Contributing Authors: Sonia Kéfi, Florian Schneider, Alain Danet, Alexandre Génin, Angeles G. Mayor, Susana Bautista, Max Rietkerk, Koen Siteur
Editor: Jane Brandt
Source document: Kéfi, S., Schneider, F. Danet, A., Génin, A. Mayor, A. G., Bautista, S. Rietkerk, M. Siteur, K. 2017. Report on indicators for critical thresholds. CASCADE Project Deliverable 6.2, 26 pp


1. Results

A number of indicators of ecosystem degradation are currently available in the literature (see Table 1 in »Identification of the indicators for those studied in CASCADE). So-called generic early-warning signals are simple metrics (return rate after a perturbation, variance, correlation) based on the phenomenon of critical slowing down, which occurs when a system approaches a bifurcation point, i.e. a point at which the system stability is going to change drastically. These indicators can be quantified on both temporal and spatial data. In the case of spatially-structured ecosystems, such as drylands, these generic indicators have been shown to be very likely to fail [32], and additional indicators, based on the ecosystem spatial structure have been suggested: in particular the shape of the vegetation patches and the shape of the patch size distribution.

In CASCADE, we reviewed these indicators, and proposed a work flow of how to quantify them on real data (see »Identification of the indicators; [46]). The code to apply these indicators on ecological data (e.g. aerial images of the landscapes) has also been made available (see »Early warning signals toolbox).

These indicators were tested in a number of more particular cases, and we identified situations in which they are expected to fail. In particular, by comparing their behavior along different types of transitions, we showed that they are not specific to catastrophic shifts, but also occur along non-catastrophic transitions [34]: they are therefore indicators of ecosystem degradation because their detection points to the fact that the ecosystem is having a harder time recovering from perturbations, but they do not indicate what type of transition the system is approaching. A model study taking the spatial component of grazing into account showed that this mechanism, by affecting vegetation patch growth, affected ecosystem resilience (by increasing the probability of catastrophic shifts at high grazing pressures) and the efficiency of patch-based indicators at announcing upcoming ecosystem degradation [8]. This grazing model analysis warns about the blind use of the patch-based degradation indicator without knowing the characteristics of the stressor and their interactions with the intrinsic mechanisms of the ecosystem. Another model study focusing on rainfall intensity, one of the major changes expected in dryland climate in the coming decades, suggested that explicitly considering rainfall intensity may help in assessing the proximity to regime shifts in patterned semiarid ecosystems, and that monitoring losses of resources through runoff and bare soil infiltration could be used to determine ecosystem resilience [41].

A number of studies performed in CASCADE additionally proposed new indicators or approaches. Using a dryland vegetation model, including erosion feedbacks, Mayor et al. [16] suggested that changes in bare-soil connectivity along a degradation gradient (resulting from changes in both plant cover and spatial patterns) may be more informative than changes in plant cover as early-warning indicators of dryland degradation. This is in agreement with recent empirical evidence [48]. Moreover, we found that basic network characteristics could offer novel indicators for identifying an upcoming desertification in semi-arid ecosystems and that the performance of these network-based indicators could be superior to these of the generic early-warning signals based on variance and autocorrelation [43].

Finally, the last task of the CASCADE modelling work was to evaluate these indicators on real data in an attempt to validate their use and efficiency. To do this, we used a large-scale data set from another European project, BIOCOM, in which we could quantify patch-based indicators on 115 dryland sites located world-wide and compare them to field-based measurements reflecting ecosystem functioning (summarized in a metric called multifunctionality). We found that abrupt changes in multifunctionality along an aridity gradient could be reflected by the patch-size distribution of vegetation. By providing the first link between plant spatial patterns and multifunctionality in global drylands, our study provides strong empirical and mechanistic support to the use of these patterns as indicators of discontinuous changes in ecosystem functioning.

2. Implications for management

The results of this part of CASCADE have a number of practical implications in terms of predicting dryland degradation.

  • Our results provide support for the use of indicators based on the spatial structure of the vegetation cover (patch-size distribution, Flowlength) to assess the ecosystem degradation level.
  • Our results nonetheless warn about the need for well identifying the main stressors at play in the ecosystem considered (e.g. rainfall and grazing) since they can affect the type of indicator to follow and their reliability.
  • Our studies have put forward a number of new indicators (Flowlength and network-based indicators) that need further testing and validation in future studies.

Jointly, all those indicators, when simultaneously evaluated and if they all converge in their trends, can help identifying the critical point at which measures should be adopted to prevent drastic changes in ecological conditions before they happen. These spatial indicators can be evaluated on spatio-temporal ecosystem data that are becoming increasingly available through e.g. aerial images.

More globally, our results suggest that ecosystems with aridity indices between 0.2 and 0.4 are especially sensitive to further disturbances [45]. In areas where aridity is expected to reach such values in the future [49] or where grazing is rising due to a higher demand in livestock products, such increased pressures could force the sites in this sensitive climatic envelope into a low multifunctionality state (i.e. degradation).

Results highlights
A key result of our study is that these abrupt changes in multifunctionality can be reflected by the patch-size distribution of vegetation, which is related to critical changes in the way dryland ecosystems are organized.

3. Outlook

Our results also pave the way for more systematically testing these indicators, in various dryland sites (worldwide) and under various drivers, since our model analyses suggest that the nature of the driver and its characteristics can affect the efficiency and the reliability of the indicators. Steps in that direction have already being initiated in CASCADE (e.g. analyses of spatial images from CASCADE field site by Utrecht University Ph.D. student Myrna de Hoop).

Simultaneously, the statistical tools needed to evaluate these indicators needs to be developed, tested and made available so that they can be widely applied. As already mentioned, tools and information about them have already been made available by CASCADE and these tools will keep being updated.

A key element currently lacking from the validation of the indicators is a quantitative measure of the pressure at play. In the work of Berdugo and colleagues [45], the indicators of ecosystem degradation have been clearly correlated with metrics reflecting ecosystem functioning (so-called multifunctionality), but no measure or information about the pressures at play in the different field sites available were available. Again, a step in that direction will be taken by the upcoming study from Myrna de Hoop since dung counts have been measured in the field in that case and can constitute a proxy for the level of grazing pressure. Moreover, quantifying anthropogenic pressures is an explicit goal of a newly funded European project on desertification, BIODESERT (coordinated by Fernando Maestre).

Note: For full references to papers quoted in this article see

» References

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