Anthropogenic land degradation
Placing land degradation in the context of ecological processes
Principal Investigator: Dr. Steve Prince
Department of Geographical Sciences, University of Maryland, College Park, MD, USA sprince [at] umd.edu
Land degradation is globally pervasive and, in some places, irreversible. Humans have historically modified the environment directly and indirectly to meet their requirements, but the rate and extent of degradation has accelerated dramatically in recent years. The resulting anthropogenic impacts on land have been so profound that a new geologic era has been recognized, the Anthropocene - generally dated from 1950.
The process and state of degradation is well-recognized, and even addressed by a United Nations agency - the UN Convention to Combat Desertification (UNCCD), to which almost 200 nations are signatories.
Many studies have contributed to the current theoretical understanding of land degradation. However, some critical aspects that underlie existing knowledge remain to be addressed. Even in the context of extensive study, surprisingly, there remain serious gaps is in the basic foundation of understanding. Clearly, a foundation should contribute to the extensive existing progress to build a coherent whole, rather than proposing yet another conceptual framework.
What is “degradation” in the ecological context?
There is a distinction between, on the one hand, the human causes, motivations and consequences of land degradation and, on the other, the biophysically imposed constraints. This relationship was first developed by Carl Saur and has long been recognized in geography under the title "possibilism" (Robbins, 2012).The term “biophysical” is used here to distinguish the human from the ecological perspectives, although humans are inextricably associated with the ecological.
It is important to recognize that environmental processes alone, can result in conditions that take the form of anthropogenic degradation (such as natural hillslope erosion), but are not anthropogenic drivers of “degradation” per se, unless the natural process is initiated or exacerbated by humans (such as erosion following removal of vegetation).
Degradation results from a multitude of drivers and can manifest in many forms, including erosion, loss of carbon stocks, and changes in hydrological regimes. It can be driven by changes in land cover, caused by, for example, pollution, pests and diseases spreading as a result of climate change, excessive livestock production, agriculture, forestry, alien species introductions, abandonment of land, mining and urbanization. Thus “degradation” is not a single phenomenon - the term encompasses a wide range of causes and conditions that reduce ecosystem services (Millennium Ecosystem Assessment, 2005).
History of degradation studies
Land degradation predates modern written history. For instance, an early well documented example is from 2,400 BC in Mesopotamia, where irrigated agriculture in the Tigris and Euphrates valleys led to salinization (Thomas & Middleton, 1994). Notwithstanding this long history, modern day attempts to quantify the extent and scale of land degradation have proven difficult, especially at the global scale.
There has been a failure to agree on what ecosystem conditions should be regarded as degraded, hampering any consensus on location, severity and extent. In forested areas, there is extensive mapping of forest loss (i.e., the transformation of forests to other land type), but there are far fewer data on the extent of degradation within untransformed forest.
Early global assessments of degradation had a narrow soil focus (e.g., Oldeman, Hakkeling, & Sombroek (1991) but more recently studies have been based on loss of net primary production, often using satellite data (Jackson & Prince, 2016; Noojipady et al., 2015; Prince, 2016; Prince et al., 2009). Following from the Millennium Ecosystem Assessment (Hassan et al., 2005), the emphasis has been on declines in the flow of ecosystem services. Assessment methods have ranged from estimation by specialists; detailed analysis of satellite observation products; social assessment of abandoned land; and simulation models (Prince, 2016; Wessels et al., 2008, 2012).
Comments such as 80% of the global croplands are degraded, or that 10-20% of rangeland are degraded are common and often cited (Adeel, Safriel, Niemeijer, & White, 2005; Gibbs & Salmon, 2015), however, progress towards a credible measure of the extent of land degradation remains elusive. The GLASOD “World Atlas of Desertification” (Oldeman et al., 1991) has been widely used, but recent reviews (Prince, 2016; Sonneveld & Dent, 2009) have found it to be seriously faulted to the point it should not be used (Sonneveld & Dent, 2009). Although a number of other attempts have been made at quantifying the global extent of degradation (Table 1) (Gibbs & Salmon, 2015), at the global scale, the spatial locations and severity of degradation remain substantially unknown (Prince, 2016). The 3rdedition of the World Atlas on Desertification has taken the position of not attempting to develop a single degradation map, but rather uses a convergence of evidence approach (Cherlet et al., 2015).
The degradation process
As noted above, there has been and continues to be confusion over the meaning of the term “degradation”. Many believe they can recognize it when they see it (in the field or with satellite imagery), yet the confusion in the literature belies this view. The definition of the term has led to interminable reviews (see review by Vogt et al., 2011) and even comprehensive versions often give rise to confusion.
The analogy of a cusp threshold (Figure 1) illustrates the distinctions between some of the different types of degradation. The effects of stress caused by human activities to which organisms are susceptible, and therefore the ecosystem service they provide (e.g., depleted soil nitrogen and crop production), can be envisaged as a “response curve”. This is shown by the blue curve from 1 to 2 to 3 (Figure 1). The ecosystem service responds rapidly, almost linearly (from point 1 to2), until the stress declines (e.g., nitrogen is added in the crop example). As the stress declines from left to right in Figure 1, further increases in the service (crop yield) decrease (from 2 to 3), often reaching a plateau when additional reductions of the specific stress have no further effect (at 3). Fluctuations in the stress cause the ecosystem service to move up and down the curve in its range of resilience (2). On the other hand, there are conditions in which stress drives down the provision of the service, as illustrated by curve 4, until it reaches a threshold (point 5) (Turnbull et al., 2008) at which the ecosystem service drops dramatically. This is an example of a non-linear ecological process. Most importantly the ecosystem service cannot be recovered no matter how much the stress is relieved. In this level of degradation, shown as the lower part of the red curve, the ecosystem reaches its completely degraded condition (point 6): this is the permanently degraded condition described in Vogt et al., (2011).
Figure 1 Two types of response to stress. In curve 1, 2 to 3 (blue) the degree of anthropogenic stress determines the level of ecosystem service over the full range, until point 3 when the stress is so low that it has no further effect. The second curve (4 to 6) reaches a threshold (5) at which the response to stress is non-linear and changes to a new state that cannot return to the upper level, no matter how much the stress is alleviated. Illustration based on Lockwood & Lockwood (1993).
The analogy of response curves is helpful only when one anthropogenic stress is involved, but normally there are many that affect ecosystem services, such as: soil type, pollution, soil compaction, loss of palatable species for livestock, reduced productivity. These stresses can be divided into two classes (Figure 2): the first are those that are caused by the physical environment with no human involvement and the second, those that are brought about by human action alone (anthropogenic stresses). These two classes of stress frequently occur together and interact.
While a service may be resilient to the full range of anthropogenic stress when there is negligible environmental stress, a moderate environmental stress moves the anthropogenic response curve closer to the threshold (Figure 1). A further increase in environmental stress drives the site over the cusp and into the zone of permanent degradation, from which no return is possible without drastic, expensive and lengthy artificial remediation. Typically neither anthropogenic nor environmental stress alone drive the site into the permanently degraded zone, but when they work together catastrophic loss of services can ensue.
Figure 2 Conceptual representation of the states and process of degradation and the potential contributions of anthropogenic (human-caused) and natural environmental stresses. The ecosystem service(s) is represented by the vertical dimension and the ecosystem dynamics by the surface. The higher up the surface in the vertical dimension, the higher the ecosystem service. The top two edges represent stress from the natural environmental (left) and anthropogenic stress (right). Both stresses increase across the surface (from 1 to 2 and from 3 to 4). The fold in the surface (at 5) represents the threshold of a zone of permanent degradation. Sites that move over the threshold of resilience on any trajectory cannot return to the upper zone of resilience. A second surface shown below (7) represents a site that naturally provides lower environmental services, but is not initially degraded: it has all the features of the upper surface including resilience and the possibility of permanent degradation.
These concepts lead to recognition of six types of “degradation” (Table 1) (Prince, 2016). Types i and iii are actually not degraded, but are often mistaken for it. Recognition of this distinction can be difficult, but it is critical when assessing the status and planning for restoration – the initial failure to recognize these two states and their difference from true degradation has caused much confusion, for example understanding of Sahelian “desertification” (Herrmann & Sop, 2016). A lot of “degradation” mapping is actually about measuring differences in potential of the ecosystem to provide services, not degradation of that potential (Vagen et al., 2005). Similarly Type ii may have existed for a long time and might be assumed to not be degraded, but it could belong to Type vi (i.e., permanently degraded). Types v and vi are the only states that deserves the term “degradation” (Adeel et al., 2005; Vogt et al., 2011), since their condition is effectively irreversible, even when the driver of the stress is removed. The degradation below the threshold is generally not static, but also moves according to its resilience as the stress varies (Type v) (Wessels et al., 2007), but never back over the threshold. Completely static degradation (Type vi) does occur, for example in heavily salinized cropland. Type iv is of greatest interest since, if the stress is alleviated, it has the capacity to recover naturally – although recovery may be accelerated by human intervention; the alternative being unremitting further degradation to Type v or vi.
Recovery from Types v and vi is actually possible, but only with significant efforts and expenses, or over exceptionally long-time periods, generally exceeding a human life-span. Moreover, the value of the restored land rarely merits the cost of restoration/recovery. For example, the 20 million ha of the southern Great Plains of the USA that were lost to the “Dust Bowl” in the early 1930s (Baveye et al., 2011; Hurt, 1986; The Nature Conservancy, 2016) were restored at the cost of approximately $17 billion (in 2017 US$) and the creation of an entirely new government agency (now called the Natural Resource Conservation Service) in 2017 employing 12,000 people in 2,900 offices. Nevertheless, land in Type vi remains low and susceptible to renewed degradation (Romm, 2011).
Remediation and restoration techniques are frequently applied to control degradation. However, the recovery of the original, pre-degradation ecosystem is at best extremely slow. In cases where there are data, disturbance remained detectable over long periods. For example, after 80 years sites in the USA shortgrass steppe, still show the degradation caused by grazing and burning (Peters et al., 2008) - there is no evidence of complete recovery. many such cases have been recognised, a common one being soil compression by heavy vehicles (Webb, 2002). Thus degradation can be permanent on century-long scales. In the ecological literature, this state is referred to as a deflected succession, a subclimax, or plagioclimax.
Detection of degradation
Types of data used for mapping large areas
Developing indicators and monitoring them are essential to any understanding of land degradation. In the report “Ecological Indicators for the Nation” the National Research Council (2000) provides criteria for selection of indicators, including methods for integrating complex ecological information into multimetric indicators, that can summarize ecological conditions and processes. Anthropogenic land degradation generally consists of multiple conditions and so most monitoring programs use several indicators (Lorenz & Lal, 2016; National Research Council, 2000). The Sustainable Development Goal Target 15.3 has proposed three indices (CBD, 2016), UNCCD uses 11 (Berry et al., 2009), WOCAT uses 57 (Liniger et al., 2008), and GLADA uses 132 (Nachtergaele & Licona-Manzur, 2008).
Data on land degradation that are appropriate for rigorous analysis and development of policy-relevant conclusions are the same as those that apply to all quantitative data collection. They have little meaning unless accompanied with explicit information on the methods used, any necessary qualifications and the variance of the reported values. For example, much of the information on the carbon cycle (Lorenz & Lal, 2016) has confidence limits. Qualitative data (including as indigenous and local knowledge) can also have error metrics and can be combined with quantitative data and statistical methods for example in joint analyses, known as “mixed methods” (Creswell, 2007). Data are collected at a wide range of spatial and temporal scales - from single points or small areas of a few hectares, all the way to global, and for one point in time to monitoring long-term trends.
Methods differ for different scales. Global measurements are almost entirely made using remote sensing since they can have global coverage, spatial resolutions of a few meters and daily, monthly and annual repeat measurements. In the case of vegetation, the remarkable characteristics of remotely-sensed measurements of vegetation indices (especially normalized difference vegetation index) and their inter-annual trends are compelling.
However, while no trend, or no trend after environmental normalization (Bai et al., 2008; Rishmawi et al., 2016), suggests no degradation, that is true only for Type i. (Table 1); the same lack of trend occurs for Type ii. (degraded in the past) and for Type vi. (stable permanently degraded state) - the worst form of degradation.
Another issue is that, while the normalized difference vegetation index is a surrogate for vegetation production (gross primary production) it is only a proxy, and can be incorrect in some conditions (Prince, 1991). Other information, such as plant diversity, generally cannot be measured directly. Some interspecific differences can be detected in repeat observations throughout the season based on seasonal phenological changes in normalized difference vegetation index. More direct detection of species has been achieved in some cases using many more spectral bands, but the “spectral diversity” often consists of more than one taxonomic species (Gholizadeh et al., 2018).
Degradation generally extends over long time scales - “long-term”, “permanent”, yet there are frequent attempts to account for the long-term at the scale of factors such as annual stocking rates, whereas soil formation has a time scale of many years. Both processes are relevant to degradation, but in quite distinct ways related to their scale of action (Jeltsch, Milton, Dean, & Van Rooyen, 1997; Weber, Moloney, & Jeltsch, 2000; Wiegand, Saitz, & Ward, 2006; Wiegand & Milton, 1996; Wiegand, Milton, & Wissel, 1995). Further, many areas of current degradation, degraded prior to current satellite-based trend data and hence may appear as stable land in these data sets (Gibbs & Salmon, 2015). The same occurs over space, for example deposition of wind-blown products of surface erosion can takes place over hundreds of square kilometers, and hundreds of kilometers from the source, yet cattle hoofs that compact the soil are limited to paddocks measuring hectares. The scale of national politics is another range of space and time scales.
Since there can be no single metric of all types of degradation (see “What is degradation?” above), combinations of a number of different measurements into a single index is often proposed (e.g., National Research Council (2000); Symeonakis & Drake (2004); Zucca & Biancalani (2011)). Examples of such indices include: “Ecological Integrity” (Andreasen et al., 2001); “Ecosystem Health” (Brown & Williams, 2016); “Index of Biotic Integrity” (Karr, 1991); “Living Planet Index” (World Wildlife Fund, 2016); and the many that combine ecological and socio-economic factors (e.g., Environmental Vulnerability Index (Pratt et al., 2004). There is disagreement about the value of indices, some claim that they give a false impression of being founded on well-accepted knowledge of ecosystem processes when, in many cases, they are or contain highly subjective components: just because an index is numeric does not make it ecologically sound. Specific indices have strengths and weaknesses, but all are subject to certain flaws: they are subject to loss of information in the condensation of multi-dimensional variability into a one-dimensional index and so the condition in need of remediation cannot be known from the index alone; they are subject to systematic bias from the conversion of raw data into categorical scores; combination of multiple data types, either implicitly or explicitly, weights the measurements of the properties by different amounts, thus emphasizing some aspects more than others (Cai et al., 2011; Kosmas et al., 2012). Weightings can only be justified if the processes are understood well enough (e.g., McRae, Deinet, & Freeman (2017).
The Sustainable Development Goal (United Nations, 2015) Target 15.3 has adopted an index “proportion of land that is degraded over total land area”, a combination of net primary production, land cover and soil organic carbon stock, above and below ground. It has been shown that these are appropriate metrics for measurement of degradation locally; however, measurement of none of the three is possible above the local scale and the misunderstanding of this in United Nations, (2015) is regrettable.
Although net primary production can be estimated globally (Tucker & Pinzon, 2016), it is not, alone, an indicator of degradation without attention to normalizations of weather and other non-anthropogenic factors (Prince et al., 1998; Rishmawi et al., 2016) and especially additional methods that are needed to separate out different types of degradation (see Table 1. and Figure 2).
Global monitoring of above and below ground carbon stock is impractical. A single, large-area map has been developed based on the development of functions for upscaling point data to a full spatial extent using correlated environmental covariates, for which spatial data are available, such as Global Soil Information System (Brus et al., 2017), however, the simple correlation technique’s variability is too large to detect the relatively small changes involved in monitoring degradation(Lorenz & Lal, 2016).
Data and models
Mechanistic models can simulate degradation and other relevant metrics using mathematical representations. Many such models exist, appropriate to different aspects of degradation (e.g., (Izaurralde et al., 2007; Kirkby et al., 2008; Tamene & Le, 2015). These models are attractive since they are designed to behave according to the same processes that determine the degradation, unlike, for example, mapping some indicator. Model results can be very accurate when the biophysical processes and data are known. However, the more realistic models are, the greater their complexity and their need for data. The demand for data and parameters can be prohibitive and often default values have to be used with consequent reduction of accuracy. Rarely do such models have adequate precision to detect subtle local degradation.
Syndromes are descriptions of archetypical, dynamic, coevolutionary patterns of human-environment interactions (Lambin & Geist, 2008). The concept shares some features of models since a set of a priori definitions based on socio-economic and biophysical factors are selected and then used to classify types of degradation. They are derived from qualitative studies of the physical and human aspects of selected degradation case studies. Syndromes have been used in relation to degradation and its socioeconomic effects (Ibáñez et al., 2008) and in a predictive model (Sietz et al., 2006). (Geist, 2005) developed an inventory of syndromes applied to dryland degradation. While attractive as summaries of the nature of specific degradation processes, the selection of types of syndromes is not based on any objective scheme. The concept has been applied at limited scales (Geist, 2005; Petchel-Held, Block, & Cassel-Gintz, 1999).
Land degradation takes place in both natural vegetation and on land transformed to an altered state and use (such as cropland and plantation forests). Although land transformation can, in its self, be considered as a form of degradation, especially when considering biodiversity, transformed land may also enhance provisioning of specific ecosystem services such as agricultural commodities. As such, the choice of an appropriate baseline against which to assess degradation is important. Evaluation of land degradation and restoration requires answers to the questions, “degraded relative to what?” and “progress in restoration towards what?” A reference or baseline is essential to detect and assess the magnitude and direction of any trend in degradation compared with the current conditions (National Research Council, 2000; Prince, 2016).
For example, the concept of “Zero Net Land Degradation” (Chasek et al., 2014) is clearly dependent on baselines for adaptive management and assessment of success. Multiple types of reference states are in use to furnish a start, baseline or reference condition for comparison with the current conditions (Table 4.2). A salutary warning of the danger of a lack of baseline was given by Alexander von Humboldt in 1848, as reported by Gritzner (1981), that travellers unfamiliar with arid lands are "easily led to adopt the erroneous inference that absence of trees is a characteristic of hot climates".
Ecosystem services are provided to human beings and have no meaning apart from that. They are a measure of human preference and satisfaction, so a particularly pertinent reference condition for measurement of degradation would be one that maximizes the desired mix of ecosystem services – that is a Target condition. This is similar to the “utilitarian” concept of the Millennium Ecosystem Assessment (Hassan et al., 2005). A target condition is based on a deliberate choice and is therefore context dependent. For example, in the case of long-standing cropland agriculture, sustained and healthy crop production, rather than the natural land cover, is the target. This is perhaps the most important reference for policy purposes, since it represents a desired future state, the achievement of which can be measured and monitored. A target, however, is not static – it is an aim and aims can change, nor is it usually possible to treat a single service alone since any gain in one can cause a loss of another, so trade-offs are needed, and the choices involved can also change. Furthermore, in many regions and ecosystems, this potential is also not static because of ongoing regional and global changes such as climate change and atmospheric nitrogen deposition.
The historical baseline is the condition of a site in the past. The change from the historical condition to the present time – the trend. This provides an objective assessment, as opposed to the selection of a Target condition which is an aspiration. A historical trend can indicate undesirable changes in an ecosystem and also point to the processes of degradation that have led to the current state and restoration efforts.
While highly desirable, unfortunately there are a few, detailed, time-series of observations of ecosystem properties that are more than 50 years old. Examples are the Park Grass Experiment started in 1856 (Silvertown et al., 2006) and selected plant communities throughout the Netherlands started in the 1930s (Smits et al., 2002). Most repetitive measurement programs are recent. Examples include the annual North American Breeding Bird Survey (Sauer et al., 2017); the many UK Biological Records Centre (Biological Records Centre, 2017) monitoring schemes; the 43-year Earth-Observing satellite record (Moran et al., 2012); and many “permanent plots” in which earlier surveys are repeated, often more than once (Bakker et al., 1996; Kapfer et al., 2017). Historical baselines have been used extensively for assessment of the status and trends of many species and ecosystems (e.g., the IUCN Red List of Threatened Species; IUCN, 2017). However, few of these records are coordinated, and start dates, repetitions and types of measurements generally differ, which makes comparisons difficult. Care must be taken to avoid a false impression of more or less degradation based on different starting points (Pauly, 1995). Furthermore, sites may have suffered degradation before the historical baseline (e.g. Gritzner, 1981).
Natural baseline In some circumstances, particularly where human influence and degradation are low, such as in isolated areas of boreal forest, remote humid forests and some islands, it may be reasonable to infer the condition before the first human influence from present land cover (Bull et al., 2014). This seems an obvious baseline from which to assess any trends in degradation and recovery, since it was before any human modification (Kotiaho et al., 2016), but practical and theoretical issues weigh against it. No exact date can be given for the first human occupation but it was sometime in the Holocene (≤10,000 BCE, but maybe only 2-300 years ago for some regions). Practically, it is rare to find objective data from so far into the past (Spikins, 2000). The only data of this type are fossil deposits, pollen and also fossil parts of plants, insects and diatoms and evidence of human-induced soil erosion that can provide some indications (Hoffmann et al., 2009). These can sometimes be dated or otherwise assigned to the pre-human period, but they are often too generalized to specify the state of the environment in adequate detail for comparison with existing conditions. Of course, a pre-human baseline has no use when the climate or other physical environmental conditions changed in the time between the baseline and the present time, for example the Little Ice Age just 400 years ago.
The start of the Anthropocene (approximately 1950) (Ludwig & Steffen, 2017; Morselli et al., 2018; Waters et al., 2016) can be a logical starting point for a natural baseline – an “Anthropocene baseline” – since it marks, by definition, the start of the massive acceleration of human influence on the natural environment and its biota. Data availability for the last 100 years is obviously more in number, type and accuracy. While anthropogenic degradation occurred in many places before the beginning of the Anthropocene, it was often negligible compared with the post-1950 period and is therefore a useful starting point to assess anthropogenic degradation.
However, even for an Anthropocene baseline, a significant amount of qualitative judgement is needed. One method is the “space for time” substitution (Johnson & Miyanishi, 2008; Pickett, 1989), which compares similar sites in different locations and treats spatial and temporal variation as equivalent. Although this assumption has been challenged, space-for-time substitution is often used due to necessity or convenience (Pickett, 1989). There is one respect in which the use of current conditions to infer a historical baseline is helpful, since non-anthropogenic, environmental changes, such as weather fluctuations will have affected both the supposed non-degraded sites and the putative degraded sites, thereby eliminating some non-anthropogenic environmental changes before the present time. A more objective method for inferring a former state from the current condition is by mathematical process modelling (McGrath et al., 2015; Spikins, 2000; Wang et al., 2006) but data are often sparse and spatial scales are coarse. There are many potential errors in modelling; for example, the mathematical representation of natural processes may not apply to the entire period between the current state and the original natural state.
Future trends of degradation
Accurate information on future environmental conditions and human effects on the environment would assist remediation and recovery efforts. Climate forecasting into an uncertain future needs to account for future land cover, changes in carbon sequestration and pollution. In order to have consistency in forecasts, “scenarios” have been developed that provide some descriptions of how the future might unfold. Scenarios are defined as “hypothetical sequences of events constructed for the purpose of focusing attention on causal processes and decision points” (Geist, 2005; Kahn & Wiener, 1967).
A range of plausible pathways, scenarios, and targets are used that capture a set of conditions for a range of land use, the efficiency of the use of land resources and products, trade and food self-sufficiency, effects of climate change, biodiversity, land use and so on. These are potential outcomes based on an internally consistent, reproducible, and plausible set of assumptions and theories of key driving forces of change (IPCC, 2000) but they should not be interpreted as forecasts.
Scenarios and their outcomes on climate change (Bjørnæs, 2015) use integrated assessment models that estimate the combined effects of human activities (e.g., land use and fossil fuel emissions) on the carbon-climate system. Integrated assessment models such as the IMAGE model (Integrated Model to Assess the Global Environment) (Stehfest et al., 2014) have been coupled with climate models (Moorcroft, 2003; Moss et al., 2010) to assess future environmental impacts (Meller et al., 2015) and livelihoods (Bos et al., 2015; IPCC, 2000) that simulate the interactions of human activities and climate. (2005-2100), providing a way to explore the implications of anthropogenic activities.
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