Developing Conceptual Frameworks for the ... - Semantic Scholar

Norman D. Yan, Brian Leung, (W.) Bill Keller, Shelley E. Arnott, John M. Gunn, and Gunnar G. Raddum

Developing Conceptual Frameworks for the Recovery of Aquatic Biota from Acidification Surface water acidity is decreasing in large areas of Europe and North America in response to reductions in atmospheric S deposition, but the ecological responses to these water-quality improvements are uncertain. Biota are recovering in some lakes and rivers, as water quality improves, but they are not yet recovering in others. To make sense of these different responses, and to foster effective management of the acid rain problem, we need to understand 2 things: i) the sequence of ecological steps needed for biotic communities to recover; and ii) where and how to intervene in this process should recovery stall. Here our purpose is to develop conceptual frameworks to serve these 2 needs. In the first framework, the primarily ecological one, a decision tree highlights the sequence of processes necessary for ecological recovery, linking them with management tools and responses to bottlenecks in the process. These bottlenecks are inadequate water quality, an inadequate supply of colonists to permit establishment, and community-level impediments to recovery dynamics. A second, more management-oriented framework identifies where we can intervene to overcome these bottlenecks, and what research is needed to build the models to operationalize the framework. Our ability to assess the benefits of S emission reduction would be simplified if we had models to predict the rate and extent of ecological recovery from acidification. To build such models we must identify the ecological steps in the recovery process. The frameworks we present will advance us towards this goal.

INTRODUCTION Across western Europe and eastern North America, rates of acid deposition are falling in response to reductions in atmospheric S emissions (1, 2), and the water-quality of acidified surface waters is beginning to improve (1, 3). To aid in the management of S emissions, biogeochemical models designed to predict socalled “critical acid loads” (4), are now being used to predict the degree of water-quality improvements which should accompany particular reductions in emissions (5, 6). For their part, ecologists are actively seeking to quantify the rates and trajectories of biotic recovery from historical acidification (7–11). The initial results are promising. Acid-sensitive species either lost to acidification or missing from historically acidified waters are appearing. This suggests that we should be able to combine biotic recovery models with critical load models to produce a more complete “picture” of the recovery process, and better predictions of its future. Ultimately, the ecological, i.e. the biotic, components of these models should have 2 functions: i) to identify when, where and for which biota recovery should occur; and ii) to identify where recovery might not occur without active management including, but not limited to additional S emissions reductions. The construction of such ecological recovery models logically proceeds from simple, first generation empirical models to more complex process-oriented models, assuming the latter will be more accurate. A few first generation models have already been developed. They couple biogeochemical models, which predict Ambio Vol. 32 No. 3, May 2003

future time trends of lake alkalinity or pH, with regression models that employ these time trends to predict changes in the occurrence or abundance of indicator taxa. The regression models are developed from synoptic surveys of biota in lakes that vary in acidity (5, 12). These first generation models operate under one key assumption—that the historical spatial patterns of biota in acidified landscapes provide sufficient information to predict future patterns of recovery. To the extent that this assumption is true, the models’ predictions should be good; however, to the extent that it is false the predictions of recovery will be flawed. Recent reviews (13, 14) indicate that rates and trajectories of recovery may be influenced by many different factors which might not be well-captured in the patterns observed in historical surveys. It is quite possible, for example, that some damaged ecological communities may be resistant to change despite waterquality improvements (14, 15). If we wish to build accurate models to predict the recovery of acidified lakes, we must begin by understanding the key processes that influence the rates and trajectories of recovery. Hence, our first objective is to construct a conceptual framework which summarizes our current understanding of the ecological processes that influence the recovery of acid-sensitive species in historically acidified lakes. While we believe the processes in the framework are generally applicable, we employ crustacean zooplankton as model species, given our wealth of acidification and recovery knowledge on these organisms (10, 13, 14, 16, 17). Given that understanding should both spur and improve action, we also provide a second framework with more of a management orientation. It embeds our understanding of key ecological recovery processes into a larger framework which identifies needed ecological models and recovery bottlenecks which can be countered by appropriate management. In summary, we have 3 objectives: i) to construct a conceptual framework of the ecological recovery sequence from historical acidification; ii) to identify points in the process where management interventions might be required to overcome recovery bottlenecks; and iii) to identify the models which will be needed to operationalize the framework. The lake-liming literature provides a starting point for the identification of the processes in ecological recovery. One key observation that emerges from this literature is that the pace and extent of recovery is critically influenced by the severity and duration of antecedent damage—the more severe the damage, the slower and less complete the recovery. The probable explanation is that colonist availability is reduced when damage is severe and long-lasting (8, 18). With this proviso, the liming literature provides quite positive evidence that periphyton (19), phytoplankton (20, 21), zooplankton (8, 22), littoral micro- (16) and macrobenthos (9) and fish (23, 24), might eventually recover following reductions in habitat acidity. We must, however, be cautious in our use of the liming literature to build a conceptual recovery framework, because there are geochemical and ecological reasons why liming might not perfectly simulate the natural recovery process. For example, Ca concentrations influence zooplankton species composition in soft-waters (25), and Ca levels increase after liming while they fall in response to reduced S deposition (1, 26). For Ca-rich taxa, falling Ca levels may partially offset the benefits of rising pH as S deposition decreases,

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but liming does not simulate this process. Colonist delivery is dles that species must overcome during the recovery process. also a key process in recovery from severe ecological damage While the first framework (Fig. 1) focuses largely on ecologi(27, 28), and here again, liming may be an imperfect recovery cal processes involved in recovery, the second framework (Fig. simulator because local lake liming will not increase regional 2) focuses more on 2 classes of management needs. It identifies colonist pools, while widespread reductions in S emissions just where in the recovery process specific actions might be taken may. Then there is the complex issue of community re-assem- to speed recovery or get it back on track, and it identifies the bly given differing lethal acid thresholds of different biota. sorts of models which are needed to predict the progress of reLiming produces more rapid water-quality changes than does covery, on the assumption that ecological recovery will likely natural recovery, essentially opening the habitat to all potential not simply mirror the damage process. colonists at the same time, not to a gradually increasing number While our interest is in restoring ecological communities, the of more acid-sensitive colonists accompanying slow water-quality improvement. In ecological reFigure 1. First conceptual framework of the processes in the ecological recovery of construction, invasion sequence matters (29). a species from historical acidification. Yellow boxes indicate actions of management Hence, while the evidence of ecological recovery agencies. Blue boxes reflect questions managers must answer about key ecological in the liming literature is encouraging (9) it may processes which influence recovery. Negative answers highlight bottlenecks to recovery, which may require management interventions to speed recovery (see Fig. 2). not be a perfect guide for the ecological recovery The framework begins with an assumption of a particular S deposition rate producing process (8, 30) that will follow reductions in S a particular water quality. This assumption is relaxed in Figure 2. emissions. If we exclude the liming and the fluvial recovery literature (11), the evidence for ecological recovery from acidification is quite sparse. Only in the region of Sudbury, Ontario, Canada, are water-quality improvements in lakes sufficiently advanced that we can begin to document rates and trajectories of ecological recovery, and even here recovery is proceeding at different rates in different lakes (17, 31). To understand such inter-lake differences, we must carefully consider the factors that regulate the recovery of biota from historical damage. Ecologists do understand the processes that influence the recovery of biota from historical damage. Recovery clearly requires the reestablishment of suitable habitat conditions, i.e. in our case the return of good water-quality. Recovery rates are also influenced by the severity and duration of historical damage, largely because they control the local availability of colonists either in habitat refuges or in egg banks. If there are no relict populations, then recovery is influenced by the distance to sources of colonists, their rates of production, and the ease of and barriers to their disFigure 2. Second conceptual framework of the recovery process. This framework persal. Finally, recovery will be influenced by the highlights the interactions between ecology, research, and management, i.e. colonization success of propagules, and by subrespectively, the ecological processes in recovery, the models needed to predict sequent rates of population growth, i.e. by the birth changes in process rates, and the potential management intervention that may be considered. Arrows principally indicate the flow of effects including feedbacks, e.g. and death rates of colonists and their offspring (27, colonization influences the probability of establishment, and species once estab28). While ecologists that are studying recovery lished may in turn alter habitats. Arrows also represent the flow of information, when of biota from acidification understand these proclinked with models. esses, they have not formalized this understanding (see Raddum et al.). Here we develop a conceptual framework of the recovery process to provide that formalization. Further, we embed the ecological processes into a large framework which identifies the ecological models which will be needed to operationalize the framework, and the management interventions which might speed recovery if it should stall. Frameworks of Ecological Recovery from Acidification The first framework is constructed from that sequence of steps that must occur if recovery is to proceed from a state of severe ecological damage, as was observed in many Sudbury-area lakes several decades ago (32–34), to complete community re-assembly. We represent this framework (Fig. 1) as a decision tree—a series of questions whose dichotomous answers reflect the key ecological hur166

Establishment and population growth to recovery targets


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framework assumes that ecological recovery is best understood as a species-level phenomenon, because thresholds and rate processes required for recovery differ substantially among species. For example, acid-sensitivity varies dramatically among species (35, 36), as does colonist vagility (37, 38), and the rate of production of colonists. Hence, the framework begins with the selection of one or more indicator species, presumably acid-sensitive species that were important components of their communities in nonacidic lakes prior to acidification. For the Sudburyarea zooplankton, Daphnia mendotae would make a good species choice (39). Once the species are selected, recovery targets are identified (Figs 1 and 2). The targets might be a range in frequencies of occurrence or abundances of the species (8, 36) or of groups of species (16, 17, 31) observed prior to acidification, or observed in nonacidic lakes in the region (40). As we presume that preacidification data are rarely available for the purpose of target setting, the latter choice may be the more common one. The next step is the quantification of the lethal acid thresholds, or more properly lethal water-quality thresholds, of the selected taxa (Fig. 1). These thresholds are available for many species (e.g. 41). They are most trustworthy where there is good agreement between lab and field-derived values (36, 39). The thresholds could, of course be multivariate, including not just pH and/or alkalinity, but also such parameters as Ca and monomeric Al (35). The next step in framework 1 is a comparison of the lethal water-quality thresholds, with the measured, or modeled, waterquality of the particular lake. Biogeochemical models are available which can be used to predict time courses of water-quality improvement in historically acidified lakes (eg. 5, 6). If the ambient water quality is too poor to permit the survival of the selected taxa, recovery will not occur, even if colonists are available (42). The only option in this case is to await further waterquality improvement, in the case of framework 1. In framework 2, liming or further S emission reductions would be options at this point. If the water quality is adequate for the selected taxa we proceed to the question of the availability of colonists. Given suitable water quality, a key factor that speeds the recovery of biota from acidification is the ready availability of colonists (10, 18). Readily-available colonists include those in sediment egg banks (43, 44), in relict populations in habitat refuges, or in upstream lakes or rivers that did not acidify (45). If

Figure 3. An estimate of the probability of colonist establishment (Pe), for different numbers of arriving colonists (N) assuming that Pe = 1-(d/ b)N, where d and b are instantaneous death (d) and birth (b) rates, respectively. The equation is solved for small differences in d/b to show how important such small differences may be. Note that the curves have an asymptotic form because Allee effects are excluded.


such colonists are available recovery may proceed quickly (8, 10). If colonists are not readily available, only natural immigration (Fig. 1) or human introductions (Fig. 2) can supply them. Dispersal models, such as reaction-diffusion (46) or gravity models (47) can be employed to predict natural or unintended but human-assisted rates of dispersal. Ecologists are actively involved in the production and evaluation of such models (48, 49), but no one has developed them to predict the rate of return of colonists to acidified lakes. This would clearly be useful (Fig. 2), because dispersal abilities vary dramatically among species (50). Zooplankton are dispersed via the wind (51), waterfowl (52), and human activities (53), and at global scales it appears that human-assisted dispersal of Cladocera to North America has increased natural rates of dispersal by many thousands of times (54). For zooplankton there is growing evidence that dispersal of colonists at small geographic scales is usually not the rate-limiting step governing the presence of species in particular lakes. In both large (55) and small lakes (56), and in experimental enclosures (42) the vast majority of introductions fail. The survival of colonists is usually poor, and several recolonization events are required to establish a population. Conservation ecologists attribute the extinction of small populations, and the frequent failure of colonists, to either stochastic or Allee effects. Population viability analyses (PVA) incorporate stochastic variations in birth and death rates to estimate minimum viable population sizes that will prevent extinction of small populations (57). Estimating this minimum viable population size is a “hot” topic (58–60). Only small differences between birth and death rates may permit either the persistence or the disappearance of small founding populations in PVA models. For example, assuming that birth and death rates are the sole determinants of persistence, we can estimate the probability of persistence (Pp) as 1– the probability of extinction (Pe), where Pe = (d/b)N, the ratio of instantaneous death over birth rates raised to the power of the number of introduced colonists (61). If dispersal rates were high, and the probability of colonist establishment were restricted only by stochastic effects, it is clear that dispersal and colonization would not be bottlenecks to ecological recovery in many cases, because very small excesses of birth over death rates lead to high probabilities of establishment with as few as 10 colonists (Fig. 3). Given this background, the next step in framework 1 is to compare the numbers of colonists or the size of the relict population, with an estimate of the minimum viable population size of the selected species (Fig. 1). While models predicting minimum viable population size are available in the conservation biology literature (62), the field is in active flux (63), and the models have not previously been employed to predict needed colonist or relict population sizes of biota in acidified lakes. There is no theoretical reason why this cannot be done. Unfortunately for the colonists, it appears that stochastic factors are not the only hurdle to population establishment. When populations are very small they become particularly vulnerable to extinction for demographic reasons, e.g. fertile adults having low encounter rates, for genetic reasons, e.g. inbreeding depression, or for ecological reasons, e.g. individuals suffering a disproportionately high predation at low population sizes (64). These causes of extinction of small populations are called Allee effects. They have the consequence of causing a threshold number of propagules below which extinction is likely, and above which the population will grow very quickly (Fig. 2b). While there are a few models to detect the Allee effect in natural populations, such work is scarce. Methods need to be developed to identify the Allee effect thresholds, and to characterize the uncertainty in its identification. If such models can be developed the ability to predict establishment of colonists, and thus ecological recovery, will be greatly improved.


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Using the Frameworks Our purpose was not to build a recovery model. Rather it was to produce a conceptual framework of the recovery process, in order to begin to move beyond first generation models of biotic recovery from acidification. We believe such movement is needed both for more accurate predictions of spontaneous recovery rates, and for more informed management interventions to foster recovery when it stalls. Two frameworks were developed to fulfill these needs—one focusing on the ecological processes themselves, and a second imbedding these processes in a larger management context. Despite promising evidence of recovery, we believe it is too early to conclude that all damaged communities, particularly those which were severely damaged, will completely re-assemble themselves once water-quality improves. Biota that disperse rapidly may quickly re-appear, but not all biota disperse rapidly, and even if they could, priority or founder effects in community assembly appear to be strong (29). It may be necessary to manipulate assemblages to reduce the resistance of damaged communities to change. We offer the first framework as a step in identifying the ecological processes underlying recovery, and the second framework as a tool to identify the needed models to quantify these processes. Even at this early stage we believe the frameworks have several uses. They begin the process of identifying the sequence of steps involved in recovery of biota from acidification. They may help identify lakespecific bottlenecks in the recovery process, and differences in recovery rates among lakes. They can be used to identify under what circumstances management interventions beyond S emission control may be required to assist recovery, and to help assess the types and frequencies of the required interventions. They can help identify which potential recovery models reflect our understanding of underlying processes of recovery. Finally, they may be used to help understand the influence on recovery of confounding variables, such as UVR radiation (67) and climatic variability (68). A great deal of time and effort has been expended to develop models to predict changes in lake alkalinity and pH in the face of acid deposition. Given that acidification damages biota, and their recovery does not automatically follow water-quality improvement, it is time to allocate time and resources to the development of ecological recovery models. This process must begin with what we have begun to do here, i.e. identifying the sequence of key underlying ecological processes. 168

Figure 4. Examples of 3 possible functional relations between colonizer number and probability of establishment when Allee effects are a) absent and b) present. As the number of colonizers increases, the probability of a population becoming established increases. However, when Allee effects are absent (a), each colonizer has an independent chance of founding a new population, resulting in asymptotic curves. When Allee effects are present (b), there are interactions between colonizers, such that the probability of establishment is disproportionately low for low colonizer number, but rises quickly beyond some threshold. This results in a more logistic curve. The location of the threshold may be crucial for establishment.

Probability establishment

a

Number colonizers

b Probability establishment

If the water quality is suitable, and enough individuals are available to sustain a population in the face of both stochastic failures and Allee effects, only one process remains in the framework, i.e. population growth from the founding or relict size to the recovery target. There is however, one remaining “black box” to be considered—confounding community-level factors beyond those incorporated into Allee effects (Fig. 1). Many communitylevel phenomena such as predation and parasitism, may alter the birth and death rates of populations. Quantifying biotic interactions is a core activity of ecologists, and if these interactions had to be considered on a lake-by-lake basis, the task of predicting regional recovery would be hopeless. However, we suspect they will only have to be considered on a community-type basis in order to predict recovery. Natural communities often exhibit discontinuities in community composition in response to the effects of keystone predators (e.g. 14, 15, 65). For example, both liming (14, 66) and natural recovery studies (31) indicate that invertebrate community composition does not recover until normal fish predation patterns are re-established. Hence, the framework will have to be operationalized differently for at least 2 classes of lakes; those with and those without normal fish assemblages. Identifying the recovery responses of these and perhaps other important classes of communities is a challenging but far from a hopeless task.

Number colonizers

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Norman Yan is an associate professor at York University. He works in partnership with the Ontario Ministry of Environment’s Dorset Environmental Science Centre. His research focuses on the impacts of multiple stressors on freshwater zooplankton. His address: Department of Biology, York University, 4700 Keele Street, Toronto, Ontario, Canada, M3J 1P3. E-mail: [email protected] Brian Leung is a research assistant professor at the University of Notre Dame. His current research focuses on ecological forecasting and bioeconomic risk analysis of invasive species, using mathematical, computational, and statistical models. His address: Department of Biological Sciences, University of Notre Dame, Notre Dame, IN 46556-0369 USA. E-mail: [email protected] Bill Keller is the senior environmental scientist – Northern Lakes, with the Ontario Ministry of Environment, and is an adjunct professor in the Department of Biology, Laurentian University. His studies focus on documenting and understanding the recovery process in damaged aquatic systems, and on investigating the combined effects of multiple stressors on lakes and on lake recovery. His address: Department of Biology, Laurentian University, Sudbury, Ontario, Canada, P3E 2C6. E-mail: [email protected] Shelley Arnott is an assistant professor in the Department of Biology at Queen’s University. Her research focuses on determining the chemical and ecological factors that influence the recovery of aquatic biota from historical acidification in the face of climatic variability and the spread of nonindigenous species. Her address: Department of Biology, Queen’s University, Kingston, Ontario, Canada, K7L 3N6. E-mail: [email protected] John Gunn is a fisheries research scientist with the Ontario Ministry of Natural Resources and heads up the Cooperative Freshwater Ecology Unit at Laurentian University. He specializes in restoration ecology of aciddamaged ecosystems as well as the ecology of salmonid fishes of Ontario. His address: Department of Biology, Laurentian University, Sudbury, Ontario, Canada, P3E 2C6. E-mail: [email protected] Gunnar G. Raddum is an associate professor at the University of Bergen. He is Head of the unit: Laboratory for Freshwater Ecology and Inland Fisheries, Zoological Institute, UiB. His main interest is the effects of acidification, liming and restoration of freshwater ecosystems in general. His address: University of Bergen, Institute of Zoology, Allegt. 41, N-5007 Bergen, Norway. E-mail: [email protected]


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