* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Toward a Better Integration of Ecological
Molecular ecology wikipedia , lookup
Ecological resilience wikipedia , lookup
Conservation biology wikipedia , lookup
Biogeography wikipedia , lookup
Habitat conservation wikipedia , lookup
Conservation agriculture wikipedia , lookup
Operation Wallacea wikipedia , lookup
Renewable resource wikipedia , lookup
Natural environment wikipedia , lookup
Latitudinal gradients in species diversity wikipedia , lookup
Human impact on the nitrogen cycle wikipedia , lookup
Perovskia atriplicifolia wikipedia , lookup
Biodiversity wikipedia , lookup
Restoration ecology wikipedia , lookup
Ecological fitting wikipedia , lookup
Biological Dynamics of Forest Fragments Project wikipedia , lookup
Reconciliation ecology wikipedia , lookup
Roundtable Toward a Better Integration of Ecological Principles into Ecogeoscience Research DANIEL C. ALLEN, BRADLEY J. CARDINALE, AND THERESA WYNN-THOMPSON Interdisciplinary research in the fields of ecohydrology and ecogeomorphology is becoming increasingly important as a way to understand how biological and physical processes interact to affect some of the world’s most pressing environmental problems; however, much of this research is based on overly simplistic assumptions about ecological systems. Here, we provide a road map for the integration of some ecological principles into these budding fields of research that warrant future study. We focus on three basic principles of ecology that should have important implications for ecohydrology and ecogeomorphology: Biological traits exist in a distribution due to species diversity, biological traits are adaptable and dynamic through time, and dynamically coupled relationships between species and their environments create biotic–abiotic feedback cycles. We develop several general hypotheses that incorporate these principles and can help guide future ecohydrology and ecogeomorphology studies. Keywords: ecogeomorphology, biogeomorphology, ecohydrology, hydroecology, ecosystem engineers A lthough much of the biological and physical sciences have developed as distinct disciplines, we increasingly recognize that global change research requires that we work at the nexus of biological and physical systems to solve many of the world’s most pressing environmental problems. Therefore, an increasing number of interdisciplinary fields are rapidly evolving—fields such as ecogeo morphology and ecohydrology—in which the relationships between biological communities and geomorphologic and hydrologic processes are investigated. Research coupling ecology and the geosciences has increased exponentially over the past decade, as is evidenced by the growth in the number of articles published, the founding of new interdisciplinary journals, and increased funding opportunities and awards (figure 1). Despite the exponential growth, the utility of research in ecogeomorphology and ecohydrology is often limited by a rather simplistic view of how biological processes influence the physical environment. Indeed, in a recent National Research Council report (NRC 2009), it was argued that a better understanding of Earth system processes could be achieved through a broader incorporation of ecological principles and researchers were called on to more fully investigate how biota influence Earth surface processes. In part, this call was motivated by the fact that purely physical models are often insufficient to predict geophysical processes. There are many examples in the literature in which researchers have been misled by not having an accurate integration of biological and physical processes. For example, paleoclimatic studies in the 1990s failed to explain past climate events with purely physical models (Foley et al. 1998). But with information on how boreal forest vegetation affects albedo, models were able to reproduce temperature changes associated with the Quaternary ice age and the Holocene warming. Vegetative effects on albedo, soil moisture, and evapotranspiration were also necessary in order to model the African monsoon of the early Holocene, when the Saharan desert was covered by extensive grasslands, savannas, and lakes (Foley et al. 1998). But such is also the case in research related to hydrology and geomorphology. In the 1980s, it was widely believed that vegetation was a passive component of the hydrologic cycle and that the Earth system was driven exclusively by ocean–atmosphere dynamics, with little influence from terrestrial vegetation (Kabat et al. 2004). Of course, we now know that one cannot realistically describe hydrological processes without including vegetation information (Gordon and Huxman 2007). Therefore, to prevent similar failures in the future, we should learn from these experiences and better integrate biology with geo morphologic and hydrologic processes. Here, we discuss three ecological principles that could address some common limitations in the current state of BioScience 64: 444–454. © The Author(s) 2014. Published by Oxford University Press on behalf of the American Institute of Biological Sciences. All rights reserved. For Permissions, please e-mail: [email protected]. doi:10.1093/biosci/biu046 Advance Access publication 16 April 2014 444 BioScience • May 2014 / Vol. 64 No. 5 http://bioscience.oxfordjournals.org Roundtable that incorporate these ecological principles. The intended audience of this article is ecogeoscience researchers who use both experimental and modeling approaches, and we include some thoughts about how we can increase collaboration and dialogue between modelers and experimentalists to produce a more quantitative understanding of the relationship between the Earth’s biological and physical processes. Figure 1. Productivity of ecohydrology and ecogeomorphology research from 1990 to 2011, measured by the cumulative number of articles published (the solid line), ecogeosciences interdisciplinary journals (the dashed line), US National Science Foundation (NSF) awards (the dotted line), and the total amount of funds awarded (the dashed and dotted line, in tens of thousands of US dollars). The research articles and NSF awards were identified by searching the Web of Science and the NSF Web site, searching for articles and awards with terms related to ecohydrology and ecogeomorphology research (the search string was ecogeomorph* OR biogeomorph* OR eco-geomorph* OR bio-geomorph* OR ecohydrolog* OR hydroecolog* OR eco-hydrolog* OR hydro-ecolog* OR ecohydraul* OR eco-hydraul*). The ecogeoscience interdisciplinary journals were identified using Journal Citation Reports to locate journals in both ecology (searching for ecology or biology) and geoscience (searching for geosciences, multidisciplinary, or water resources) categories. Note the log scale on the y-axis. ecogeoscience research, from an ecological perspective: (1) a distribution of biological traits exists in nature due to species diversity, (2) biological traits are dynamic, and (3) dynamic coupling between biological traits and geophysical processes produce dynamic feedback cycles. For the purposes of this article, we refer to ecogeomorphology and ecohydrology collectively as the ecogeosciences. Although we acknowledge that, in the development of any research field, it is often good practice to start simply and add complexity piecemeal, it is our hope that, by addressing some ecological limitations early on, we can help guide future research on the dynamics between biological communities and geophysical processes. Because reviews of ecohydrology and ecogeomorphology are already present in the literature (e.g., D’Odorico et al. 2010a, Reinhardt et al. 2010), we do not offer an exhaustive list of important ecological–geophysical interactions. Rather, we use specific case studies as platforms to discuss how we can address these limitations and to generate hypotheses for ecohydrology and ecogeomorphology research http://bioscience.oxfordjournals.org Ecological principle 1: Biological traits in nature are variable because of species diversity, and variation can have impacts that differ from the mean value of those traits Perhaps the most striking aspect of the Earth is its diversity of life, which is often reflected in the diversity of traits that organisms possess. All biological responses to and effects on physical processes are ultimately governed by species traits. Species traits are a foundational concept in community ecology and affect how species coexist (Macarthur 1958), compete with one another (Tilman 1981), or facilitate one another (Stachowicz 2001). When species vary in biological traits, it follows logically that they should also vary in their responses to and effects on physical processes. However, in the vast majority of studies in ecogeosciences, the fact that a distribution of biological traits exists in nature has been ignored. This is not to say that researchers in the ecogeosciences do not consider biological traits in their studies, nor are we suggesting that these researchers are not aware that traits vary among species. Indeed, there are numerous examples of studies or models in which different parameter values are considered for different types of organisms or ecosystems (e.g., the photosynthetic rate of a grassland versus that of a forest). Even so, in the overwhelming majority of studies, biological variation per se has not been considered, nor has it been acknowledged that the systems studied are characterized by multiple, coexisting species that all have unique trait values. Ecologists have found that we cannot take a simple mean value of an ecological parameter and expect that mean to accurately represent the influence of organisms on ecosystemlevel processes. Rather, the variation around that mean can have stronger effects on a given process than does the mean itself. There is now abundant evidence that biodiversity and its corresponding trait variation exert direct control over key ecosystem processes, which are strongly linked to physical processes, such as the production of plant biomass (Cardinale et al. 2011). In the physical sciences, a similar phenomenon can be found in studies in which the effects of the height of roughness elements (e.g., a protruding stone or animal shell on a stream bottom) on near-bed flow patterns are examined. De Marchis and Napoli (2012) modeled turbulent channel flows over two surfaces with the same mean roughness height (often used as a parameter in hydraulics models) but allowed the surface geometry of one surface to vary while the other remained constant (i.e., the surfaces shared the same mean roughness height, but one had a larger variance). The result was that the more-varied surface produced May 2014 / Vol. 64 No. 5 • BioScience 445 Roundtable Table 1. General hypotheses that integrate basic ecological principles into ecohydrology and ecogeomorphology research. Ecological principle Hypothesis (1) A distribution of biological traits exists in nature H1: Biodiversity increases the likelihood of a system containing a unique species that has disproportionate effects on a given process, such that a more diverse system will have stronger effects than a less diverse system would. H2: Biodiversity increases the likelihood of a system containing complementary traits (which produce additive biodiversity effects through niche differentiation) or synergistic traits (which produce nonadditive biodiversity effects through interactions), such that a more diverse system will have stronger effects than a less diverse system would. (2) Biological traits are dynamic H3: The expression of biological traits is phenotypically plastic such that changes in the physical environment can lead to changes in trait expression that alter hydrologic and geomorphic processes. H4: Biological traits can change rapidly because of evolution, on short timescales of just a few generations. Therefore, the evolution of biological traits that influence hydrologic and geomorphic processes can evolve rapidly, as well, which can affect hydrology and geomorphology. (3) Bidirectional relationships generate feedback cycles H5: Landscapes and biological communities coevolve and influence each other over time, and accurately describing feedback cycles is necessary to capture the dynamic coupling of their relationships. a markedly different flow pattern, which suggests that the simple mean roughness height does not accurately represent roughness effects (De Marchis and Napoli 2012). Variation around the mean of physical parameters (e.g., precipitation variables) in ecogeoscience models is frequently addressed through the use of stochastic models and Monte Carlo simulations, which randomly assign parameter values on the basis of the distribution of physical properties observed within a system (e.g., Petrie and Brunsell 2012). Therefore, although ecogeoscience research has incorporated variation in physical variables, it is still common practice to represent biological characteristics with a mean value (e.g., the plant canopy height of a grassland or a forest). Although some researchers have made steps in the right direction, such as considering the effects of variation in vegetation cover (canopy heights and canopy gaps) as roughness elements (Okin 2008), the explicit links to variation in biological community structure or biodiversity are still not made in these studies. Here, we argue that the variability of biological variables should also be considered in ecogeoscience research in order to better describe the relationships between biological and physical systems. Because biological traits are variable among species, the total number of species and traits present can have strong implications for community effects on ecosystems. One current paradigm in the field of ecology is that biodiversity affects ecosystem processes as diverse as biomass production, flower pollination rates, and prey suppression. These effects can be quite large, as impacts of biodiversity loss can rival the effects of climate change, nutrient pollution, and invasive species (Hooper et al. 2012). However, there is a dearth of studies in which researchers have examined how biodiversity might influence physical processes, although we consider it plausible. When more species are present in an ecosystem, there is an increased likelihood that a unique species with especially strong impacts on a given process will be present (H1 in table 1). If that species then comes to dominance over others, the more species that are present in an ecosystem, the more 446 BioScience • May 2014 / Vol. 64 No. 5 likely it is that that high-performing species will enhance ecosystem-level processes. In ecogeomorphology, we know that certain species (i.e., ecosystem engineers) can have especially strong influences on landscape evolution (Jones 2012). In fluvial systems, some plant species play important pioneering roles as ecosystem engineers in river channel evolution. Plants, such as Salix spp., which resist uprooting during floods, regenerate vegetatively from flood-transported fragments and tolerate a wide range of water levels are central to the development of pioneer landforms (e.g., islands) in rivers (Gurnell et al. 2012). In turn, these pioneering plant species alter flow patterns in ways that encourage sediment deposition, which provides new habitat for other species to colonize. Therefore, some species have disproportionate impacts on physical processes precisely because biological traits are variable, and a more diverse system is more likely to contain and become dominated by such a species. Thus, species-specific trait effects can produce biodiversity effects on ecosystems. A second way in which biodiversity affects natural processes is that, when more species are present, the likelihood of complementary or synergistic traits’ being present increases (H2 in table 1). A few studies have shown that the interactive effects of biodiversity can affect physical processes. Cardinale and colleagues (2002) manipulated the diversity of net-spinning caddisfly larvae, which build individual silk nets in gravel-bed streams to passively capture the particulate organic matter on which they feed. When caddisfly larvae were placed in artificial streams, the near-bed current velocities when all three species were present were 22% greater than the near-bed velocities in streams containing each species in isolation (figure 2). Because the nets of the caddisfly species differ in size, the biogenic structure created was more topographically complex (i.e., there was more variation in surface features), which influenced the patterns of near-bed water flow. Likewise, Allen and Vaughn (2011) found that the gravel erosion that occurred during a simulated high-flow event when multiple freshwater mussel species were present was 44% greater than the simple mean http://bioscience.oxfordjournals.org Roundtable Figure 2. The relationship between species diversity and physical processes from two experiments, one in which freshwater mussel biodiversity was manipulated and in which the amount of gravel eroded (the black dark bars, in grams; source: The data are from Allen and Vaughn 2011), and in the other, caddisfly larvae biodiversity was manipulated, and near-bed current velocities were measured (in centimeters per second; the light bars; source: The data are from Cardinale et al. 2002). The horizontal lines indicate the mean value of physical processes from single species treatments to represent differences in physical processes when interactions between species are allowed to occur (long-dashed line, gravel eroded; short-dashed line, current velocity). The error bars represent the positive standard error. of eroded gravel from the isolated single-species treatments (figure 2), presumably because of the increased flow turbulence created by a more topographically complex surface from the different shapes and sizes of mussel species and species differences in burrowing depth. Therefore, after measuring a physical process occurring in the presence of one of three species in isolation, the mean value of those measurements could not adequately predict that same process when the three species were present together. Therefore, a second type of biodiversity effect can be generated by species trait differences that either complement or facilitate each other. When we consider the evidence from the large number of experiments in which the effect of biodiversity on ecological processes has been investigated, there is good reason to believe that the influence of biodiversity on geophysical processes may be ubiquitous. Because pioneer biodiversity and ecosystem function studies were focused on plant communities (Tilman et al. 1997), there has been a great deal of research in which the relationship between plant biodiversity and ecosystem functions has been investigated. For example, in a recent meta-analysis of 574 independent manipulations of primary producer species richness (terrestrial plants and freshwater algae), Cardinale and colleagues (2011) found strong support for the hypothesis that biodiversity increases biomass production and the efficiency http://bioscience.oxfordjournals.org of resource use in ecosystems. The mechanisms producing these effects (briefly described above) are a result of both species-specific trait effects (H1 in table 1, or the so-called selection effect; 122 of 196 cases) and effects of species trait differences that can complement or facilitate each other (H2 in table 1, or the so-called complementarity effect; 157 of 196 cases). Finally, another question of interest is often whether a diverse system can outperform its most productive single species, and Cardinale and colleagues (2011) found that that this occurred in 37% of the studies (transgressive overyielding; 138 of 375 cases). Therefore, there is ample evidence that biodiversity can affect key ecosystem processes that we know are also related to physical processes. In one diversity manipulation in a tree plantation, Potvin and Gotelli (2008) found that individual trees in multispecies plantings showed increases of 30%– 58% in tree basal area when compared with trees in singlespecies plantings. Furthermore, a limited number of studies suggest that biodiversity may also increase plant water-use efficiency (Verheyen et al. 2008). Much of the research in the ecogeosciences has been focused on how plants influence geophysical processes—relationships that are at least partly due to some aspect of plant biomass (D’Odorico et al. 2010a, Osterkamp et al. 2012). For example, plant biomass can affect geophysical processes as diverse as water infiltration rates, sediment deposition, and air temperatures (table 2). There are two field studies that we know of in which a relationship between biodiversity and a geophysical process was observed, although diversity itself was not directly manipulated. In a comparative field study, Wang and colleagues (2012) investigated the relationship between plant species richness (encompassing woody and herbaceous plants) and soil erosion on plots in an evergreen broadleaf forest that varied in succession stages, which produced a gradient in species richness. They found a negative relationship between species richness and the frequency of surface runoff events, with the most diverse plots (32 tree species) experiencing 9 runoff events over 3 years, compared with 72 runoff events in plots with 2 tree species (Wang et al. 2012). Moreover, tree species richness explained approximately 70% of the variation in surface runoff, as well as sediment and phosphorus losses (Wang et al. 2012). Although the mechanisms producing this effect are unknown, we know from other studies that tree biodiversity increases the production of fine root biomass (Balvanera et al. 2006, Brassard et al. 2013), which could reduce soil bulk density and increase soil hydraulic conductivity and organic matter content. Likewise, in an analysis of an observational data set, Bowker and colleagues (2010) showed that the biodiversity of a biological soil crust community in a dryland ecosystem affected the soil’s physical properties, including surface roughening (related to water infiltration and dust trapping) and soil stability (related to erosion). In their structural equation model, Bowker and colleagues (2010) showed that diversity metrics were positively correlated with surface roughening (species richness, r = .60) and soil stability (species richness, r = .24; evenness, May 2014 / Vol. 64 No. 5 • BioScience 447 Roundtable Table 2. Summary of biodiversity effects on aspects of plant communities that have been linked to geophysical processes. Citations Plant ecosystem property Physical property Aboveground biomass Canopy interception of rainfall Water or wind velocity Sediment erosion, transport, and deposition Water use Soil moisture Infiltration, groundwater recharge, evapotranspiration Verheyen et al. 2008 D’Odorico et al. 2010a Riparian woody biomass production Large woody debris, biogenic structure Sediment erosion, transport, and deposition Piotto 2008 Osterkamp et al. 2012 Surcharge (mass added to stream banks) Mass wasting Soil porosity Soil infiltration rate and capacity Increase soil cohesion chemically and physically Soil erosion, stream or river geomorphology Belowground biomass and associated soil microbes Geophysical process Biodiversity and ecosystem function Ecogeoscience Runoff, infiltration, hydrologic cycle Cardinale et al. 2011 D’Odorico et al. 2010a r = .34). Despite the limited number of experiments in which biodiversity effects on physical processes was observed, the above examples demonstrate that biological variation can modify biotic effects on physical processes by 22%–44% beyond the mean traits of the component species (Cardinale et al. 2002, Allen and Vaughn 2011). Therefore, failure to include biological variation into ecogeoscience models and experiments could potentially mislead our interpretations and understanding of ecogeoscience. One important consideration is that the vast majority of biodiversity and ecosystem function studies are controlled experiments conducted at small spatial scales, and there is not as much data about how biodiversity might affect ecosystem and geophysical processes at large scales. However, the limited number of experiments and syntheses in which the biodiversity–ecosystem relationship at larger scales were investigated all suggest that biodiversity effects increase as a function of scale. In a recent meta-analysis, Griffin and colleagues (2013) found that the magnitude of biodiversity effects increases as the spatial and temporal scales of the biodiversity manipulation increase. In other studies, plant biodiversity effects over large scales in natural ecosystems have been examined, and it has been found that biodiversity increases tree productivity in temperate and boreal forests across eastern Canada (Paquette and Messier 2011), and plant biodiversity affected multiple ecosystem functions in a global survey of 224 dryland ecosystems in all continents except Antarctica (Maestre et al. 2012). Finally, when considering the importance of three different spatial scales of biodiversity (α diversity, the number of unique species at a local scale; β diversity, the number of unique species assemblages in a landscape; and γ diversity, the number of unique species in a landscape), Pasari and colleagues (2013) found that, although α diversity had the strongest effects on ecosystem functions that were considered individually, β and γ diversity had positive effects on ecosystem multifunctionality. It is likely that, as one increases the spatial scale, the trait 448 BioScience • May 2014 / Vol. 64 No. 5 Burri et al. 2011, Nepf 2012 Simon and Collison 2002 Balvanera et al. 2006 D’Odorico et al. 2010a Gyssels et al. 2005 variation among species and the niche differences between species are greatest, such that biological variation is manifest most strongly at large scales. Therefore, there is good reason to expect that explicit incorporation of biological variation in ecohydrology and ecogeomorphology models could lead to major improvements in our understanding of how biological communities affect geophysical processes. Physical models that condense biological impacts down to a single value (e.g., a mean value of biological traits present in nature; figure 3a) are likely to produce quantitatively incorrect conclusions and may, in fact, be qualitatively incorrect, as well (Balvanera et al. 2006, Cardinale et al. 2011). Therefore, we suggest that we should account for biological variation when studying the relationships between organisms and geophysical processes (figure 3b) and hypothesize that variation in biological traits can produce biodiversity effects on physical processes (H1 and H2 in table 1). Ecological principle 2: Biological traits are dynamic Another fundamental concept in ecology is that biological communities are dynamic in both space and time: Changing biological traits in nature can result from both ecological and evolutionary processes. Ecological processes that lead to changes in biological traits include species replacements through succession, competition, or invasive species—processes of species turnover that have already received considerable attention in ecogeoscience research (Gillette and Pitchford 2004, Huxman et al. 2005, Horn et al. 2012). However, recent research also suggests that evolution can occur more rapidly than was previously thought and on timescales that are relevant to those of ecohydrologic and ecogeomorphologic processes (Schoener 2011). Yet a common assumption of researchers investigating temporal dynamics in ecohydrologic or ecogeomorphic processes is that the biological traits related to geophysical processes are static (figure 3c). Because much of this work is relatively http://bioscience.oxfordjournals.org Roundtable Figure 3. The state of current ecogeoscience research and suggested future directions. The focus of ecogeoscience research is to describe the relationship between a biological factor, β, which interacts with a physical (abiotic) factor, α, to produce a geophysical process, Π. One example might be soil slope stability (Π), which is a product of the shear strength of the soil matrix (α) and the cohesion added to the soil matrix by plant roots (β). In panels (a)– (f), conceptual illustrations are accompanied by conceptual equations. (a) Ecogeoscience research generally holds a narrow view of biota and assumes that biological traits are uniform, focused on the mean value of a biological factor, μ, and we suggest that (b) ecogeoscience research should embrace the role of biological variability, σ2, rather than trying to control for it. (c) In ecogeoscience research, it is often assumed that biological traits are static over time, t, whereas we argue that (d) ecogeoscience research ought to account for changes in biological traits over time, as well as changes in the physical environment. (e) Although biota and geophysical processes are related, ecogeoscience research tends to be limited to unidirectional interactions of the effect of one on the other. To fully understand the feedbacks between biota and geophysical processes, we contend that (f) ecogeoscience research needs to incorporate an element of time to allow feedbacks to develop in order to understand how the interactions between physical and biological factors (γ and δ) affect geophysical processes. Drawings: Jesse Antuma. http://bioscience.oxfordjournals.org new in ecology, there is not a great deal of research on how rapid biological trait change can affect geophysical processes. However, we believe that rapid trait change is something that researchers should consider in the future of ecogeoscience research, especially given how rapidly some organisms are adapting to climate change (figure 3d; Hudson et al. 2011). One pathway that can lead to biological traits changing over time is phenotypic plasticity, in which environmental factors influence changes in the expression of biological traits (i.e., phenotypes). Therefore, a species or genotype may express traits differently under different environmental conditions, which may trigger a change in phenotype. An organism’s physical environment is one well-studied factor that can initiate phenotypic change in a plastic trait. For example, much of current climate change research is focused on how increased temperature and atmospheric carbon dioxide concentrations can alter plant trait expression. In a long-term carbon dioxide and ozone enrichment experiment in a temperate US forest, Pregitzer and colleagues (2008) found that trees increased belowground carbon allocation in response to increased carbon dioxide and ozone, which increased fine root biomass by more than 50%. In a warming experiment in the Arctic tundra, Hudson and colleagues (2011) observed that, after 16 years of 1–2-degree (Celsius) warming, three shrub species and one forb species responded by increasing in leaf size by more than 40% and increased in plant height by nearly 30%. Precipitation changes are important as well; Hoeppner and Dukes (2012) documented that drought increased the growth of deep roots by 121% in a grassland. Therefore, if plant traits such as fine root biomass and leaf size are phenotypically plastic in response to changing environmental conditions, the hydrologic and geomorphic processes that these plant traits affect (such as soil erosion resistance and evapotranspiration rates, respectively; table 2) can change over time, as well (H3 in table 1). A second pathway for biological trait change over time is evolution. Perhaps May 2014 / Vol. 64 No. 5 • BioScience 449 Roundtable the most profound impacts of changing biological traits on geophysical processes are observed in the fossil and geologic records, because the evolution of land plants had a profound impact on fluvial processes and sediment deposits. In a review of changes in alluvial formations when land plants were evolving and colonizing land, Davies and Gibling (2010) found that land plant evolution was associated with increases in the proportion of mudrock and in sandstone maturity, as well as a decrease in the overall sand grain size. They also found evidence for the formation of meandering rivers after the appearance of land plants with rooting systems. Davies and Gibling (2010) suggested that the evolution of land plants led to a period of landscape evolution that should be considered one of the most significant geomorphologic changes in Earth history. However, there is a growing body of evidence showing that evolution can cause ecologically significant changes over much shorter timescales than was previously thought possible (Schoener 2011). Although the question of whether rapidly evolving biological traits can influence ecohydrologic and ecogeomorphic processes has yet to be vigorously addressed, there is some evidence to suggest that this could be a fruitful area of research. For example, the evolutionary responses of native plants to invasive species are one model system to study rapid evolution. Dostál and colleagues (2012) found that native Impatiens noli-tangere evolved differences in plant size, germination phenology, and phenotypic plasticity, depending on whether a coexisting nonnative species was present. Moreover, Rowe and Leger (2011) showed that the native grass Elymus multisetus evolved changes in root:shoot ratio, root length, and morphology in response to an invasive grass competitor. In another example, Franks (2011) showed that the annual plant Brassica rapa evolved to flower earlier at a cost of decreased water-use efficiency in response to drought, an adaptive strategy for drought avoidance. Because plant traits related to water use are likely to be under high selection pressure, especially under changing precipitation and temperature regimes, the relationship between the rapid evolution of plant traits and ecohydrologic processes may be a useful avenue of research. Therefore, the rapid evolution of plant traits related to water-use efficiency, plant size, root:shoot ratio, root depth, and morphology has the potential to affect hydrologic and geomorphologic processes, considerations that should be addressed in future research (H4 in table 1). One important directive will be to investigate the relative magnitude of ecological effects occurring at different temporal scales, contrasting impacts from migrations of species over long timescales (e.g., biome migrations in response to climate change) with the effects of the short-term evolution of existing species in response to changing ecological conditions. Ecological principle 3: From unidirectional feedbacks to dynamically coupled feedback cycles The historical view that biomes are primarily a product of their physical environment has been changing over recent 450 BioScience • May 2014 / Vol. 64 No. 5 decades, because there has been a great deal of emphasis on how organisms, themselves, act as agents of geomorphic and hydrologic change (Reinhardt et al. 2010). Although there is no denying that physical processes have strong effects on ecological processes, we are beginning to understand that the relationships between biology and Earth surface processes are dynamically coupled—meaning that we can observe and document causal relationships in both directions and that those causal relationships are mutually dependent. Moreover, studies in which only a unidirectional relationship has been addressed between physical and biological processes often cannot fully explain the patterns found in nature. As an example, shrub encroachment into arid and semiarid grasslands has often been attributed to climate warming—to climate effects on woody and herbaceous plant physiology—but climate effects on vegetation alone cannot explain variation in grass–shrub cover at the landscape scale (Archer 1989). However, once a bidirectional relationship that includes vegetation effects on microclimate is considered, the heterogeneous cover of shrubs and grasslands can be explained (D’Odorico et al. 2010b). In ecology, dynamically coupled relationships are common and produce feedback cycles, in which dynamically coupled bidirectional interactions govern process trajectories. This is something that is also common in the ecogeosciences, in which feedback cycles describing interactions between biota and Earth surface processes have been proposed for some time (Schlesinger et al. 1990). Indeed, we have noticed that the ecogeoscience literature is increasingly using the term feedback in papers, but, on closer inspection, these papers often do not actually demonstrate what an ecologist would consider a feedback cycle. In many papers, conceptual models that describe dynamically coupled feedback cycles are proposed but not quantified (Schlesinger et al. 1990, Monger and Bestelmeyer 2006, Okin et al. 2006). Still others show a unidirectional relationship between physical and biological processes that were previously known to operate in the opposite direction (Gillette and Pitchford 2004, Mueller et al. 2007, Okin 2008). Nevertheless, in ecology, evidence of opposing unidirectional relationships between two processes is a necessary—albeit insufficient—condition to quantify a feedback (figure 3e). To demonstrate a feedback, one must show that two processes are dynamically coupled; the outcome of process A at time t affects the outcome of process B at time t + 1 (e.g., figure 3f) and vice versa. The dynamically coupled relationships between vegetation and river channel geomorphology in anabranching ephemeral rivers in drylands could be described by a feedback cycle (Tooth and Nanson 2000). For example, we could apply the relationships among tree abundance, water velocity, and channel width to the graph in figure 3f, where α is mean water velocity in the anabranches, β is tree abundance in the dry river channel, and Π is the average anabranch width. Anabranch width and water velocity are related to each other: Given a constant discharge, a wider channel will be shallower and will have a lower velocity, http://bioscience.oxfordjournals.org Roundtable whereas a narrower channel will be deeper and will have a higher velocity. Anabranch width and tree abundance are related to each other: The establishment of trees in a sandy dry riverbed promotes the formation of ridges and islands within the overall channel, creating anabranches but decreasing the cumulative cross-sectional flow area. Finally, vegetation and water velocity are also related: Vegetation slows water velocity near the vegetation but increases it in the center of the anabranches away from the vegetation; however, if the water velocity is high enough, trees can become uprooted. Therefore, we would expect a dynamically coupled feedback cycle to occur over time, ultimately reaching a semiequilibrium state in which the average anabranch width does not change much, even though the placement of ridges and islands may change as vegetation dies and colonizes over time. There have been some efforts to quantify feedback cycles in ecogeoscience research, particularly in ecogeomorphological research linking feedbacks between vegetation type and erosion in arid systems. Shrub–grass dynamics and shrub encroachment in arid systems have been proposed to be a function of three types of feedback cycles: fire– vegetation feedbacks, soil erosion–vegetation feedbacks, and vegetation–microclimate feedbacks (D’Odorico et al. 2012). In the fire–vegetation feedback, a negative feedback loop promoting shrub encroachment has been proposed such that decreases in fire frequency increase shrub survival and cover, which decreases grass cover, which further decreases fire frequency. The erosion–vegetation feedback is another negative feedback loop proposed to promote shrub encroachment, in which a loss of grass cover increases the erosion of fine soil particles, which reduces soil fertility, which then reduces grass cover and promotes shrub growth. Finally, the vegetation–microclimate feedback loop mentioned earlier also promotes shrub encroachment, such that shrub cover increases bare soil, which increases nocturnal soil temperatures, which then increases shrub growth and survival. However, various attempts to quantify these feedback cycles have been met with limitations that affect the generality of the findings of those studies. For example, Okin and colleagues (2009) developed a simple model that quantified shrub–grass biomass dynamics. In this model, grasses had a competitive advantage over shrubs, but a physical–biological feedback was not modeled. Instead of modeling erosion explicitly to link decreases in grass cover with decreases in available soil resources, Okin and colleagues (2009) modeled the carrying capacity of grass biomass as a function of the existing grass biomass. This simplified model does not actually include any terms representing a physical process. D’Odorico and colleagues (2012) developed this model a bit further and incorporated a single parameter to represent the strength of a physical–biological feedback that may exist, but they still did not explicitly model any physical process. Moreover, neither of these models was fit with any ecological or physical data (Okin et al. 2009, D’Odorico et al. 2012). http://bioscience.oxfordjournals.org Nevertheless, there are a few cases in which dynamically coupled feedback cycles have been quantified in ecogeo science research, and these efforts have greatly helped our understanding of the development of landforms over time. Larsen and colleagues (2007) presented a model for the ridge and slough morphology of peatlands common in lowgradient lotic wetlands that exhibit spatial heterogeneity and even a pattern of vegetated ridges. Previous models were focused solely on peat accretion dynamics, which were enhanced by vegetation but were unable to predict long-term ridge-growth dynamics. The model presented by Larsen and colleagues (2007) relied on dual feedbacks between peat accretion and sediment transport (from flow pulses) and was able to produce ridge elevations characteristic of the ridge and slough formations of the Everglades. By including dynamically coupled relationships in the model, Larsen and colleagues (2007) were able to accurately predict ridge development in peatlands. In another study, in which historical photographs were used to document river development after a catastrophic flood that removed all vegetation from the floodplain, Corenblit and colleagues (2010) observed a positive feedback cycle between vegetation and river channel development. Pioneering vegetation (herbs and shrubs) that grew on bare soil helped to promote landform accretion, which then promoted the establishment of a dense riparian forest that was ultimately stable under the current hydrogeomorphologic conditions (Corenblit et al. 2010). Therefore, studies in which modeling or historical approaches were used show great potential for testing hypotheses that dynamically coupled feedback cycles generate coevolution between landscapes and biological communities (H5 in table 1). We advocate that such efforts be a focus of future research in ecogeoscience. In order to develop more-realistic models, we need more collaboration between modelers and experimentalists. Many of the models in ecogeoscience research are simplified because the model-building processes can get overly complicated very quickly as the number of variables grows, which also increases the uncertainty of the model. Therefore, the majority of models include simple functions to relate biotic and abiotic functions in order to test hypotheses and are rarely based on any real data in either the development or testing phases. As a result, these models are inherently unrealistic and do not provide tangible results that can be used to accurately predict coupled systems. However, in order for ecogeoscience models to grow in complexity and realism and to improve in accuracy, we need more ecologists who are willing to collaborate with modelers to collect the simple data necessary to validate these simple models. Although collecting this simple data may not excite ecologists currently exploring morecomplex principles in the field, the development and testing of basic models is necessary, because many of our most important questions need to be addressed at large spatial or long temporal scales, which modeling methods are often better suited to address. May 2014 / Vol. 64 No. 5 • BioScience 451 Roundtable addressed by integrating the ecological principle that distributions of biological traits exist in nature into ecogeoscience research in order to understand the relationship between biological variation and hydrologic and geomorphic processes. (2) In an era of global climate and environmental change, we do not know how biotic adaptations to these changes will affect the performance of important hydrologic and geo morphic processes. This problem can be addressed by incorporating the ecological principle that biological traits are dynamic into ecogeo science research, which will allow us to understand and ultimately predict how species adaptations to global change will affect hydrologic and geomorphic processes. Figure 4. The relationships between the hypotheses presented in this paper (3) Given global challenges 1 and 2 and ecological, evolutionary, hydrologic, and geomorphic processes. The solid above, the physical and biological sysline denotes the influence of ecological principle 1, hypothesis 1 (H1), and tems of the Earth are changing simultahypothesis 2 (H2). The short-dashed line represents principle 2, hypothesis 3 neously, and the consequences of these (H3), and hypothesis 4 (H4). The long-dashed lines demonstrate principle 3 and concurrent changes on dynamically hypothesis 5 (H5). coupled feedback cycles relating biota to hydrologic and geomorphic processes Conclusions are unknown. By integrating dynamically coupled feedback Although we present these three ecological principles sepacycles into ecogeoscience research, we will ultimately be rately, the reality is that they are likely working in concert, able to predict how biotic and physical changes will affect and we do not mean to suggest that the hypotheses in table 1 landscape coevolution and equilibrium states in nature. We are mutually exclusive. Incorporating all of these principles discussed several examples of studies in which researchtogether will be important to accurately describing landers are advancing our understanding of the links between scape evolution, because biological traits and physical varibiological communities and landscape processes by inteables are likely to covary and possibly direct the trajectory of grating these principles and then proposed hypotheses for the biological–physical system toward an equilibrium state future research. With biological communities undergo(figure 4). Therefore, future research is needed in which ing rapid changes because of biodiversity losses and the these dynamic interactions and the progression of physical– introduction of nonnative species and changes in physical biological systems toward equilibrium states are examined. processes occurring because of simultaneous altered hydroIndeed, there is a growing body of research in which logic regimes and climate change, there is a great need interactions between biology and geophysical processes for research to advance our understanding of how these are examined, particularly in the budding fields of ecogeochanges will affect landscape processes. morphology and ecohydrology. Research in these fields is increasing in importance as many of our most pressing Acknowledgments environmental problems (e.g., water quantity and quality of Funding was provided by National Science Foundation grant surface flows) are exacerbated by joint changes in biologino. DBI-1103500 to DCA. We thank Celia Miller, Hanna cal and physical processes. However, to increase our ability Naughton, and anonymous reviewers for comments that to understand these problems, we need to better integrate improved the manuscript. ecological principles into ecogeoscience research. We have suggested three basic ecological principles as future focus References cited areas, which will help address some of our key global change Allen DC, Vaughn CC. 2011. Density-dependent biodiversity effects on challenges: (1) We cannot accurately predict how the rapid physical habitat modification by freshwater biavalves. Ecology 92: 1013–1019. increase in extinctions and biodiversity losses will affect Archer S. 1989. Have southern Texas savannas been converted to woodlands geomorphic and hydrologic processes, nor do we know in recent history? American Naturalist 134: 545–561. how many species might be needed in order to restore these Balvanera P, Pfisterer AB, Buchmann N, He JS, Nakashizuka T, Raffaelli D, processes to a desired level of function as a part of larger Schmid B. 2006. Quantifying the evidence for biodiversity effects on efforts to restore degraded systems. This challenge can be ecosystem functioning and services. Ecology Letters 9: 1146–1156. 452 BioScience • May 2014 / Vol. 64 No. 5 http://bioscience.oxfordjournals.org Roundtable Bowker MA, Maestre FT, Escolar C. 2010. Biological crusts as a model system for examining the biodiversity–ecosystem function relationship in soils. Soil Biology and Biochemistry 42: 405–417. Brassard BW, Chen HYH, Cavard X, Laganière J, Reich PB, Bergeron Y, Pare D, Yuan Z. 2013. Tree species diversity increases fine root productivity through increased soil volume filling. Journal of Ecology 101: 210–219. Burri K, Gromke C, Lehning M, Graf F. 2011. Aeolian sediment transport over vegetation canopies: A wind tunnel study with live plants. Aeolian Research 3: 205–213. Cardinale BJ, Palmer MA, Collins SL. 2002. Species diversity enhances ecosystem functioning through interspecific facilitation. Nature 415: 426–429. Cardinale BJ, Matulich KL, Hooper DU, Byrnes JE, Duffy E, Gamfeldt L, Balvanera P, O’Connor MI, Gonzalez A. 2011. The functional role of producer diversity in ecosystems. American Journal of Botany 98: 572–592. Corenblit D, Steiger J, Tabacchi E. 2010. Biogeomorphologic succession dynamics in a Mediterranean river system. Ecography 33: 1136–1148. Davies NS, Gibling MR. 2010. Cambrian to Devonian evolution of alluvial systems: The sedimentological impact of the earliest land plants. EarthScience Reviews 98: 171–200. De Marchis M, Napoli E. 2012. Effects of irregular two-dimensional and three-dimensional surface roughness in turbulent channel flows. International Journal of Heat and Fluid Flow 36: 7–17. D’Odorico P, Laio F, Porporato A, Ridolfi L, Rinaldo A, Rodriguez-Iturbe I. 2010a. Ecohydrology of terrestrial ecosystems. BioScience 60: 898–907. D’Odorico P, Fuentes JD, Pockman WT, Collins SL, He Y, Medieros JS, DeWekker S, Litvak ME. 2010b. Positive feedback between microclimate and shrub encroachment in the northern Chihuahuan desert. Ecosphere 1 (art. 17). D’Odorico P, Okin GS, Bestelmeyer BT. 2012. A synthetic review of feedbacks and drivers of shrub encroachment in arid grasslands. Ecohydrology 5: 520–530. Dostál P, Weiser M, Koubek T. 2012. Native jewelweed, but not other native species, displays post-invasion trait divergence. Oikos 121: 1849–1859. Foley JA, Levis S, Prentice IC, Pollard D, Thompson SL. 1998. Coupling dynamic models of climate and vegetation. Global Change Biology 4: 561–579. Franks SJ. 2011. Plasticity and evolution in drought avoidance and escape in the annual plant Brassica rapa. New Phytologist 190: 249–257. Gillette DA, Pitchford AM. 2004. Sand flux in the northern Chihuahuan desert, New Mexico, USA, and the influence of mesquitedominated landscapes. Journal of Geophysical Research: Earth Surface 109. doi:10.1029/2003JF000031 Gordon WS, Huxman TE. 2007. Ecohydrology and climate change. Pages 113–128 in Wood PJ, Hannah DM, Sadler JP, eds. Hydroecology and Ecohydrology: Past, Present and Future. Wiley. Griffin JN, Byrnes JEK, Cardinale BJ. 2013. Effects of predator richness on prey suppression: A meta-analysis. Ecology 94: 2180–2187. Gurnell AM, Bertoldi W, Corenblit D. 2012. Changing river channels: The roles of hydrological processes, plants and pioneer fluvial landforms in humid temperate, mixed load, gravel bed rivers. Earth-Science Reviews 111: 129–141. Gyssels G, Poesen J, Bochet E, Li Y. 2005. Impact of plant roots on the resistance of soils to erosion by water: A review. Progress in Physical Geography 29: 189–217. Hoeppner SS, Dukes JS. 2012. Interactive responses of old-field plant growth and composition to warming and precipitation. Global Change Biology 18: 1754–1768. Hooper DU, Adair EC, Cardinale BJ, Byrnes JEK, Hungate BA, Matulich KL, Gonzalez A, Duffy JE, Gamfeldt L, O’Connor MI. 2012. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486: 105–108. Horn JD, Joeckel RM, Fielding CR. 2012. Progressive abandonment and planform changes of the central Platte River in Nebraska, central USA, over historical timeframes. Geomorphology 139: 372–383. http://bioscience.oxfordjournals.org Hudson JMG, Henry GHR, Cornwell WK. 2011. Taller and larger: Shifts in Arctic tundra leaf traits after 16 years of experimental warming. Global Change Biology 17: 1013–1021. Huxman TE, Wilcox BP, Breshears DD, Scott RL, Snyder KA, Small EE, Hultine K, Pockman WT, Jackson RB. 2005. Ecohydrological implications of woody plant encroachment. Ecology 86: 308–319. Jones CG. 2012. Ecosystem engineers and geomorphological signatures in landscapes. Geomorphology 157–158: 75–87. Kabat P, Claussen M, Dirmeyeter PA, Gash JHC, de Guenni LB, Maybeck M, Pielke RA Sr, Vörösmarty CI, Hutjes RWA, Lütkemeier S, eds. 2004. Vegetation, Water, Humans and the Climate: A New Perspective on an Interactive System. Springer. Larsen LG, Harvey JW, Crimaldi JP. 2007. A delicate balance: Ecohydrological feedbacks governing landscape morphology in a lotic peatland. Ecological Monographs 77: 591–614. Macarthur RH. 1958. Population ecology of some warblers of northeastern coniferous forests. Ecology 39: 599–619. Maestre FT, et al. 2012. Plant species richness and ecosystem multifunctionality in global drylands. Science 335: 214–218. Monger HC, Bestelmeyer BT. 2006. The soil-geomorphic template and biotic change in arid and semi-arid ecosystems. Journal of Arid Environments 65: 207–218. Mueller EN, Wainwright J, Parsons AJ. 2007. The stability of vegetation boundaries and the propagation of desertification in the American Southwest: A modelling approach. Ecological Modelling 208: 91–101. Nepf HM. 2012. Flow and transport in regions with aquatic vegetation. Annual Review of Fluid Mechanics 44: 123–142. [NRC] National Research Council. 2009. Landscapes on the Edge: New Horizons for Research on the Earth’s Surface. National Academies Press. Okin GS. 2008. A new model of wind erosion in the presence of vegetation. Journal of Geophysical Research: Earth Surface 113. doi:10.1029/2007JF000758 Okin GS, Gillette DA, Herrick JE. 2006. Multi-scale controls on and consequences of aeolian processes in landscape change in arid and semi-arid environments. Journal of Arid Environments 65: 253–275. Okin GS, D’Odorico P, Archer SR. 2009. Impact of feedbacks on Chihuahuan desert grasslands: Transience and metastability. Journal of Geophysical Research 114 (art. G01004). Osterkamp WR, Hupp CR, Stoffel M. 2012. The interactions between vegetation and erosion: New directions for research at the interface of ecology and geomorphology. Earth Surface Processes and Landforms 37: 23–36. Paquette A, Messier C. 2011. The effect of biodiversity on tree productivity: From temperate to boreal forests. Global Ecology and Biogeography 20: 170–180. Pasari JR, Levi T, Zavaleta ES, Tilman D. 2013. Several scales of biodiversity affect ecosystem multifunctionality. Proceedings of the National Academy of Sciences 110: 10219–10222. Petrie MD, Brunsell NA. 2012. The role of precipitation variability on the ecohydrology of grasslands. Ecohydrology 5: 337–345. Piotto D. 2008. A meta-analysis comparing tree growth in monocultures and mixed plantations. Forest Ecology and Management 255: 781–786. Potvin C, Gotelli NJ. 2008. Biodiversity enhances individual performance but does not affect survivorship in tropical trees. Ecology Letters 11: 217–223. Pregitzer KS, Burton AJ, King JS, Zak DR. 2008. Soil respiration, root biomass, and root turnover following long-term exposure of northern forests to elevated atmospheric CO2 and tropospheric O3. New Phytologist 180: 153–161. Reinhardt L, Jerolmack D, Cardinale BJ, Vanacker V, Wright J. 2010. Dynamic interactions of life and its landscape: Feedbacks at the interface of geomorphology and ecology. Earth Surface Processes and Landforms 35: 78–101. Rowe CLJ, Leger EA. 2011. Competitive seedlings and inherited traits: A test of rapid evolution of Elymus multisetus (big squirreltail) in response to cheatgrass invasion. Evolutionary Applications 4: 485–498. Schlesinger WH, Reynolds JF, Cunningham GL, Huenneke LF, Jarrell WM, Virginia RA, Whitford WG. 1990. Biological feedbacks in global desertification. Science 247: 1043–1048. May 2014 / Vol. 64 No. 5 • BioScience 453 Roundtable Schoener TW. 2011. The newest synthesis: Understanding the interplay of evolutionary and ecological dynamics. Science 331: 426–429. Simon A, Collison AJC. 2002. Quantifying the mechanical and hydrologic effects of riparian vegetation on streambank stability. Earth Surface Processes and Landforms 27: 527–546. Stachowicz JJ. 2001. Mutualism, facilitation, and the structure of ecological communities. BioScience 51: 235–246. Tilman D. 1981. Tests of resource competition theory using four species of Lake Michigan algae. Ecology 62: 802–815. Tilman D, Knops J, Wedin D, Reich P, Ritchie M, Siemann E. 1997. The influence of functional diversity and composition on ecosystem processes. Science 277: 1300–1302. Tooth S, Nanson GC. 2000. The role of vegetation in the formation of anabranching channels in an ephemeral river, Northern plains, arid central Australia. Hydrological Processes 14: 3099–3117. 454 BioScience • May 2014 / Vol. 64 No. 5 Verheyen K, Bulteel H, Palmborg C, Olivié B, Nijs I, Raes D, Muys B. 2008. Can complementarity in water use help to explain diversityproductivity relationships in experimental grassland plots? Oecologia 156: 351–361. Wang Z, Hou Y, Fang H, Yu D, Zhang N, Xu C, Chen M, Sun L. 2012. Effects of plant species diversity on soil conservation and stability in the secondary succession phases of a semihumid evergreen broadleaf forest in China. Journal of Soil and Water Conservation 67: 311–320. Daniel C. Allen ([email protected]) is affiliated with the School of Letters and Sciences at Arizona State University, in Mesa. Bradley J. Cardinale is affiliated with the School of Natural Resources and Environment at the University of Michigan, in Ann Arbor. Theresa Wynn-Thompson is affiliated with the Department of Biological Systems Engineering at Virginia Tech, in Blacksburg. http://bioscience.oxfordjournals.org