedings of the National Academy of Sciences
[Show abstract][Hide abstract] ABSTRACT: Projecting the future composition and function of communities is a major challenge, and there is an urgent need to develop, improve, and test the predictive capacity of ecological models under different climate states. We tested the effect of climate on spatial patterns of plant community composition over the past 21 000 yr, focusing on whether the spatial relationships between environmental distance and compositional dissimilarity are stable over time. We used a network of fossil-pollen sites in eastern North America, combined with paleoclimate simulations from the Last Glacial Maximum (LGM; 21 000 calibrated years before present, 21 kyr BP) to the present. We modeled relationships between climate, geography, and compositional dissimilarity at 1 kyr periods using generalized dissimilarity modeling (GDM) and determined the strongest predictors of compositional dissimilarity. We assessed the performance of models calibrated for one time period (e.g. 14 kyr BP) in predicting patterns in the same period as well as at other times (e.g. 12 kyr BP), and tested whether predictive performance was related to the magnitude of climate change between the calibration and prediction time periods. Finally, we examined whether pooling data from multiple time periods improved predictive performance. Models explained 32 to 51% of compositional dissimilarity between locations within any single time period. The best set of predictors changed across time, with summer temperature and geographic distance the strongest predictors of compositional dissimilarity for most time periods. Models built for one time period explained turnover during nearby time periods relatively well, but performance decayed across time and with increasing climate change. Results were similar regardless of whether models were projected forward or backward through time, and did not improve when data were pooled across time. GDM predicts well the spatial patterns of past compositional dissimilarity and holds promise for modeling the drivers of compositional dissimilarity across space and time. However, the modeled relationships between compositional turnover and environmental distance are non-stationary, so caution is needed when predicting across periods of significant climatic change.
[Show abstract][Hide abstract] ABSTRACT: Data, whether images, measurements, counts, occurrences, or character codings, are a cornerstone of vertebrate paleontology. Every published paper,master’s thesis, and doctoral dissertation relies on these data to document patterns and processes in evolution, ecology, taphonomy, geography, geologic time, and functional morphology, to name just a few. In turn, the vertebrate paleontology community relies on published data in order to reproduce and verify others’ work, as well as to expand upon published analyses in new ways without having to reconstitute data sets that have been used by earlier authors and to accurately preserve data for future generations of researchers. Here, we review several databases that are of interest to vertebrate paleontologists and strongly advocate for more deposition of basic research data in publicly accessible databases by vertebrate paleontologists.
Full-text · Article · Jan 2013 · Journal of Vertebrate Paleontology
[Show abstract][Hide abstract] ABSTRACT: Age–depth relationships in sedimentary archives such as lakes, wetlands and bogs are non-linear with irregular probability distributions associated with calibrated radiocarbon dates. Bayesian approaches are thus well-suited to understanding relationships between age and depth for use in paleoecological studies. Bayesian models for the accumulation of sediment and organic matter within basins combine dated material from one or more records with prior information about the behavior of deposition times (yr/cm) based on expert knowledge. Well-informed priors are essential to good modeling of the age–depth relationship, but are particularly important in cases where data may be sparse (e.g., few radiocarbon dates), or unclear (e.g., age-reversals, coincident dates, age offsets, outliers and dates within a radiocarbon plateau).
Full-text · Article · Aug 2012 · Quaternary Science Reviews
[Show abstract][Hide abstract] ABSTRACT: Empirically derived species distributions models (SDMs) are increasingly relied upon to forecast species vulnerabilities to future climate change. However, many of the assumptions of SDMs may be violated when they are used to project species distributions across significant climate change events. In particular, SDM's in theory assume stable fundamental niches, but in practice, they assume stable realized niches. The assumption of a fixed realized niche relative to climate variables remains unlikely for various reasons, particularly if novel future climates open up currently unavailable portions of species fundamental niches. To demonstrate this effect, we compare the climate distributions for fossil-pollen data from 21 to 15 ka bp (relying on paleoclimate simulations) when communities and climates with no modern analog were common across North America to observed modern pollen assemblages. We test how well SDMs are able to project 20th century pollen-based taxon distributions with models calibrated using data from 21 to 15 ka. We find that taxa which were abundant in areas with no-analog late glacial climates, such as Fraxinus, Ostrya/Carpinus and Ulmus, substantially shifted their realized niches from the late glacial period to present. SDMs for these taxa had low predictive accuracy when projected to modern climates despite demonstrating high predictive accuracy for late glacial pollen distributions. For other taxa, e.g. Quercus, Picea, Pinus strobus, had relatively stable realized niches and models for these taxa tended to have higher predictive accuracy when projected to present. Our findings reinforce the point that a realized niche at any one time often represents only a subset of the climate conditions in which a taxon can persist. Projections from SDMs into future climate conditions that are based solely on contemporary realized distributions are potentially misleading for assessing the vulnerability of species to future climate change.
No preview · Article · May 2012 · Global Change Biology
[Show abstract][Hide abstract] ABSTRACT: Deciphering the evolution of global climate from the end of the Last Glacial Maximum approximately 19 ka to the early Holocene 11 ka presents an outstanding opportunity for understanding the transient response of Earth's climate system to external and internal forcings. During this interval of global warming, the decay of ice sheets caused global mean sea level to rise by approximately 80 m; terrestrial and marine ecosystems experienced large disturbances and range shifts; perturbations to the carbon cycle resulted in a net release of the greenhouse gases CO(2) and CH(4) to the atmosphere; and changes in atmosphere and ocean circulation affected the global distribution and fluxes of water and heat. Here we summarize a major effort by the paleoclimate research community to characterize these changes through the development of well-dated, high-resolution records of the deep and intermediate ocean as well as surface climate. Our synthesis indicates that the superposition of two modes explains much of the variability in regional and global climate during the last deglaciation, with a strong association between the first mode and variations in greenhouse gases, and between the second mode and variations in the Atlantic meridional overturning circulation.
Full-text · Article · Feb 2012 · Proceedings of the National Academy of Sciences
[Show abstract][Hide abstract] ABSTRACT: In Quaternary paleoecology and paleoclimatology, compositionally
dissimilar fossil assemblages usually indicate dissimilar environments;
this relationship underpins assemblage-level techniques for
paleoenvironmental reconstruction such as mutual climatic ranges or the
modern analog technique. However, there has been relatively little
investigation into the form of the relationship between compositional
dissimilarity and climatic dissimilarity. Here we apply generalized
dissimilarity modeling (GDM; Ferrier et al. 2007) as a tool for modeling
the expected non-linear relationships between compositional and climatic
dissimilarity. We use the CCSM3.0 transient paleoclimatic simulations
from the SynTrace working group (Liu et al. 2009) and a new generation
of fossil pollen maps from eastern North America (Blois et al. 2011) to
1) assess the spatial relationships between compositional dissimilarity
and climatic dissimilarity and 2) whether these spatial relationships
change over time. We used a taxonomic list of 106 genus-level pollen
types, six climatic variables (winter precipitation and mean
temperature, summer precipitation and temperature, seasonality of
precipitation, and seasonality of temperature) that were chosen to
minimize collinearity, and a cross-referenced pollen and climate dataset
mapped for time slices spaced 1000 years apart. When GDM was trained for
one time slice, the correlation between predicted and observed spatial
patterns of community dissimilarity for other times ranged between 0.3
and 0.73. The selection of climatic predictor variables changed over
time, as did the form of the relationship between compositional turnover
and climatic predictors. Summer temperature was the only variable
selected for all time periods. These results thus suggest that the
relationship between compositional dissimilarity in pollen assemblages
(and, by implication, beta diversity in plant communities) and climatic
dissimilarity can change over time, for reasons to be further studied.