Resource competition and coexistence in heterogeneous metacommunities: many-species coexistence is unlikely to be facilitated by spatial variation in resources

There is little debate about the potential of environmental heterogeneity to facilitate species diversity. However, attempts to show the relationship between spatial heterogeneity and diversity empirically have given mixed results. One reason for this may be the failure to consider how species respond to the factor in the environment that varies. Most models of the heterogeneity-diversity relationship assume heterogeneity in non-resource environmental factors. These models show the potential for spatial heterogeneity to promote many-species coexistence via mainly the spatial storage effect. Here, I present a model of species competition under spatial heterogeneity and resource factors. This model allows for the stable coexistence of only two species. Partitioning the model to quantify the contributions of variation-dependent coexistence mechanisms shows contributions from only one mechanism, growth-density covariance. More notably, it shows the lack of potential for any contribution from the spatial storage effect, the only mechanism that can facilitate stable many-species coexistence. This happens because the spatial storage effect measures the contribution of different species to specializing on different parts of the gradient of the heterogeneous factor. Under simple models of resource competition, in which all species grow best at high resource levels, such specialization is impossible. This analysis suggests that, in the absence of additional mechanisms, spatial heterogeneity in a single resource is unlikely to facilitate many-species coexistence and, more generally, that when evaluating the relationship between heterogeneity and diversity, a distinction should be made between resource and non-resource factors

Using the Dispersal Assembly Hypothesis to predict local species richness from the relative abundance of species in the regional species pool

In recent years, many authors have attempted to determine whether local processes (such as competitive exclusion) result in local species richness below that of a local community assembled only by dispersal from a larger regional species pool. To do this they hypothesize that no local processes are significant and that local communities are assembled by dispersal only (Dispersal Assembly Hypothesis or DAH). Some authors have presumed that a prediction of this hypothesis is that, if many regions of similar ecological type (e. g., grassland, temperate deciduous forest, etc.) are compared then local richness will be the same fixed proportion of regional richness, across all regions. To compare this putative prediction with observed data, they plot local richness on the vertical axis vs. regional richness on the horizontal axis and discover how well a straight line fits: if it fits well then they accept the hypothesis and they take the slope as an estimate of the fixed proportion; if it fits poorly (usually by curving down for larger regional pools), then they reject the hypothesis and they presume that local processes are significant. In the present paper, I hypothesize DAH, and for each of two different species relative abundance distributions and predict, by simulation, the probability distribution of species richness in a local community, for a full spectrum of local community sample sizes. The plot of the expected values of these predicted distributions vs. regional richness (for a constant local sample size) and vs. increasing local sample size (for constant regional richness) is not a straight line in either case, but always curves downward. Thus, a straight line (proportional sampling) is never a prediction of DAH, so that looking for straight lines in data plots is irrelevant to testing DAH. Finally, I describe how to compare these predictions to observed data to test whether local processes significantly limit local species richness

Analysis of Per Capita Contributions from a Spatial Model Provides Strategies for Controlling Spread of Invasive Carp

Metapopulation models may be applied to inform natural resource management to guide actions targeted at location-specific subpopulations. Model insights frequently help to understand which subpopulations to target and highlight the importance of connections among subpopulations. For example, managers often treat aquatic invasive species populations as discrete populations due to hydrological (e.g., lakes, pools formed by dams) or jurisdictional boundaries (e.g., river segments by country or jurisdictional units such as states or provinces). However, aquatic invasive species often have high rates of dispersion and migration among heterogenous locations, which complicates traditional metapopulation models and may not conform to management boundaries. Controlling invasive species requires consideration of spatial dynamics because local management activities (e.g., harvest, movement deterrents) may have important impacts on connected subpopulations. We expand upon previous work to create a spatial linear matrix model for an aquatic invasive species, Bighead Carp, in the Illinois River, USA, to examine the per capita contributions of specific subpopulations and impacts of different management scenarios on these subpopulations. Managers currently seek to prevent Bighead Carp from invading the Great Lakes via a connection between the Illinois Waterway and Lake Michigan by allocating management actions across a series of river pools. We applied the model to highlight how spatial variation in movement rates and recruitment can affect decisions about where management activities might occur. We found that where the model suggested management actions should occur depend crucially on the specific management goal (i.e., limiting the growth rate of the metapopulation vs. limiting the growth rate of the invasion front) and the per capita recruitment rate in downstream pools. Our findings illustrate the importance of linking metapopulation dynamics to management goals for invasive species control

An algorithmic and information-theoretic approach to multimetric index construction

The use of multimetric indices (MMIs), such as the widely used index of biological integrity (IBI), to measure, track, summarize and infer the overall impact of human disturbance on biological communities has been steadily growing in recent years. Initially, MMIs were developed for aquatic communities using preselected biological metrics as indicators of system integrity. As interest in these bioassessment tools has grown, so have the types of biological systems to which they are applied. For many ecosystem types the appropriate biological metrics to use as measures of biological integrity are not known a priori. As a result, a variety of ad hoc protocols for selecting metrics empirically has developed. However, the assumptions made by proposed protocols have not be explicitly described or justified, causing many investigators to call for a clear, repeatable methodology for developing empirically derived metrics and indices that can be applied to any biological system. An issue of particular importance that has not been sufficiently addressed is the way that individual metrics combine to produce an MMI that is a sensitive composite indicator of human disturbance. In this paper, we present and demonstrate an algorithm for constructing MMIs given a set of candidate metrics and a measure of human disturbance. The algorithm uses each metric to inform a candidate MMI, and then uses information-theoretic principles to select MMIs that capture the information in the multidimensional system response from among possible MMIs. Such an approach can be used to create purely empirical (data-based) MMIs or can, optionally, be influenced by expert opinion or biological theory through the use of a weighting vector to create value-weighted MMIs. We demonstrate the algorithm with simulated data to demonstrate the predictive capacity of the final MMIs and with real data from wetlands from Acadia and Rocky Mountain National Parks. For the Acadia wetland data, the algorithm identified 4 metrics that combined to produce a −0.88 correlation with the human disturbance index. When compared to other methods, we find this algorithmic approach resulted in MMIs that were more predictive and comprise fewer metrics

Ecology and host interactions of the angiosperm parasite <italic>Cuscuta gronovii</italic> (Convolvulaceae) Willd. in southeastern Michigan wetlands.

Ecologists have long recognized that generalist parasites are capable of impacting the nature of their hosts' interactions with other species and thus have a potentially significant role in shaping community structure. I use a plant host-parasite system to study the impacts of a generalist parasite the structure of its hosts' communities. The parasitic vine, Cuscuta gronovii (Convolvulaceae), grows in wetlands where it often infects the annual Impatiens capensis (Balsaminaceae), and perennials Aster puniceus (Asteraceae) and several Solidago species (Asteraceae). Field observations and experiments were carried out to test the hypotheses that C. gronovii infectiveness and virulence varied with life-history stage of the parasite and among the species of host. Data show that, although C. grononvii infects a number of host species by the end of the growing season, all initial infections occurred on one species, I. capensis. Surveys also demonstrate that the virulence of C. gronovii varies among host species. Field grown Solidago gigantea plants show no significant deleterious effect of C. gronovii infection; however, Aster puniceus and I. capensis are impacted negatively by C. gronovii infection. At the plot level, increased intensity of infection was associated with reduced per capita perennial rhizome production, but was not significantly correlated with per capita fruit production of I. capensis. This suggests an indirect mutualism between I. capensis and C. gronovii, in which I. capensis enabled C. gronovii to infect perennials hosts that compete with I. capensis for space. Mathematical models based upon the natural history of this system were constructed to predict the conditions that result in indirect mutualism between C. gronovii and I. capensis. These models suggest that indirect mutualism is possible under a wide variety of conditions and depends primarily upon the species composition of the neighborhood in which the pair occurs. Lattice simulation models were used to predict the effect of selection on I. capensis susceptibility to C. gronovii. These models predict that evolution of either highly resistant, or susceptible populations of I. capensis populations are possible, depending on the dispersal distance of I. capensis and the virulence of C. gronovii. My research demonstrates that fitness outcomes of multi-species interactions can vary widely in space and time and that knowledge of the range of natural variability within which the interaction occurs and how this variability effects both the direct and indirect interactions between species will be necessary to predict where along a continuum from antagonistic to mutualistic lies the interaction between a pair of species.Ph.D.Biological SciencesBotanyEcologyForestryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/124559/2/3150088.pd

A causal examination of the effects of confounding factors on multimetric indices

The development of multimetric indices (MMIs) as a means of providing integrative measures of ecosystem condition is becoming widespread. An increasingly recognized problem for the interpretability of MMIs is controlling for the potentially confounding influences of environmental covariates. Most common approaches to handling covariates are based on simple notions of statistical control, leaving the causal implications of covariates and their adjustment unstated. In this paper, we use graphical models to examine some of the potential impacts of environmental covariates on the observed signals between human disturbance and potential response metrics. Using simulations based on various causal networks, we show how environmental covariates can both obscure and exaggerate the effects of human disturbance on individual metrics. We then examine from a causal interpretation standpoint the common practice of adjusting ecological metrics for environmental influences using only the set of sites deemed to be in reference condition. We present and examine the performance of an alternative approach to metric adjustment that uses the whole set of sites and models both environmental and human disturbance effects simultaneously. The findings from our analyses indicate that failing to model and adjust metrics can result in a systematic bias towards those metrics in which environmental covariates function to artificially strengthen the metric–disturbance relationship resulting in MMIs that do not accurately measure impacts of human disturbance. We also find that a “whole-set modeling approach” requires fewer assumptions and is more efficient with the given information than the more commonly applied “reference-set” approach

An algorithmic and information-theoretic approach to multimetric index construction

The use of multimetric indices (MMIs), such as the widely used index of biological integrity (IBI), to measure, track, summarize and infer the overall impact of human disturbance on biological communities has been steadily growing in recent years. Initially, MMIs were developed for aquatic communities using preselected biological metrics as indicators of system integrity. As interest in these bioassessment tools has grown, so have the types of biological systems to which they are applied. For many ecosystem types the appropriate biological metrics to use as measures of biological integrity are not known a priori. As a result, a variety of ad hoc protocols for selecting metrics empirically has developed. However, the assumptions made by proposed protocols have not be explicitly described or justified, causing many investigators to call for a clear, repeatable methodology for developing empirically derived metrics and indices that can be applied to any biological system. An issue of particular importance that has not been sufficiently addressed is the way that individual metrics combine to produce an MMI that is a sensitive composite indicator of human disturbance. In this paper, we present and demonstrate an algorithm for constructing MMIs given a set of candidate metrics and a measure of human disturbance. The algorithm uses each metric to inform a candidate MMI, and then uses information-theoretic principles to select MMIs that capture the information in the multidimensional system response from among possible MMIs. Such an approach can be used to create purely empirical (data-based) MMIs or can, optionally, be influenced by expert opinion or biological theory through the use of a weighting vector to create value-weighted MMIs. We demonstrate the algorithm with simulated data to demonstrate the predictive capacity of the final MMIs and with real data from wetlands from Acadia and Rocky Mountain National Parks. For the Acadia wetland data, the algorithm identified 4 metrics that combined to produce a −0.88 correlation with the human disturbance index. When compared to other methods, we find this algorithmic approach resulted in MMIs that were more predictive and comprise fewer metrics

Analysis of per capita contributions from a spatial model provides strategies for controlling spread of invasive carp

Abstract Metapopulation models may be applied to inform natural resource management to guide actions targeted at location‐specific subpopulations. Model insights frequently help to understand which subpopulations to target and highlight the importance of connections among subpopulations. For example, managers often treat aquatic invasive species populations as discrete populations due to hydrological (e.g., lakes, pools formed by dams) or jurisdictional boundaries (e.g., river segments by country or jurisdictional units such as states or provinces). However, aquatic invasive species often have high rates of dispersion and migration among heterogenous locations, which complicates traditional metapopulation models and may not conform to management boundaries. Controlling invasive species requires consideration of spatial dynamics because local management activities (e.g., harvest, movement deterrents) may have important impacts on connected subpopulations. We expand upon previous work to create a spatial linear matrix model for an aquatic invasive species, Bighead Carp, in the Illinois River, USA, to examine the per capita contributions of specific subpopulations and impacts of different management scenarios on these subpopulations. Managers currently seek to prevent Bighead Carp from invading the Great Lakes via a connection between the Illinois Waterway and Lake Michigan by allocating management actions across a series of river pools. We applied the model to highlight how spatial variation in movement rates and recruitment can affect decisions about where management activities might occur. We found that where the model suggested management actions should occur depend crucially on the specific management goal (i.e., limiting the growth rate of the metapopulation vs. limiting the growth rate of the invasion front) and the per capita recruitment rate in downstream pools. Our findings illustrate the importance of linking metapopulation dynamics to management goals for invasive species control

Appendix A. Information pertaining to the bacterial and fungal isolates used in our study.

Information pertaining to the bacterial and fungal isolates used in our study

Appendix B. The relationships between population growth rates and respiration rates for a subset of the bacterial strains used in this study.

The relationships between population growth rates and respiration rates for a subset of the bacterial strains used in this study