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Survey research in conservation biology

Survey research in conservation biology C. R. Margules Haila. Y. and Margules. C. R. 1996. Survey research in conservation biology. Ecography 1 9 323-331. We present systematic arguments for the necessity of field survey in conservation biology. Preservation of biological diversity has become a major challenge in conservation biology, but to comprehend diversity, ecologists have to obtain information on what units the ‘diversity’ of different parts of the world consists of, where these units are, and how they respond to natural and human-induced environmental change. To reach this end, systematic survey procedures need to be developed that incorporate data collecting, data analysis and conclusions about distributional patterns as well as management recommendations into an iterative process that is corrected as experience accumulates. The appropriate survey design depends on the task and needs to be fixed separately in each case; developing long-term observational systems is no less challenging a task than developing experimental systems in laboratory research, or modeling systems in theoretical research. We conclude the paper with five principles of ecological survey. A common denominator of these principles is the need to make explicit decisions at each step so that errors and insufficiences can be corrected later. Y. Haila. Dept of Regional Studies, Enuironmentul Policy, Uniu. of Tamprrr, P.O. Bu.r 607, FIV-33101 Tampere. Finland. - C. Margules, CSIRO. Wildlgk und Ecology. P.O. Box 84, Lyneham, ACT 2602. Australia. A major challenge for conservation biology is to formulate practicable strategies for preserving the biological diversity of the earth, transformed and impoverished by human activities at an increasing rate. Unfortunately, it is not clear what this challenge exactly means. This is because “biological diversity” is a complex, descriptive notion. That ecological phenomena are “diverse” means that different situations usually look different. Furthermore, human transformations of nature merge with natural processes: there are no absolute standards to distinguish between human-induced and natural change (Haila and Levins 1992). Nevertheless, conservationists should be able to give, in specific situations, both theoretically sound and practically relevant answers to the question, Which variations in diversity matter, and from which perspective? In this essay we promote systematic field survey procedures as one adequate way to respond to this challenge. We adopt a broad view of survey as any systematic collecting of observations on patterns of variation in the presence or abundance of species, or even behaviour of individuals. This view also includes monitoring as repeated survey. Survey research requires the development of theory, methods and analytical techniques accompanying the systematic collection of field records and associated analyses for the purpose of mapping the abundance and range of species or some surrogate such as communities or species assemblages. Biodiversity is customarily divided into three main domains: the diversity of genes in populations, the diversity of populations in ecosystems, and the diversity of ecosystems in landscapes and biological regions (e.g. Wilson 1988, Solbrig 1991). Different biological processes maintain and reproduce biodiversity in the different domains, and several alternative criteria can be used for assessing diversity depending on which one of these Accepted 19 December 1995 Copyright Q ECOGRAPHY 1996 ISSN 0906-7590 Printed in Ireland - all rights reserved ECOGRAPHY 193 (19%) domains is primarily emphasized. The notion of ‘biodiversity’ is thus theory-laden: it builds upon presuppositions about the organization of nature into levels and about processes that operate on these levels (Haila and Kouki 1994). Consequently, every attempt to ‘operationalize‘ biodiversity preservation as research programs or management recommendations requires both conceptual and analytical specification as to what particular aspect of biodiversity is in focus. The emphasis in conservation biology has mainly been on populations of single species (e.g. Caughley 1994). The need of adequate inventories has been emphasized recently (see Margules and Austin 1991, 1994, Ricklefs and Schluter 1993, Hawksworth 1994), but the data available for describing global biodiversity patterns let alone for drawing conclusions on processes that maintain those patterns are grossly inadequate; as Schluter and Ricklefs (19935) put it: “Up to this point, studies of diversity have not produced samples of sufficient data to resolve this hierarchy of structure in ecological systems.” From the perspective of modern theoretical ecology conducting surveys may appear as a mundane activity that does not deserve the attention of a pure scientist. Yet it is this body of knowledge - recorded field observations - that both gives rise to and acts as an informal test of most current ecological theory. Field survey gives data both for basic science and practical management, and the quality of the data requires systematic attention. Ultimately, collecting data. interpreting data. theoretical conclusions and practical management should be integrated together into an ongoing. iterative process in conservation biology. analytics, translation by Warrington 1964). “as we cannot know the reason for a fact before we know the fact, we cannot know what a thing is before knowing that it is”. This principle is obvious in the case of biological diversity. Descriptive naturalistic work laid the basis for modern evolutionary biology in the 19th century as well as for the “modern synthesis” which eventually gave rise to an evolutionary understanding of ecological phenomena. Naturalism was important in the background of, for instance, Chetverikov, Dobzhansky, Mayr, Simpson, Stebbins; see Mayr and Provine (1980) and Bock (1994). This is equally true of the interest in biodiversity. As a matter of fact, systematic observation, giving fuel for induction, is, in an historical perspective, the only possible starting point for the development of scientific theories (Hacking 1983). Second, the role of descriptive research does not stop with recording what there is in the world. It also has a systematic dimension, which is mapping the domain of existence of the phenomena of interest. We need to know where ‘biodiversity’ is as well as what it looks like. It is particularly important to define diagnostic criteria for drawing distinctions in order to answer questions such as. ‘When is the object observed at present different from the previous one?’ Measurement is a solution to this dilemma, but every measurement is, of course, based on a decision as to what particular aspect of the phenomenon is worth measuring. and how this can be done. The problems of characterizing numerically the ‘diversity’ of ecological collections have been reviewed many times (e.g. Williams 1964, Hurlbert 1971, May 1975. Huston 1994). Note that conceptual innovation has been more important in this endeavour than purely technical development; for instance, the separation of local (alpha-). The necessity of descriptive research in ecology between-site (beta-) and regional (gamma-) diversity from each other by Whittaker (1972). and the developIn tandem with the growth of modem, analytically ment of methods for standardizing sample size to allow oriented ecology since the 1960’s. traditions of descrip- comparisons of species richness in different samples tive investigation of nature fell into relative oblivion (Simberloff 1978). T e e were conceptual developments hs (Kingsland 1985), despite some eloquent statements in that gave structure to observational work. their defence (e.g. Simberloff 1982). Natural history has However, biological diversity cannot be evaluated sometimes even been presented as a pejorative opposite just by ‘measurement’, that is, different sites cannot be of “scientific” ecology (Peters 1990; for a criticism, see attributed value only. on the basis of the level or Haila 1996). Such a position is, however, untenable: the amount of diversity. Rather, biological diversity in understanding of ecological phenomena builds neces- different sites is similar or different. Adequate reference sarily upon a strong observational component. Modem areas are needed particularly for the assessment of analytic ideals about research methodology ought to be biodiversity in human-modified areas. This may be integrated, not placed in opposition, with this particular straightforward in regions such as the northern boreal feature of ecology which, furthermore, is shared with forest which still have relatively natural references left, many other branches of science (Mayr 1982, Haila but very complicated in intensively human-modified 1992). In the following we elaborate upon four particu- parts of the world. This further emphasises the need of lar reasons to conduct descriptive ecological research, a systematic approach in defining the purpose and and illustrate them with the example of biodiversity. design of biodiversity survey work. First of all, descriptive work is required to find out, In addition, there are practical issues that demand what there is. As Aristotle noticed (Prior and posterior attention. A thorough inventory, for instance a com324 ECOGRAPHY 1 9 3 (1996) plete enumeration of all species from all possible locations. is not an option. Most species have not been described and named. Even if they were, determining all distribution patterns would be an impossible demand on available resources. It is only possible to record a sample of biological diversity, and that sample from only a sub-set of all possible locations. The issues then are to decide which components of the biota (sub-sets of species, etc.) should be sampled, and to design a strategy for surveying the geographic region of interest for those identified components of the biota. On this level also practical considerations such as ease of identification and consistency of sampling are important (e.g. Greenslade and Greenslade 1984, Cranston 1990). Yet the issue of survey design has received too little attention, with only a few notable exceptions (Gillison and Brewer 1985, Austin and Heyligers 1989, 1991). Third, descriptive research acquires an analytic dimension when it is asked, ‘What is the phenomenon of interest consistent with? For instance, when patterns of diversity variation on the local scale have been described, using Whittaker’s distinctions, it becomes possible to ask questions about processes that might produce such patterns: site-specific, local, regional and global processes differentiate from one another; this is discussed in several chapters in Ricklefs and Schluter (1993). What is more important, it becomes possible to exclude some alternative explanations. For instance, when it is observed that breeding birds colonize a set of forest fragments strictly as a function of background abundances, the hypothesis that some particular ‘area effects’ influence the system can be rejected (Haila et al. 1993). In other words, results of systematic surveys can be used to test underlying presuppositions; this is what Haila (1988) called “analytic description”. If an observed pattern is discordant with an expectation that follows from a particular process-hypothesis, then the hypothesis is unlikely to be relevant. As a conception of scientific activity, this matches and expands the ‘hypothetico-deductive’ model expounded by Popper (1935). Science requires criticism through refuting hypotheses, but also systematic criticism of the conceptual presuppositions upon which particular hypotheses are built (Haila and Levins 1992). The ‘analytic dimension’ of decriptive research relates to the following problem. To tease apart the elements of biodiversity ecologists have to obtain information of two kinds. First, we have to know what species (genotypes, communities, or whatever units are used) there are. where they are and how numerous they are. These are ecological patterns. Second, we need to know why they are where they are, and how they respond to natural and human-induced environmental change. This will identify the ecological processes that produce and reproduce the patterns. This is the familiar patternECOCRAPHY 1Y3 (199696) process distinction, which is a characteristic feature of ecology in a far stronger sense than of laboratory sciences (Watt 1947. Wiens 1984, Austin 1986, 1987, Birks 1986. Haila 1992). Ecological patterns are detected by surveys whereas experiments are needed to understand and predict ecological processes (Austin 1987, 1991). It deserves emphasis, however, that in research on biodiversity, which often entails comparisons across time and space, the task of designing proper experiments may be formidable (Nicholls and Margules 1991, Margules 1992, Underwood and Petraitis 1993). Fourth, descriptive research has a practical dimension by providing answers to the question, ‘What should or could be done to/with the phenomenon of interest’!’ In biodiversity research, the detection of patterns often has practical priority over understanding processes, for two reasons. First, it is no use .developing management strategies to prevent extinctions and protect biological diversity if the sites being managed do not contain the full complement of species to be preserved, or at least as many as possible, in the first place (Margules et al. 1988, 1994, Margules 1989). Second, patterns such as the rarity of a particular set of species or a particular type of environment give, per se, cause for concern. Recently, innovative methods have been developed to assess the relative priorities of areas for biodiversity protection, based on the complement of features (e.g. species, communities, ecosystems, etc.) that different areas possess (Margules et al. 1988, 1994, Vane-Wright et al. 1991, Pressey et al. 1993, Williams et al. 1996). The data sets available for these methods to work on are collections of field records, and most are collections held in museums and herbaria. Unfortunately, such records are to a greater or lesser extent biased samples of real distribution patterns. They have been collected in an opportunistic manner, the taxa noted were the ones of interest to the collector and the places they were recorded from are places where the collector expected to find them, or which were conveniently accessible (Margules and Austin 1994). In the face of numerous imminent and irretrievable planning and policy decisions affecting biodiversity it is necessary to make full use of such data. Nevertheless, the limitations should be acknowledged and new field surveys should be implemented to reduce bias in existing collections. A philosophy of ecological survey The tradition of making systematic descriptions of the living world goes back to the early modem times; the locus classicus in the English speaking world is Gilbert White’s (1720-1793) book ‘The Natural History and Antiquities of Selborne, in the County of Southampton’ (see Worster 1985). However, surveys using systematic sampling methods were started only in the late 19th 325 century (Mclntosh 1985. Haila 1992). The early surveys derived inspiration from variable and often idiosynchratic sources such as the nationalistic maxim, ‘know your native country’, but the results of purely descrip tive work have often proved valuable. For instance, the quantitative bird censuses conducted in Finland by Einari Merikallio, a secondary school biology teacher who counted birds during his summer holidays from the 1920’s through the mid-1950’s. have given a baseline for studying long-term changes in northern European avifauna (Jarvinen and Vlisiinen 1977. 1978). The modern challenge is somewhat different: The task is to collect representative samples from a particular region for a defined purpose. This requires adoption of a systematic approach, derived from the particular task at hand. We highlight this requirement with the work of McKenzie et al. (1989) on a conservation plan in the Nullabor region in southern Australia, a huge and poorly known arid area. The survey phase of the work included the following elements: 1) assessment of geographic distribution patterns of selected species over the whole region by field surveys; 2) derivation of assemblage patterns from the survey results on the basis of coexistence of different species in the survey sites; 3) interpolation of these “assemblage” patterns over the region;’and 4) field validation of the pattern on the basis of an additional data set. The project resulted in a suggestion of a reserve network that would fully represent biodiversity in the region. The planning and realization of the survey built upon three assumptions. First, it was assumed that the distribution patterns of the taxa selected for the survey could be adequately sampled with an environmental stratification of the whole area. Second, the “assemblages” of co-occumng taxa, although put together as a pure sampling collection, were assumed to give an adequate surrogaie for modeling the environmental determinants of diversity variation over the region. Third, the additional field data collected after the modeling phase were deemed adequate for assessing the representativeness of the pattern. This example demonstrates that any systematic survey research is necessarily based upon a variety of assumptions and decisions which are of both methodological and substantive character. It is our thesis that this is always the case in systematic survey research. Medawar (1965) eloquently distinguished between two competing images of science. One emphasizes the creative imagination of the scientist who makes speculative adventures into the unknown. The other views the task of the scientist essentially as one of reporting systematically, with the help of the scientific method, what there is in nature. But Medawar also showed that scientific activity really requires both: “In as much as these two sets of opinions contradict each other flatly in every particular, it seems hardly possible that they should both be true; but anyone who has done or reflected deeply upon scientific research knows that there is in fact a great deal of truth in both of them. For a scientist must indeed be freely imaginative and yet sceptical, creative and yet a critic. Therc is a sense in which he must be free, but another in which his thoughts must be very precisely regimented; there is poetry in science, but also a lot of bookkeeping” (p. 32). What are the lessons for biological surveys? The first is to shake off any notion that what is worth observing can be taken for granted. Free of this notion the surveyor can be imaginative and use her or his knowledge of ecological patterns and understanding of ecological processes to decide where in the landscape, and when in time, field observations should be collected so that they illuminate the purpose of the survey. Second, on the other hand, survey data should be evaluated systematically and critically. One should ask, following data analysis. was the design appropriate to the question being asked and did the design enable relevant ecological patterns to be discerned? The answer is usually ‘not entirely’ or ‘not completely satisfactorily’. Thus, there is a third lesson, which is that the design of subsequent surveys, in the same or a different place. should be conditioned by the results of prior surveys. Survey work is a long-term, iterative process. The scale of survey The issue of scaling. in particular, poses challenges for field survey (Wiens 1989). A theoretical problem is to decide what size the units under consideration are; this essentially fixes the scale of the study. The methodological problem that follows is to define an appropriate ‘observation window’ (Rosen 1977). It is, obviously, impossible to assess long-term patterns on one-year data, but judging how many years of data collecting are needed is far from trivial. The number and distribution of sampling sites needed for assessing spatial patterns presents an analogous problem. On the continental scale, spatial units can be distinguished by clearly discernable gross variations in ecological patterns, i.e. biogeographic regions. There is a long history of biogeographic regionalisation in continental Europe, particularly Russia (see, e.g. Isachenko 1973 for a review: Larson 1986), but in the U.K.and north America land classification and regionalisation did not begin in earnest until the 1930’s (Bourne 1931, and Veatch 1930 respectively). On the regional scale, within biogeographic regions, appropriate spatial units might be landscape types. The physical environment is a “structure” on which the “patterns” of species assemblages are superimposed (Margalef 1979). One might identify spatial units by using geophysically defined landscape types directly or by using species assemblages as surrogates. The idea is ECOGRAPHY 1 9 3 (1996) that geophysical components of the landscape such as micro-climate. geological substrate, landforms, soils and vegetation, co-occur in unique associations which can be described and mapped for the purpose of the study (Belbin 1993). Two problems arise from this. The first is that while classifications made by experienced geographers are likely to be sensible and suit the purpose for which they are made, the degree of variance among the internal characteristics of intuitively defined classes cannot be quantified (Spence 1965). Modem numerical classifications implemented on computers have overcome this problem (once the attributes are fixed). The second problem concerns boundaries. The determination of boundaries has ultimately been subjective, relying on an individual’s ability to synthesise complex interacting variables. Today, the availability of satellite imagery has added yet another multi-state variable (or set of variables) and the advent of fast computers makes it possible to classify the landscape numerically (e.g. Laut et al. 1975, Austin and Margules 1986, Mackey et al. 1989, Margules and Belbin 1990). Numerical classifications have not resolved the issue of boundary location. Boundaries are still determined arbitrarily by, for example, the choice of classification units or by weights explicitly or implicitly applied to variables or by the choice of association measure. For all practical purposes the question of where, precisely, a boundary should be located is not a serious issue. It is of academic and technical interest, but because there are so many solutions, and because a regionalisation is a way of providing a spatial framework for other purposes, the issue of boundary location should not assume great importance although it is important, of course, that the basic criteria used are ecologically meaningful. What is important is that the method of arriving at a boundary is explicit and that the range of values of the variables involved is expressed quantitatively. For examples of field surveys conducted on the regional scale that employ explicit procedures for boundary definition, see Jarvinen and Viiidnen (1979, 1980), Austin et al. (1983, 1984), Margules (1989), Margules and Austin (1991). Within regions the same sorts of methodological problems arise, but on a more detailed scale. The purpose of an ecological data base is for planning for and managing the biological diversity in the presence of human exploitation. Iterative cycles of field survey research might begin from an assessment on ‘assemblage level’ of broad distributional patterns across major gradients in human-modified environments, and then focus on cases in which the general pattern seems to be modified by a particular causal process such as, for instance, ‘area effects’ (Haila and Hanski 1984) or variation in habitat quality (Haila et al. 1989). Survey data are indispensable for quantifying assemblage variation as a function of human-caused habitat changes ECOGRAPHY 1 9 3 (1996) (see, for instance, Haila et al. 1980, Virkkala 1987, Niemela et al. 1993a, b). On a still more detailed scale survey data can give indirect evidence for potential mechanisms and thus suggest topics for experimentation. For instance, quantitative data on the spatial structure of populations can be linked to such issues as small-scale variation in habitat quality (NiemelZ et al. 1992) and source-sink population structures (Wiens and Rotenberry 1981). Monitoring of movements of individuals facilitates evaluation of factors that are critical on the population level (Hanski and Haila 1988). But whatever the level of description, the patterns detected are in the data, not necessarily in nature (Austin 1985, 1986). Whether the patterns can be given interesting ecological interpretations depends on conclusions about the processes behind, but these always require independent testing. For all of these reasons it is crucial that survey data are collected systematically and in an explicit manner; that the analytical techniques are appropriate and explicit; and that the results are used, or made available for use, for more than just the immediate purpose of the survey. They should be used to build on the body of knowledge which is our current understanding of ecology, and to evaluate the field observation process - the survey design - so that incremental improvements are made each time. The a m of establishing long-term observational sysi tems which develop in iterative steps is no less challenging a task than developing experimental systems in laboratory research (Griesemer and Wade 1988). or modeling systems in theoretical research (Levins 1966). Knowledge is gained in an iterative process, which Freire (1972) calls praxis - action, reflection, re-action: we all do it all of the time. Five principles of survey design The design of an ecological survey is mediated by 1) the purpose of the survey, 2) current knowledge of ecological patterns and processes, and 3) the techniques to be used in analysing the survey data (Austin 1991). The purpose of a survey determines the appropriate scale and sampling intensity and can help determine which species should be sampled. For example, an ecological survey to inform a regional planning process must gather information about the whole region, not just part of it. Yet a survey is only ever a sample of the geographic space. Thus, the first principle of survey design is sample the expected range of ecological variation. Current knowledge of ecological patterns and processes tells us what that variation might be: what factors have caused the expected patterns to arise and what observable variables will best predict the spatial 327 distribution of those patterns. Thus, the second principle of survey design is to use both local knowledge and ecological theory and stratify the area to be surveyed using known or expected environmental variables that predict ecological patterns. Analytical methods should draw upon well-defined algorithms, provide a quantitative description of variance among internal features of any classification, and take account of the shape of the response of any predicted variable, such as the frequency of occurrence of a species. Numerical pattern analysis (e.g. Anderberg 1973, Belbin 1991, Faith 1991) provides an explicit (not objective) quantitative description of patterns in ecological data. Statistical models (e.g. Austin et al. 1984, 1990, Nicholls 1989, 1991) facilitate the prediction, with known reliability, of observations to unsampled space. it is especially important for this second activity, prediction to unsampled space, that surveys record observations from the entire range of the predictor variables so that prediction is an act of interpolation rather than extrapolation beyond the range of the sample. Underlying both pattern analysis and statistical modelling are the assumptions that samples are drawn from the same population. that the sample is unbiased within any stratification, and that samples are comparable, i.e. of the same size and recorded in the same way. Thus the third principle of survey design is adopt statistical rigour. In practice, this means that samples should be recorded from a known plot size, preferably the same size (though small variations in plot size can be accounted for in an analysis), and, within the lowest level of stratification, from a random location. A fourth principle of survey design arises from considering analytical techniques. which is take logistic problems into consideration. In practice, this means choosing variables for stratification that can be mapped and choosing sample sites that can be reached. Since the purpose of a survey is the adequate description of ecological patterns, and not necessarily the discovery of variables forcing ecological processes (Austin and Heyligers I99 l), reasonable leeway exists in choosing variables for stratification. The fifth and final principle of survey design is be prepared to adjust as experience accumulates. Because survey design is an imaginative, as well as discerning, activity, it is possible, even usual, to get it at least partly wrong. A survey design must be, for instance, flexible enough to incorporate new variables in the stratification after field work has begun. Wade 1988) are specific tools in ecology used to address particular questions in idealized situations (Haila 1992). The issue addressed with field survey is an urgent one: asessing what impoverishment of biological diversity human modification of the environment brings about, and initiating adequate management strategies to stop such impoverishment. Consequently, increasing the efficiency of this tool should have a high priority. We have emphasized the iterative character of ficld survey. Whenever field survey is undertaken in close connection with particular management efforts, the iterative nature of the work is emphasized even more. Theoreticians are usually not interested in the idiosyncracies of single cases, whereas the success or failure of managers in their practical tasks may hinge upon knowing local peculiarities. Every particular situation resembles other situations in some ways and differs from them in others. Managers have to decide, which differences matter. There is no other way to prepare for such decisions than to conduct systematic, repeated surveys which are modified as experience accumulates (Main 1987, Hobbs and Saunden 1993). This relates to what Hacking has characterized as ‘stabilization’ of scientific research. The term refers to a process through which established sciences “tend to produce a self-vindicating structure that keeps them stable” (Hacking 1992: 29-30). ‘Stabilization’ of research occurs historically as an interplay between theoretical commitments underlying a particular research programme, and empirical results. Answers obtained within the research programme give accumulating credibility to the underlying assumptions. Then. naturally, subsequent problems are formulated within the boundaries of the programme as well, and on it goes. Hacking uses the stabilization of Newtonian physics as a paradigmatic historical example of this process. The idea of ‘stabilization’ implies a dose of relativism as regards scientific theories: the shape of theories is influenced by historical contingencies backed by a wealth of social and historical factors. Thus, however firmly established a theoretical edifice such as Newtonian physics is, we cannot know for sure whether it is “true” or not. On the other hand, the idea of ‘stabilization’ shifts the focus to the domain in which the theory works. This brings the applied side of science into the picture: whenever the task is to solve particular problems, what we want to make sure is that the proposed solutions work, and are satisfactory with rcspect to the original goal. Consequently, a degree of stabilization of the chain from theoretical and empirical research to management is a goal to strive for, not to turn research practices into dogmas but to facilitate critical understanding of particular science-application chains. Conclusions: accepting the challenges of This applies also to the task of preserving biodiverecological field survey sity. In this case the chain from basic science to manObservational systems, similar to theoretical models agement runs from field survey, and various theoretical (Levins 1966) and laboratory systems (Griesemer and presuppositions used in designing the survey, through 328 ECOGRAPHY 1 9 3 (1W6). Bock, W. 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Press. Worster. D. 1985. Nature's economy. A history of ecological ideas. - Cambridge Univ. Press. ECOGRAPHY 1 9 3 (1996) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecography Wiley

Survey research in conservation biology

Haila, Y.; Margules, C. R.
Ecography , Volume 19 (3) – Jan 1, 1996
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Copyright © 1996 Wiley Subscription Services, Inc., A Wiley Company
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10.1111/j.1600-0587.1996.tb01261.x
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Abstract

C. R. Margules Haila. Y. and Margules. C. R. 1996. Survey research in conservation biology. Ecography 1 9 323-331. We present systematic arguments for the necessity of field survey in conservation biology. Preservation of biological diversity has become a major challenge in conservation biology, but to comprehend diversity, ecologists have to obtain information on what units the ‘diversity’ of different parts of the world consists of, where these units are, and how they respond to natural and human-induced environmental change. To reach this end, systematic survey procedures need to be developed that incorporate data collecting, data analysis and conclusions about distributional patterns as well as management recommendations into an iterative process that is corrected as experience accumulates. The appropriate survey design depends on the task and needs to be fixed separately in each case; developing long-term observational systems is no less challenging a task than developing experimental systems in laboratory research, or modeling systems in theoretical research. We conclude the paper with five principles of ecological survey. A common denominator of these principles is the need to make explicit decisions at each step so that errors and insufficiences can be corrected later. Y. Haila. Dept of Regional Studies, Enuironmentul Policy, Uniu. of Tamprrr, P.O. Bu.r 607, FIV-33101 Tampere. Finland. - C. Margules, CSIRO. Wildlgk und Ecology. P.O. Box 84, Lyneham, ACT 2602. Australia. A major challenge for conservation biology is to formulate practicable strategies for preserving the biological diversity of the earth, transformed and impoverished by human activities at an increasing rate. Unfortunately, it is not clear what this challenge exactly means. This is because “biological diversity” is a complex, descriptive notion. That ecological phenomena are “diverse” means that different situations usually look different. Furthermore, human transformations of nature merge with natural processes: there are no absolute standards to distinguish between human-induced and natural change (Haila and Levins 1992). Nevertheless, conservationists should be able to give, in specific situations, both theoretically sound and practically relevant answers to the question, Which variations in diversity matter, and from which perspective? In this essay we promote systematic field survey procedures as one adequate way to respond to this challenge. We adopt a broad view of survey as any systematic collecting of observations on patterns of variation in the presence or abundance of species, or even behaviour of individuals. This view also includes monitoring as repeated survey. Survey research requires the development of theory, methods and analytical techniques accompanying the systematic collection of field records and associated analyses for the purpose of mapping the abundance and range of species or some surrogate such as communities or species assemblages. Biodiversity is customarily divided into three main domains: the diversity of genes in populations, the diversity of populations in ecosystems, and the diversity of ecosystems in landscapes and biological regions (e.g. Wilson 1988, Solbrig 1991). Different biological processes maintain and reproduce biodiversity in the different domains, and several alternative criteria can be used for assessing diversity depending on which one of these Accepted 19 December 1995 Copyright Q ECOGRAPHY 1996 ISSN 0906-7590 Printed in Ireland - all rights reserved ECOGRAPHY 193 (19%) domains is primarily emphasized. The notion of ‘biodiversity’ is thus theory-laden: it builds upon presuppositions about the organization of nature into levels and about processes that operate on these levels (Haila and Kouki 1994). Consequently, every attempt to ‘operationalize‘ biodiversity preservation as research programs or management recommendations requires both conceptual and analytical specification as to what particular aspect of biodiversity is in focus. The emphasis in conservation biology has mainly been on populations of single species (e.g. Caughley 1994). The need of adequate inventories has been emphasized recently (see Margules and Austin 1991, 1994, Ricklefs and Schluter 1993, Hawksworth 1994), but the data available for describing global biodiversity patterns let alone for drawing conclusions on processes that maintain those patterns are grossly inadequate; as Schluter and Ricklefs (19935) put it: “Up to this point, studies of diversity have not produced samples of sufficient data to resolve this hierarchy of structure in ecological systems.” From the perspective of modern theoretical ecology conducting surveys may appear as a mundane activity that does not deserve the attention of a pure scientist. Yet it is this body of knowledge - recorded field observations - that both gives rise to and acts as an informal test of most current ecological theory. Field survey gives data both for basic science and practical management, and the quality of the data requires systematic attention. Ultimately, collecting data. interpreting data. theoretical conclusions and practical management should be integrated together into an ongoing. iterative process in conservation biology. analytics, translation by Warrington 1964). “as we cannot know the reason for a fact before we know the fact, we cannot know what a thing is before knowing that it is”. This principle is obvious in the case of biological diversity. Descriptive naturalistic work laid the basis for modern evolutionary biology in the 19th century as well as for the “modern synthesis” which eventually gave rise to an evolutionary understanding of ecological phenomena. Naturalism was important in the background of, for instance, Chetverikov, Dobzhansky, Mayr, Simpson, Stebbins; see Mayr and Provine (1980) and Bock (1994). This is equally true of the interest in biodiversity. As a matter of fact, systematic observation, giving fuel for induction, is, in an historical perspective, the only possible starting point for the development of scientific theories (Hacking 1983). Second, the role of descriptive research does not stop with recording what there is in the world. It also has a systematic dimension, which is mapping the domain of existence of the phenomena of interest. We need to know where ‘biodiversity’ is as well as what it looks like. It is particularly important to define diagnostic criteria for drawing distinctions in order to answer questions such as. ‘When is the object observed at present different from the previous one?’ Measurement is a solution to this dilemma, but every measurement is, of course, based on a decision as to what particular aspect of the phenomenon is worth measuring. and how this can be done. The problems of characterizing numerically the ‘diversity’ of ecological collections have been reviewed many times (e.g. Williams 1964, Hurlbert 1971, May 1975. Huston 1994). Note that conceptual innovation has been more important in this endeavour than purely technical development; for instance, the separation of local (alpha-). The necessity of descriptive research in ecology between-site (beta-) and regional (gamma-) diversity from each other by Whittaker (1972). and the developIn tandem with the growth of modem, analytically ment of methods for standardizing sample size to allow oriented ecology since the 1960’s. traditions of descrip- comparisons of species richness in different samples tive investigation of nature fell into relative oblivion (Simberloff 1978). T e e were conceptual developments hs (Kingsland 1985), despite some eloquent statements in that gave structure to observational work. their defence (e.g. Simberloff 1982). Natural history has However, biological diversity cannot be evaluated sometimes even been presented as a pejorative opposite just by ‘measurement’, that is, different sites cannot be of “scientific” ecology (Peters 1990; for a criticism, see attributed value only. on the basis of the level or Haila 1996). Such a position is, however, untenable: the amount of diversity. Rather, biological diversity in understanding of ecological phenomena builds neces- different sites is similar or different. Adequate reference sarily upon a strong observational component. Modem areas are needed particularly for the assessment of analytic ideals about research methodology ought to be biodiversity in human-modified areas. This may be integrated, not placed in opposition, with this particular straightforward in regions such as the northern boreal feature of ecology which, furthermore, is shared with forest which still have relatively natural references left, many other branches of science (Mayr 1982, Haila but very complicated in intensively human-modified 1992). In the following we elaborate upon four particu- parts of the world. This further emphasises the need of lar reasons to conduct descriptive ecological research, a systematic approach in defining the purpose and and illustrate them with the example of biodiversity. design of biodiversity survey work. First of all, descriptive work is required to find out, In addition, there are practical issues that demand what there is. As Aristotle noticed (Prior and posterior attention. A thorough inventory, for instance a com324 ECOGRAPHY 1 9 3 (1996) plete enumeration of all species from all possible locations. is not an option. Most species have not been described and named. Even if they were, determining all distribution patterns would be an impossible demand on available resources. It is only possible to record a sample of biological diversity, and that sample from only a sub-set of all possible locations. The issues then are to decide which components of the biota (sub-sets of species, etc.) should be sampled, and to design a strategy for surveying the geographic region of interest for those identified components of the biota. On this level also practical considerations such as ease of identification and consistency of sampling are important (e.g. Greenslade and Greenslade 1984, Cranston 1990). Yet the issue of survey design has received too little attention, with only a few notable exceptions (Gillison and Brewer 1985, Austin and Heyligers 1989, 1991). Third, descriptive research acquires an analytic dimension when it is asked, ‘What is the phenomenon of interest consistent with? For instance, when patterns of diversity variation on the local scale have been described, using Whittaker’s distinctions, it becomes possible to ask questions about processes that might produce such patterns: site-specific, local, regional and global processes differentiate from one another; this is discussed in several chapters in Ricklefs and Schluter (1993). What is more important, it becomes possible to exclude some alternative explanations. For instance, when it is observed that breeding birds colonize a set of forest fragments strictly as a function of background abundances, the hypothesis that some particular ‘area effects’ influence the system can be rejected (Haila et al. 1993). In other words, results of systematic surveys can be used to test underlying presuppositions; this is what Haila (1988) called “analytic description”. If an observed pattern is discordant with an expectation that follows from a particular process-hypothesis, then the hypothesis is unlikely to be relevant. As a conception of scientific activity, this matches and expands the ‘hypothetico-deductive’ model expounded by Popper (1935). Science requires criticism through refuting hypotheses, but also systematic criticism of the conceptual presuppositions upon which particular hypotheses are built (Haila and Levins 1992). The ‘analytic dimension’ of decriptive research relates to the following problem. To tease apart the elements of biodiversity ecologists have to obtain information of two kinds. First, we have to know what species (genotypes, communities, or whatever units are used) there are. where they are and how numerous they are. These are ecological patterns. Second, we need to know why they are where they are, and how they respond to natural and human-induced environmental change. This will identify the ecological processes that produce and reproduce the patterns. This is the familiar patternECOCRAPHY 1Y3 (199696) process distinction, which is a characteristic feature of ecology in a far stronger sense than of laboratory sciences (Watt 1947. Wiens 1984, Austin 1986, 1987, Birks 1986. Haila 1992). Ecological patterns are detected by surveys whereas experiments are needed to understand and predict ecological processes (Austin 1987, 1991). It deserves emphasis, however, that in research on biodiversity, which often entails comparisons across time and space, the task of designing proper experiments may be formidable (Nicholls and Margules 1991, Margules 1992, Underwood and Petraitis 1993). Fourth, descriptive research has a practical dimension by providing answers to the question, ‘What should or could be done to/with the phenomenon of interest’!’ In biodiversity research, the detection of patterns often has practical priority over understanding processes, for two reasons. First, it is no use .developing management strategies to prevent extinctions and protect biological diversity if the sites being managed do not contain the full complement of species to be preserved, or at least as many as possible, in the first place (Margules et al. 1988, 1994, Margules 1989). Second, patterns such as the rarity of a particular set of species or a particular type of environment give, per se, cause for concern. Recently, innovative methods have been developed to assess the relative priorities of areas for biodiversity protection, based on the complement of features (e.g. species, communities, ecosystems, etc.) that different areas possess (Margules et al. 1988, 1994, Vane-Wright et al. 1991, Pressey et al. 1993, Williams et al. 1996). The data sets available for these methods to work on are collections of field records, and most are collections held in museums and herbaria. Unfortunately, such records are to a greater or lesser extent biased samples of real distribution patterns. They have been collected in an opportunistic manner, the taxa noted were the ones of interest to the collector and the places they were recorded from are places where the collector expected to find them, or which were conveniently accessible (Margules and Austin 1994). In the face of numerous imminent and irretrievable planning and policy decisions affecting biodiversity it is necessary to make full use of such data. Nevertheless, the limitations should be acknowledged and new field surveys should be implemented to reduce bias in existing collections. A philosophy of ecological survey The tradition of making systematic descriptions of the living world goes back to the early modem times; the locus classicus in the English speaking world is Gilbert White’s (1720-1793) book ‘The Natural History and Antiquities of Selborne, in the County of Southampton’ (see Worster 1985). However, surveys using systematic sampling methods were started only in the late 19th 325 century (Mclntosh 1985. Haila 1992). The early surveys derived inspiration from variable and often idiosynchratic sources such as the nationalistic maxim, ‘know your native country’, but the results of purely descrip tive work have often proved valuable. For instance, the quantitative bird censuses conducted in Finland by Einari Merikallio, a secondary school biology teacher who counted birds during his summer holidays from the 1920’s through the mid-1950’s. have given a baseline for studying long-term changes in northern European avifauna (Jarvinen and Vlisiinen 1977. 1978). The modern challenge is somewhat different: The task is to collect representative samples from a particular region for a defined purpose. This requires adoption of a systematic approach, derived from the particular task at hand. We highlight this requirement with the work of McKenzie et al. (1989) on a conservation plan in the Nullabor region in southern Australia, a huge and poorly known arid area. The survey phase of the work included the following elements: 1) assessment of geographic distribution patterns of selected species over the whole region by field surveys; 2) derivation of assemblage patterns from the survey results on the basis of coexistence of different species in the survey sites; 3) interpolation of these “assemblage” patterns over the region;’and 4) field validation of the pattern on the basis of an additional data set. The project resulted in a suggestion of a reserve network that would fully represent biodiversity in the region. The planning and realization of the survey built upon three assumptions. First, it was assumed that the distribution patterns of the taxa selected for the survey could be adequately sampled with an environmental stratification of the whole area. Second, the “assemblages” of co-occumng taxa, although put together as a pure sampling collection, were assumed to give an adequate surrogaie for modeling the environmental determinants of diversity variation over the region. Third, the additional field data collected after the modeling phase were deemed adequate for assessing the representativeness of the pattern. This example demonstrates that any systematic survey research is necessarily based upon a variety of assumptions and decisions which are of both methodological and substantive character. It is our thesis that this is always the case in systematic survey research. Medawar (1965) eloquently distinguished between two competing images of science. One emphasizes the creative imagination of the scientist who makes speculative adventures into the unknown. The other views the task of the scientist essentially as one of reporting systematically, with the help of the scientific method, what there is in nature. But Medawar also showed that scientific activity really requires both: “In as much as these two sets of opinions contradict each other flatly in every particular, it seems hardly possible that they should both be true; but anyone who has done or reflected deeply upon scientific research knows that there is in fact a great deal of truth in both of them. For a scientist must indeed be freely imaginative and yet sceptical, creative and yet a critic. Therc is a sense in which he must be free, but another in which his thoughts must be very precisely regimented; there is poetry in science, but also a lot of bookkeeping” (p. 32). What are the lessons for biological surveys? The first is to shake off any notion that what is worth observing can be taken for granted. Free of this notion the surveyor can be imaginative and use her or his knowledge of ecological patterns and understanding of ecological processes to decide where in the landscape, and when in time, field observations should be collected so that they illuminate the purpose of the survey. Second, on the other hand, survey data should be evaluated systematically and critically. One should ask, following data analysis. was the design appropriate to the question being asked and did the design enable relevant ecological patterns to be discerned? The answer is usually ‘not entirely’ or ‘not completely satisfactorily’. Thus, there is a third lesson, which is that the design of subsequent surveys, in the same or a different place. should be conditioned by the results of prior surveys. Survey work is a long-term, iterative process. The scale of survey The issue of scaling. in particular, poses challenges for field survey (Wiens 1989). A theoretical problem is to decide what size the units under consideration are; this essentially fixes the scale of the study. The methodological problem that follows is to define an appropriate ‘observation window’ (Rosen 1977). It is, obviously, impossible to assess long-term patterns on one-year data, but judging how many years of data collecting are needed is far from trivial. The number and distribution of sampling sites needed for assessing spatial patterns presents an analogous problem. On the continental scale, spatial units can be distinguished by clearly discernable gross variations in ecological patterns, i.e. biogeographic regions. There is a long history of biogeographic regionalisation in continental Europe, particularly Russia (see, e.g. Isachenko 1973 for a review: Larson 1986), but in the U.K.and north America land classification and regionalisation did not begin in earnest until the 1930’s (Bourne 1931, and Veatch 1930 respectively). On the regional scale, within biogeographic regions, appropriate spatial units might be landscape types. The physical environment is a “structure” on which the “patterns” of species assemblages are superimposed (Margalef 1979). One might identify spatial units by using geophysically defined landscape types directly or by using species assemblages as surrogates. The idea is ECOGRAPHY 1 9 3 (1996) that geophysical components of the landscape such as micro-climate. geological substrate, landforms, soils and vegetation, co-occur in unique associations which can be described and mapped for the purpose of the study (Belbin 1993). Two problems arise from this. The first is that while classifications made by experienced geographers are likely to be sensible and suit the purpose for which they are made, the degree of variance among the internal characteristics of intuitively defined classes cannot be quantified (Spence 1965). Modem numerical classifications implemented on computers have overcome this problem (once the attributes are fixed). The second problem concerns boundaries. The determination of boundaries has ultimately been subjective, relying on an individual’s ability to synthesise complex interacting variables. Today, the availability of satellite imagery has added yet another multi-state variable (or set of variables) and the advent of fast computers makes it possible to classify the landscape numerically (e.g. Laut et al. 1975, Austin and Margules 1986, Mackey et al. 1989, Margules and Belbin 1990). Numerical classifications have not resolved the issue of boundary location. Boundaries are still determined arbitrarily by, for example, the choice of classification units or by weights explicitly or implicitly applied to variables or by the choice of association measure. For all practical purposes the question of where, precisely, a boundary should be located is not a serious issue. It is of academic and technical interest, but because there are so many solutions, and because a regionalisation is a way of providing a spatial framework for other purposes, the issue of boundary location should not assume great importance although it is important, of course, that the basic criteria used are ecologically meaningful. What is important is that the method of arriving at a boundary is explicit and that the range of values of the variables involved is expressed quantitatively. For examples of field surveys conducted on the regional scale that employ explicit procedures for boundary definition, see Jarvinen and Viiidnen (1979, 1980), Austin et al. (1983, 1984), Margules (1989), Margules and Austin (1991). Within regions the same sorts of methodological problems arise, but on a more detailed scale. The purpose of an ecological data base is for planning for and managing the biological diversity in the presence of human exploitation. Iterative cycles of field survey research might begin from an assessment on ‘assemblage level’ of broad distributional patterns across major gradients in human-modified environments, and then focus on cases in which the general pattern seems to be modified by a particular causal process such as, for instance, ‘area effects’ (Haila and Hanski 1984) or variation in habitat quality (Haila et al. 1989). Survey data are indispensable for quantifying assemblage variation as a function of human-caused habitat changes ECOGRAPHY 1 9 3 (1996) (see, for instance, Haila et al. 1980, Virkkala 1987, Niemela et al. 1993a, b). On a still more detailed scale survey data can give indirect evidence for potential mechanisms and thus suggest topics for experimentation. For instance, quantitative data on the spatial structure of populations can be linked to such issues as small-scale variation in habitat quality (NiemelZ et al. 1992) and source-sink population structures (Wiens and Rotenberry 1981). Monitoring of movements of individuals facilitates evaluation of factors that are critical on the population level (Hanski and Haila 1988). But whatever the level of description, the patterns detected are in the data, not necessarily in nature (Austin 1985, 1986). Whether the patterns can be given interesting ecological interpretations depends on conclusions about the processes behind, but these always require independent testing. For all of these reasons it is crucial that survey data are collected systematically and in an explicit manner; that the analytical techniques are appropriate and explicit; and that the results are used, or made available for use, for more than just the immediate purpose of the survey. They should be used to build on the body of knowledge which is our current understanding of ecology, and to evaluate the field observation process - the survey design - so that incremental improvements are made each time. The a m of establishing long-term observational sysi tems which develop in iterative steps is no less challenging a task than developing experimental systems in laboratory research (Griesemer and Wade 1988). or modeling systems in theoretical research (Levins 1966). Knowledge is gained in an iterative process, which Freire (1972) calls praxis - action, reflection, re-action: we all do it all of the time. Five principles of survey design The design of an ecological survey is mediated by 1) the purpose of the survey, 2) current knowledge of ecological patterns and processes, and 3) the techniques to be used in analysing the survey data (Austin 1991). The purpose of a survey determines the appropriate scale and sampling intensity and can help determine which species should be sampled. For example, an ecological survey to inform a regional planning process must gather information about the whole region, not just part of it. Yet a survey is only ever a sample of the geographic space. Thus, the first principle of survey design is sample the expected range of ecological variation. Current knowledge of ecological patterns and processes tells us what that variation might be: what factors have caused the expected patterns to arise and what observable variables will best predict the spatial 327 distribution of those patterns. Thus, the second principle of survey design is to use both local knowledge and ecological theory and stratify the area to be surveyed using known or expected environmental variables that predict ecological patterns. Analytical methods should draw upon well-defined algorithms, provide a quantitative description of variance among internal features of any classification, and take account of the shape of the response of any predicted variable, such as the frequency of occurrence of a species. Numerical pattern analysis (e.g. Anderberg 1973, Belbin 1991, Faith 1991) provides an explicit (not objective) quantitative description of patterns in ecological data. Statistical models (e.g. Austin et al. 1984, 1990, Nicholls 1989, 1991) facilitate the prediction, with known reliability, of observations to unsampled space. it is especially important for this second activity, prediction to unsampled space, that surveys record observations from the entire range of the predictor variables so that prediction is an act of interpolation rather than extrapolation beyond the range of the sample. Underlying both pattern analysis and statistical modelling are the assumptions that samples are drawn from the same population. that the sample is unbiased within any stratification, and that samples are comparable, i.e. of the same size and recorded in the same way. Thus the third principle of survey design is adopt statistical rigour. In practice, this means that samples should be recorded from a known plot size, preferably the same size (though small variations in plot size can be accounted for in an analysis), and, within the lowest level of stratification, from a random location. A fourth principle of survey design arises from considering analytical techniques. which is take logistic problems into consideration. In practice, this means choosing variables for stratification that can be mapped and choosing sample sites that can be reached. Since the purpose of a survey is the adequate description of ecological patterns, and not necessarily the discovery of variables forcing ecological processes (Austin and Heyligers I99 l), reasonable leeway exists in choosing variables for stratification. The fifth and final principle of survey design is be prepared to adjust as experience accumulates. Because survey design is an imaginative, as well as discerning, activity, it is possible, even usual, to get it at least partly wrong. A survey design must be, for instance, flexible enough to incorporate new variables in the stratification after field work has begun. Wade 1988) are specific tools in ecology used to address particular questions in idealized situations (Haila 1992). The issue addressed with field survey is an urgent one: asessing what impoverishment of biological diversity human modification of the environment brings about, and initiating adequate management strategies to stop such impoverishment. Consequently, increasing the efficiency of this tool should have a high priority. We have emphasized the iterative character of ficld survey. Whenever field survey is undertaken in close connection with particular management efforts, the iterative nature of the work is emphasized even more. Theoreticians are usually not interested in the idiosyncracies of single cases, whereas the success or failure of managers in their practical tasks may hinge upon knowing local peculiarities. Every particular situation resembles other situations in some ways and differs from them in others. Managers have to decide, which differences matter. There is no other way to prepare for such decisions than to conduct systematic, repeated surveys which are modified as experience accumulates (Main 1987, Hobbs and Saunden 1993). This relates to what Hacking has characterized as ‘stabilization’ of scientific research. The term refers to a process through which established sciences “tend to produce a self-vindicating structure that keeps them stable” (Hacking 1992: 29-30). ‘Stabilization’ of research occurs historically as an interplay between theoretical commitments underlying a particular research programme, and empirical results. Answers obtained within the research programme give accumulating credibility to the underlying assumptions. Then. naturally, subsequent problems are formulated within the boundaries of the programme as well, and on it goes. Hacking uses the stabilization of Newtonian physics as a paradigmatic historical example of this process. The idea of ‘stabilization’ implies a dose of relativism as regards scientific theories: the shape of theories is influenced by historical contingencies backed by a wealth of social and historical factors. Thus, however firmly established a theoretical edifice such as Newtonian physics is, we cannot know for sure whether it is “true” or not. On the other hand, the idea of ‘stabilization’ shifts the focus to the domain in which the theory works. This brings the applied side of science into the picture: whenever the task is to solve particular problems, what we want to make sure is that the proposed solutions work, and are satisfactory with rcspect to the original goal. Consequently, a degree of stabilization of the chain from theoretical and empirical research to management is a goal to strive for, not to turn research practices into dogmas but to facilitate critical understanding of particular science-application chains. Conclusions: accepting the challenges of This applies also to the task of preserving biodiverecological field survey sity. In this case the chain from basic science to manObservational systems, similar to theoretical models agement runs from field survey, and various theoretical (Levins 1966) and laboratory systems (Griesemer and presuppositions used in designing the survey, through 328 ECOGRAPHY 1 9 3 (1W6). Bock, W. 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