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. 2013 May 8;8(5):e62111.
doi: 10.1371/journal.pone.0062111. Print 2013.

Scientific foundations for an IUCN Red List of ecosystems

David A Keith  1 , Jon Paul Rodríguez, Kathryn M Rodríguez-Clark, Emily Nicholson, Kaisu Aapala, Alfonso Alonso, Marianne Asmussen, Steven Bachman, Alberto Basset, Edmund G Barrow, John S Benson, Melanie J Bishop, Ronald Bonifacio, Thomas M Brooks, Mark A Burgman, Patrick Comer, Francisco A Comín, Franz Essl, Don Faber-Langendoen, Peter G Fairweather, Robert J Holdaway, Michael Jennings, Richard T Kingsford, Rebecca E Lester, Ralph Mac Nally, Michael A McCarthy, Justin Moat, María A Oliveira-Miranda, Phil Pisanu, Brigitte Poulin, Tracey J Regan, Uwe Riecken, Mark D Spalding, Sergio Zambrano-Martínez
Affiliations

Scientific foundations for an IUCN Red List of ecosystems

David A Keith et al. PLoS One. .

Abstract

An understanding of risks to biodiversity is needed for planning action to slow current rates of decline and secure ecosystem services for future human use. Although the IUCN Red List criteria provide an effective assessment protocol for species, a standard global assessment of risks to higher levels of biodiversity is currently limited. In 2008, IUCN initiated development of risk assessment criteria to support a global Red List of ecosystems. We present a new conceptual model for ecosystem risk assessment founded on a synthesis of relevant ecological theories. To support the model, we review key elements of ecosystem definition and introduce the concept of ecosystem collapse, an analogue of species extinction. The model identifies four distributional and functional symptoms of ecosystem risk as a basis for assessment criteria: A) rates of decline in ecosystem distribution; B) restricted distributions with continuing declines or threats; C) rates of environmental (abiotic) degradation; and D) rates of disruption to biotic processes. A fifth criterion, E) quantitative estimates of the risk of ecosystem collapse, enables integrated assessment of multiple processes and provides a conceptual anchor for the other criteria. We present the theoretical rationale for the construction and interpretation of each criterion. The assessment protocol and threat categories mirror those of the IUCN Red List of species. A trial of the protocol on terrestrial, subterranean, freshwater and marine ecosystems from around the world shows that its concepts are workable and its outcomes are robust, that required data are available, and that results are consistent with assessments carried out by local experts and authorities. The new protocol provides a consistent, practical and theoretically grounded framework for establishing a systematic Red List of the world's ecosystems. This will complement the Red List of species and strengthen global capacity to report on and monitor the status of biodiversity.

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Conflict of interest statement

Competing Interests: Provita is a non-governmental conservation organization based in Venezuela, focused on the conservation of threatened species and ecosystems (www.provitaonline.org or www.provita.org.ve). Provita has been involved with the IUCN Red List of Ecosystems effort for several years, and has therefore sponsored some of the authors’ activities (which they acknowledge). This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.

Figures

Figure 1
Figure 1. Probability density functions for the population and ecosystem variables that measure proximity to the thresholds that define species extinction (A, B) and ecosystem collapse (C, D).
The probability density functions represent uncertainty in the measurement of the variables. For species, the population threshold that defines extinction is known with certainty (e.g. zero abundance of a species, defined by the vertical line in A and B). In A, the estimated population is definitely greater than the extinction threshold, so there is no doubt that the species is extant. Alternatively, the probability that the abundance is above the threshold (the area under the curve) might be less than one (B), in which case the species could be extinct or extant. The shaded area is the probability that the species remains extant. For ecosystems, the x-axis could represent spatial distribution, number of species, water quality, etc. In contrast to species, uncertainty about the definition of ecosystem collapse leads to a range of possible values for this threshold (dashed box in C and D). The ecosystem variable is above this upper bound in some cases (C), so there is no doubt that the ecosystem persists. Alternatively, probable values for the ecosystem variable might intersect the uncertain threshold (D), in which case the ecosystem may be collapsed or not. In this case, there is some probability that the ecosystem parameter is above the upper bound of the threshold (shaded dark grey), which places a lower bound on the probability that the ecosystem persists (i.e. that it has not collapsed). There is an additional probability (pale grey) that the ecosystem parameter is above the threshold that depends on the amount of uncertainty in the threshold (i.e. width of the box). The sum of these two probabilities places an upper bound on the probability ecosystem persists. With further deterioration (E), the lower bound on the probability of ecosystem persistence is zero (no dark shading) and the upper bound is the pale shaded area.
Figure 2
Figure 2. Mechanisms of ecosystem collapse, and symptoms of collapse risk.
Figure 3
Figure 3. Protocol for assessing the risk of collapse of an ecosystem using proposed Red List criteria v2.0 (see Table 3 )
.
Figure 4
Figure 4. Time scales for assessment of change under criteria A, C and D.
Figure 5
Figure 5. Contrasting pathways of environmental or biotic degradation and their corresponding risk classifications under criteria C and D.
(a) initially widespread and benign degradation, later increasing in severity. (b) severity and extent of degradation increase at similar rates. (c) localised but severe degradation, later becoming more widespread. Ecosystems that just fail to meet the thresholds for Vulnerable status (e.g. extremely severe (>80%) decline in environmental quality over 20–30% of distribution, or severe (>30%) decline over 70–80% of distribution) may be assigned Near Threatened (NT) status.
Figure 6
Figure 6. Estimation of relative severity of environmental degradation (criterion C) or disruption of biotic interactions (criterion D).
Example using stream flowthrough data as percent of mean unregulated flows (aqua line joining filled circles) for the Murray River adapted from , see Appendix S2.8. There is uncertainty in both the rate of decline in flowthrough (two alternative regression lines) and the level of flowthrough at which the water-dependent ecosystem would collapse (shaded area). The threshold of collapse is the level of stream flowthrough that would result in widespread tree death and replacement of forest vegetation (most likely by shrubland). This was estimated to occur when mean flowthrough (as estimated by long-term regression) falls to 0–10% of unregulated flow levels (shown as a bounded estimate c1–c2, dashed lines), as widespread tree dieback began to occur when flowthrough was zero in several year of the past decade (see Appendix S2.8 for process model and justification). Based on a best-fit Gaussian regression model of the flowthrough data (dark blue line), the mean flowthrough fell from 71% in 1960 (dotted line a1) to 50% in 2009 (dotted line b1). A beta regression model (red line) gave an improved fit to the data and indicates a decline in mean flowthrough from 63% in 1960 (a2) to 31% in 2009 (b2). A standardised estimate of the relative severity of hydrological degradation over the past 50 years = 100×(b-a)/(c-a). The minimum plausible estimate = 100×(b1–a1)/(c1–a2) = 100×(71–50)/(71–0) = 30% and the maximum plausible estimate = 100×(b2–a2)/(c2–a1) = 100×(63–31)/(63–10) = 60%. Based on uncertainty in the flowthrough regression models and collapse threshold, a bounded estimate of hydrological degradation in this ecosystem is therefore 30–60% over the past 50 years.
Figure 7
Figure 7. Number of ecosystems assessed for each criterion and number for which each criterion determined overall status.
Figure 8
Figure 8. Sensitivity of risk assessment outcomes (relative to uncertainty bounds of the original assessment) to variation in threshold values for (a) all five criteria in combination; (b) criterion A only; (c) criterion B only; (d) criterion C only; (e) criterion D only; and (f) criterion E only.
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