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High-integrity human intervention in ecosystems: Tracking self-organization modes

['Yuval R. Zelnik', 'Centre For Biodiversity Theory', 'Modelling', 'Theoretical', 'Experimental Ecology Station', 'Cnrs', 'Moulis', 'Department Of Ecology', 'Swedish University Of Agricultural Sciences', 'Uppsala']

Date: 2021-11

Humans play major roles in shaping and transforming the ecology of Earth. Unlike natural drivers of ecosystem change, which are erratic and unpredictable, human intervention in ecosystems generally involves planning and management, but often results in detrimental outcomes. Using model studies and aerial-image analysis, we argue that the design of a successful human intervention form calls for the identification of the self-organization modes that drive ecosystem change, and for studying their dynamics. We demonstrate this approach with two examples: grazing management in drought-prone ecosystems, and rehabilitation of degraded vegetation by water harvesting. We show that grazing can increase the resilience to droughts, rather than imposing an additional stress, if managed in a spatially non-uniform manner, and that fragmental restoration along contour bunds is more resilient than the common practice of continuous restoration in vegetation stripes. We conclude by discussing the need for additional studies of self-organization modes and their dynamics.

Human intervention in ecosystems is motivated by various functional needs, such as provisioning ecosystem services, but often has unexpected detrimental outcomes. A major question in ecology is how to manage human intervention so as to achieve its goal without impairing ecosystem function. The main idea pursued here is the need to identify the inherent response ways of ecosystems to disturbances, and use them as road maps for conducting interventions. This approach is demonstrated mathematically using two contexts, grazing management and vegetation restoration, and compared to remote sensing data for the latter. Among the surprising insights obtained is the beneficial effect of grazing, in terms of resilience to droughts, that can be achieved by managing it non-uniformly in space.

Data Availability: All data needed to evaluate the conclusions in the paper are present in the paper and in the Supplementary Materials. Additional data related to this paper is available at the open-access repository: https://doi.org/10.5281/zenodo.3902397 . Script files for simulations and analysis of results shown in the manuscript are available at the open-access repository: https://doi.org/10.5281/zenodo.5499990 .

We focus in this paper on two contexts of human intervention in drylands: grazing management in drought-prone grasslands, and rehabilitation of degraded landscapes by water-harvesting methods. Through these two examples, we will show that the consideration of the inherent ecosystem dynamics provides essential information needed for maintaining high ecological integrity. We use model analysis to consider both cases, and show that the results remain valid even when strong environmental stochasticity is taken into account. We further analyze aerial images of an afforestation project to demonstrate actual dynamics driven by emerging SO modes. We conclude with a discussion of available empirical support for the proposed approach and the need for further empirical and theoretical studies.

Out of all contexts of ecological self-organization, dryland ecosystems stand out as excellent case studies for exploring high-integrity human intervention. Drylands are home to over a third of the world’s population, and a variety of research questions related to human intervention and the escalating concerns about desertification and biodiversity loss have been raised [ 5 , 25 ]. Drylands also show striking spatial self-organization phenomena, which are well accounted for by dryland-vegetation models [ 26 – 32 ], and are readily accessible via remote sensing methods [ 33 – 35 ]. While dryland landscapes represent infinite-dimensional systems (because of their spatial extent), the actual dynamics may be governed by a small number of SO modes associated with a few instabilities of ecosystem states. Vegetation models indeed uncover two generic instabilities: bare soil losing stability to uniform vegetation, and uniform vegetation losing stability to periodic vegetation patterns [ 36 ]. Dryland-vegetation models therefore provide an excellent tool to study high-integrity human intervention problems that take into account more of the inherent dynamical complexity of actual ecosystems.

The simplest realization of this thesis is the much discussed topic of ecological thresholds [ 13 , 19 – 21 ], where the implicit assumption is the existence of an unstable ecosystem state [ 22 – 24 ] that acts as a barrier, and thus as a threshold, for transitions between two alternative stable states. The simplicity in the notion of ecological thresholds makes it easily applicable, but at the same time it limits its usefulness. This univariate representation of the ecosystem focuses on whether a single process (representing a single SO mode), such as biomass removal, goes past a threshold, and thus limits management options to keeping the ecosystem within some safety bounds. However, the multivariate nature of most ecosystems and their spatial extent calls for generalizing that approach by considering the multidimensional phase space (state space) spanned by all relevant SO modes, and the unstable states it contains. These unstable states, usually neglected because no real-life ecosystem converges to them, strongly affect the flow in phase space; not only do they divide the phase space into distinct basins of attraction, and thereby determine the possible ecosystem trajectories that disturbances can induce, but this division may change as unstable states appear or disappear as a result of environmental changes. Viewing specific forms of human intervention as initial points in that phase space, informed choices of intervention, based on deep understanding of the phase-space structure, may avoid undesired outcomes.

Here, we put forward the thesis that by analyzing specific ecological contexts of interest, exploiting methods of dynamical-systems and pattern-formation theories [ 14 – 18 ], concrete suggestions for human intervention can be made that meet the requirement of high ecological integrity under conditions of environmental variability, that is, keep the system at a high degree of self-organization, despite the intervention.

The significance of studying the dynamics of self-organization lies in the ability to uncover their characteristic spatio-temporal modes. These modes, hereafter ‘self-organization (SO) modes’, represent ecological processes that direct ecosystems toward various stable states. Metaphorically, they are the road signs that point toward the possible destinations of self-organization. Mathematically, they are growing eigenmodes associated with instabilities (bifurcations) of ecosystem states [ 14 ], which dictate the growth directions of perturbations around unstable ecosystem states.

An instrumental concept that emerged in the context of human intervention is that of ‘ecological integrity’, viewed here as a system attribute that reflects the degree to which an ecosystem is self-organized in a functional ecosystem state [ 8 , 9 ]. Implicit in this concept are three premises. i) Ecosystems are nonlinear dynamical systems that approach a stable state (see short glossary in Table 1 and detailed glossary in S1 Appendix ) when left undisturbed [ 10 ]. ii) The approach to a functional stable state, i.e. a state of a steadily functioning ecosystem, is a self-organization process, which involves the emergence of spatially heterogeneous landscapes, networks of energy and resource flows, complex food webs, and high species diversity [ 8 , 11 , 12 ]. iii) Human intervention often interferes with these self-organization processes, typically resulting in reduced functionality and ecological integrity [ 13 ]. The introduction of the integrity concept is highly constructive in focusing attention on the natural tendency of ecosystems to self-organize. However, most efforts have been devoted to the intricate question [ 3 ] of assessing ecological integrity [ 8 , 9 ], overlooking the dynamics of self-organization and the implications of these dynamics to management practices.

The dominant role played by humans in shaping and transforming the ecology of the Earth is well recognized [ 1 ]. Human intervention in ecosystems is driven by various needs, including ecosystem services, land-use changes, and rehabilitation of degraded ecosystems [ 2 ]. Unlike unpredictable natural drivers of ecosystem change, such as droughts, forest fires and pest outbreaks, human drivers typically involve planning and controlled management. Yet, the outcomes of human intervention are often detrimental to the ecosystems involved, often when coupled to natural drivers [ 3 ]; biodiversity loss [ 4 ], desertification [ 5 , 6 ], and degradation of coastal ecosystems [ 7 ], are a few examples of such detrimental human impact. Planning human intervention under conditions of environmental variability, without impairing ecosystem function, is thus a major challenge of current ecological research.

Results

General approach The question we address here is how to intervene in an ecosystem and maintain it near a self-organized functional state, despite environmental fluctuations and the additional stress that the intervention itself incurs. We first propose a general approach for high-integrity human intervention of this kind, and then demonstrate it with two examples, grazing management in drought-prone grasslands, and rehabilitation of degraded landscapes by water-harvesting methods. The general approach consists of the following steps: Identification of self-organized (SO) modes and the variables that quantify them, hereafter ‘SO variables’. Uncovering the structure of the phase space spanned by the SO variables. Exploration of high-integrity human intervention forms as initial phase-space points from which phase trajectories emanate toward functional ecosystem states. Implementing these steps for a given ecological context may turn out to be too hard without a conceptual simplification of that context. In the two examples presented below, the context is dryland ecosystems, and the conceptual simplification involves the consideration of primary producers only, specifically plants, and a single limiting resource—water. This simplification necessarily misses significant aspects of the ecosystem’s complexity such as plant-soil feedbacks, species interactions and biodiversity, as well as aspects of the complex human impact on ecosystems [5, 6, 21, 37]. However, in drylands it is a reasonable simplification due to the dominance of plant-water interactions and the often existence of a single pattern-forming species [18]. Given these conditions, the analysis can rely on existing mathematical models that capture various pattern-forming feedbacks, and account for a wide variety of observed vegetation patterns [14, 36, 38]. More complex situations, e.g. where plant-soil feedbacks play important roles in addition to plant-water interactions, can be studied as well, but are not considered here [39]. The general approach may also be implemented in the absence of mathematical models, when detailed empirical data, including high-resolution remote-sensing images and rainfall patterns, are available. In this case it, is important to make sure that the scales of the empirical data and relevant phenomena match, i.e., that spatial and temporal scales are large enough to capture the spatial and temporal patterns of interest.

Robustness to environmental stochasticity The results described so far were obtained by solving Eqs (1) and (2) with constant precipitation rates, where precipitation downshifts were captured by initial states that were calculated at higher constant precipitation values. To what extent do these results remain valid under more general conditions of environmental stochasticity, such as due to fluctuating rainfall? To answer this question we studied the model equations using a precipitation rate that changes annually, with random values taken from a Gamma distribution [49] We compared three cases: no noise, weak noise and strong noise, realizations of which are shown in Fig 5a. For each case we considered initial conditions involving superpositions of two states as follows: increasing portions of a periodic-pattern component in a superposition with uniform vegetation (“pattern share”) for the grazing management problem (vertical axis in Fig 5b), and increasing portions of a rhombic-pattern component in a superposition with a stripe pattern (“rhombic share”) for the rehabilitation problem (vertical axis in Fig 5c). The basic states in these mixed initial conditions, i.e. uniform vegetation in the grazing management problem and stripe pattern in the rehabilitation problem, were calculated at high precipitation and the outcomes of precipitation downshifts to the prescribed mean precipitation values on the horizontal axes in Fig 5b and 5c were studied under the three aforementioned precipitation cases. The outcomes are of three types: no change in the basic state (blue domains), shift to an alternative functional state (green domains), and collapse to bare soil (grey domains). As the figure indicates, the overall response remains the same, irrespective of the noise level. Higher proportions of the alternative state in the initial conditions mean that the system is less liable to collapse, as implied by the shrinking grey domains. PPT PowerPoint slide

PNG larger image

TIFF original image Download: Fig 5. Responses to precipitation downshifts under stochastic precipitation and different initial conditions of mixed vegetation states. Left, middle and right columns correspond to negligible, weak and strong precipitation fluctuations, respectively. (a) Demonstration of noise level. (b) Asymptotic states (see color legend) for the grazing management system, where initial conditions consist of increasing portions of periodic pattern in uniform vegetation (pattern share). (c) Asymptotic states for the vegetation rehabilitation system, where initial conditions consist of increasing portions of rhombic pattern in stripe pattern (rhombic share). Each pixel in the parameter plane (mean precipitation—share) shows the asymptotic state obtained from averaging over 20 simulations with a unique randomization of temporal noise from a Gamma distribution per simulation, where the initial conditions correspond to mixtures of states calculated at P = 115[mm/yr] (P = 260[mm/yr]) for middle (bottom) row. Note that this vertical axis is logarithmic. https://doi.org/10.1371/journal.pcbi.1009427.g005

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