INTRODUCCIÓN

Ecosystem-based management requires measurement and monitoring (Christensen et al., 1996; Floate et al., 1994) as memory is notoriously unreliable and “convenient myths”, for example “all degradation occurred last century”, gain credence (Pickup & Stafford Smith, 1993, p. 479). Positivistic science is the agreed measurement system for ecosystem ecology (Brussard et al., 1998; Grumbine, 1994; Lélé & Norgaard, 1996; Macnaghten & Urry, 1998; Malone, 2000; Norgaard, 1994; Paehlke, 2004). This is based on the assumption that science creates objective knowledge (Cortner, 2000; Lélé & Norgaard, 1996; Macnaghten & Urry, 1998). Cortner (2000) by contrast sees the idea of science as objective and value free as a myth. Paehlke (2004) considers positivist science is epistemologically unable to accommodate values. However, post- positivistic versions of science as openly value-laden, holistic, interdisciplinary and politically active are advocated by some (Cortner, 2000; Funtowicz & Ravetz, 1995; Kay, Regier, Boyle, & Francis, 1999; O'Riordan, 2000). Traditional ecological knowledge has been explored as an

alternative and holistic way of measuring and monitoring the environment. (See Ch.4, s4.3.3.1 for further description of TEK.)

Daily et al. (2000) consider the actual monitoring of ecosystems is scarce. Measurement is extended to monitoring by the act of repetition and comparison (R. B. Allen, 1993). Park (2000, p. 88) considers that the comparison of change over time is one of the best ways of evaluating ecosystem health or integrity as it can establish the direction an ecosystem is moving whether “depleting, stable, collapsing or recovering”.

Ecosystem ‘health’ as the basis for measuring ecological sustainability is based on two broad categories, the context and the components (Costanza, 1999). The context consists of those aspects of the environment that are not immediately amenable to anthropogenic change on a regional/landscape/catchment scale, e.g., climate, landform, and geology. Change in context is on a long (or slow) time scale. The components are those factors that directly change as a result of human activity, e.g., the soil, water, biota, and air. The rate of change of the components is over a short (or fast) time scale (see Ch.4, s4.3.4 for further detail).

The complexity and interconnectedness of ecosystems makes measurement difficult (Aarts & Nienhuis, 1999; Daily et al., 2000; Peterseil et al., 2004). Positivistic science is seen by some as being epistemologically incapable because of it’s inherently reductionist approach (Costanza, 1999; Kay et al., 1999; Lélé & Norgaard, 1996; Norgaard, 1994), however Risser (1995b) advocates for scientific measurement regardless, with a ‘first approximation’ being preferable to no measurement at all.

Paradoxically, despite the claimed limitations of the ability of positivistic science to adequately research environmental complexity (Holling, 1996) the measurement systems promoted for ecosystem management have been based on some partial measure (Risser, 1995a) such as indicators (Meadows, 1998; Slocombe, 1998a). The general categories of indicators for ecosystem management can be divided into contextual and component indicators. The contextual indicators include climate and geomorphology (Leathwick et al., 2003). The component indicators identified are biological diversity (includes vegetation and fauna) (R. B. Allen, 1993; Christensen et al., 1996; Costanza, 1999; Floate et al., 1994; Gibson & Bosch, 1996; Hunter, Mulcock, & Gibson, 2003; Jensen, Webster, Carter, & Treskonova, 1997; Kelly et al., 1986; Neher, 1992; Risser, 1995b), naturalness (Christensen et al., 1996; Kelly et al., 1986; Peterseil et al., 2004), soil properties (Espie et al., 1998; Floate et al., 1994; Hewitt, 1997; Mulcock & Ensor, 1998; Neher, 1992; Risser, 1995b; Stephens, Harmsworth, & Dymond, 1999; Walker & Meyers, 2004; Williams & Mulcock, 1996), water (Biggs et al., 1990; Floate et al., 1994; Harding & Winterbourn, 1997; Neher, 1992), energy and productivity (Costanza, 1999; E. Odum et al., 1981), and socio-economic factors (Espie et al., 1998; Neher, 1992).

In turn each of these general categories is a complex multi-faceted sub-system and so the categories are in turn divided up into elements that lend themselves to (mostly) quantitative measurement. Callicott et al. (1999) propose biodiversity as foundational and therefore a primary indicator of ecosystem health and thus of ecosystem function and process. Pragmatism and practicality dictates that the measurement of biological diversity can not include all species and their numbers so again some partial measure is called for. Key (Risser, 1995a) or keystone species (Costanza, 1999) reflect one response to this problem, but identifying which species are critical to the ongoing functioning of an ecosystem can be elusive (Aarts & Nienhuis, 1999), there being no certainty that they reflect the wider ecosystem (Knight, 1998). Permanent vegetation transects and quadrats measure the changes in the species richness, or the diversity of plant species in a fixed location over time (R. B. Allen, 1993; Duncan, Webster, & Jensen, 2001; Grove et al., 2002; Jensen et al., 1997; Mark & Dickinson, 2003; Rose, Platt, & Frampton, 1995; Whitehouse, Cuff, Evans, & Jensen, 1988). One rangeland method nominates certain species as ‘increasers’, ‘decreasers’ and ‘invaders’ in semi-natural or native pastoral systems to measure the ‘condition’ of the pasture (Caughley, 1984; Gibson & Bosch, 1996; Risser, 1995b). This method and the species richness measure do not explicitly differentiate between indigenous and introduced species, but can be used to monitor change in their relative proportions. Another system involves differentiating un-grazed and grazed lands using some form of exclosure (Wills & Begg, 1986) as a basis for comparison. A variation of this is the employment of benchmark or biosphere reserves (Frankel, 1978; O'Connor, Espie, & Hughey, 2004) where ‘high-integrity’ ecosystems provide the “baseline for assessing the relative condition or state of other ecosystems” (Brussard et al., 1998, p. 12). Scientific monitoring is key, both of the core reserves and the comparison of the core and buffer (Frankel, 1978).

The interpretation of indicator measurement must be able to differentiate between the effects of natural variation, for example drought, and the effects of land use (Brussard et al., 1998). Szaro, Berc et al. (1998) consider that overall there has been limited investment in baseline monitoring which makes it difficult to ‘identify trends and predict ecosystem responses’ and that benchmarks need to be based on fully functioning reference sites and over a sufficient time period, i.e., long- term (see Ch.4, s4.3.4 for discussion of time scale) to encompass the natural climate-driven variation of ecosystems.

Often only one of the basic building blocks, i.e., soil, water, air or biota, is partially measured. According to Thackway, Davey, Hoare and Cresswell (2005, p. 68) “single theme views of the environment (e.g., soil, vegetation or climate) fail to represent the true ecological complexity of landscapes and the different ways in which they respond to different land management practices”. Brussard et al. (1998, p. 16) stipulate that indicators “must be tested to show they indicate what they are supposed to”. Brussard et al. (1998) also outline a framework of indicator categories to

adequately cover all ecological scales: structural, e.g., habitat complexity; compositional, e.g., the taxonomic elements of the ecosystem because some (for example invertebrates and microbes) are poorly represented, and species diversity, e.g., species richness as an indicator of ecological disruption; and process to measure ecosystem function, e.g., primary productivity. Knight (1998) considers the assumptions underpinning the employment of indicators as reflective of wider ecosystem processes are flawed on both conceptual and empirical grounds.

Thackway et al. (2005, p. 68) add to the above by advising that “ecosystem-based management requires the synthesis of information on geomorphology, soils, water values, vegetation and biota”, i.e., it should be interdisciplinary (Malone, 2000; Szaro, Berc et al., 1998), and holistic (Cortner, Wallace, Burke, & Moote, 1998) with integration over all scales, both spatial and temporal. Thus interpretation and modelling should follow measurement and monitoring as a way of reconstituting the complexity of ecosystems and as the basis for the implementation of the findings (Costanza, 1999; Szaro, Berc et al., 1998).

Landscape ecology provides a conceptual framework for addressing the interactions of agricultural and non-agricultural ecosystems (Lowrance, 1992; Norton & Miller, 2000) by focussing on landscape patterns (Knight, 1998; Sexton et al., 1998). ‘Landscape homogenisation’ can be a key indicator for biodiversity loss (Swift et al., 2004). Landscape simplification, the reduction in number of fields, larger land parcels, and their increasingly rectangular shapes, has been employed as evidence of increasing intensification and the concomitant loss of biological diversity as a result of the removal of ‘small biotypes’ (Peterseil et al., 2004; Pietro, 2001). Meurk and Swaffield (2000) advocate for a landscape scale restoration by reducing homogenisation of farmed landscapes and creating patchiness in the landscape by using transport corridors and field margins for ecological restoration.

The goal of measurement and monitoring is not just understanding ecosystems as they exist: environmental science needs to be predictive (Costanza, 1999; Janzen, 2004; Risser, 1995b; Szaro, Berc et al., 1998) and predictive in a world of anthropogenic change where reliance on recorded measurement may no longer be adequate (Wratt, 2003). Holling (1978) advises that the complexity of the environment, the uncertainties inherent in the use of partial measures and subsequent modelling to reconstitute the whole, means the knowledge derived from science needed to be treated as uncertain and inexact and the ‘precautionary principle’ applied. The Ecological Society of America (1996) make the point that environmental measurement is hypothesis testing, not certain knowledge, and advocate for ‘adaptive management’ in the sense of Holling (1978) which incorporates systematic hypothesis testing, awareness of the risks involved because of incomplete knowledge, and management to ensure ecosystems have sufficient resilience as insurance if choices prove wrong (Carpenter et al., 2006).

4.3.3.1 Traditional ecological knowledge

Traditional ecological knowledge (TEK) is considered by some to complement western science for the purposes of ecosystem management (Berkes, 1999; Berkes, Colding, & Folke, 2000; Moller, Berkes, Lyver, & Kislalioglu, 2004; Nadasdy, 2006). By definition TEK has developed and been transmitted over many generations of participatory and communal adaptive resource management, is combined with belief systems (Berkes, 1999) and is supported by narratives (Berkes, 1999; Berkes et al., 2000; Berkes & Turner, 2006). TEK is seen as providing diachronic knowledge (Berkes et al., 2000; Moller et al., 2004) as opposed to the synchronous knowledge of positivistic science (Berkes, 1999), albeit relatively localised in spatial extent (Moller et al., 2004). Moller et al. (2004) citing Mackinson (2001) draw a close analogy between the intuitive way of knowing involved with TEK and fuzzy logic, both of which are better matched to the construction of ecosystems as non-linear. It is also noted that TEK pays attention to the unusual, whereas the scientific methodological requirement of replication creates normative knowledge (Moller et al., 2004).

Moller et al. (2004) point out that TEK pays attention to those aspects of an ecosystem that are important to the user, for example ‘optimising catches while minimising effort’ (Mackinson, 2001), or palatable plants of use for grazing animals (Fernandez-Gimenez, 2000; Kakinuma, Ozaki, Takasuki, & Chuluun, 2008). The same critique could be made of Western science with its utilitarian and resourcist approach to the environment (Berkes, 1999). By following the best grazing the nomadic Mongolian herders effectively created a landscape mosaic (Fernandez- Gimenez, 2000) similar to that of the naturally occurring one on the African savannah (Savory, 1988) that enhanced ecosystem productivity. In addition, there was a deliberate conservation of closer lower altitude grazing for winter use (Fernandez-Gimenez, 2000; Kakinuma et al., 2008), but political change and the ensuing changes to land tenure, land management and social arrangements had resulted in disruption of local control and the overgrazing of these winter- reserved areas (Fernandez-Gimenez, 2000).

TEK can provide insights into where to start a scientific investigation, but focuses on outcomes. By contrast, western science, while monitoring outcomes, incorporates finding the causes that produce those outcomes (Moller et al., 2004; Szaro, Berc et al., 1998). Kakinuma et al. (2008) and Fernadez-Gimenez (2000) both show that Mongolian herders correctly perceive a decline in rangeland conditions, but they largely attribute this to climate which is partially correct, but they omit the effects of overgrazing.

Berkes (1999), and Berkes and Turner (2006) make the point that TEK is inherently political in that it is part of the beachhead to protect indigenous rights.

‘Local knowledge’ refers to an analogous but more recently created knowledge (Berkes, 1999; Berkes & Turner, 2006), for example that of ‘settler societies’ (Griffith, 2006). Bosch, Allen, Williams and Ensor (1996) argue that incorporating local knowledge and engaging farmers as scientific researchers would expand the knowledge base and increase knowledge sharing as an improved basis for adaptive management.

The collaborative combination of positivistic science and TEK/local knowledge provides a two- fold benefit; that of wider acceptance of the findings and the cross checking effect of two different epistemological approaches.