Discussion

Our results demonstrate that the gradual decrease of (already) low vegetation cover in a dryland system causes a non-linear increase in the production of runoff and sediments, with the change from 45 to 30% being critical for that increase, whereas no further significant increase was produced by a change from 30% to 15% cover. Similarly, rill development and the contribution of microtopography to erosion potential (LS-factor) were much lower in IC45 than in IC30 and IC15 plots, which did not present major differences between them.

These results support our two first hypotheses and are consistent with a number of conceptual frameworks and models that point to a sharp increase in bare-soil connectivity at low vegetation cover values as the main factor driving to the substantial change in runoff and erosion (Ludwig and Tongway 1995, Davenport et al. 1998, Wilcox et al. 2003, Mayor et al. 2013, Saco and Moreno-de las Heras 2013). Furthermore, the fact that plots IC30 and IC15 have a similar hydrological behaviour, with much higher resource loss than IC45 suggests that both IC30 and IC15 resulted in a particular degraded state, significantly different from IC45.

For our experimental system, a small reduction in vegetation cover within the range of 45%-30% of vegetation cover caused a critical change in the hydrological behaviour of the system. This sensitive range or threshold area could vary among dryland systems depending on a number of environmental factors such as soil type, topography, or rainfall regime, that have proven to be critical control factors of dryland hydrology (e.g., Francis and Thornes 1990, Boorman et al. 1995, Devito et al. 2005, Moody and Martin 2009).

A critical question is if the sharp increase in resource loss resulting from a decrease in vegetation cover triggers a feedback loop that further decreases plant cover, eventually leading to full degradation (ecosystem shift to a desert/unproductive state). Within CASCADE, we have incorporated this type of feedback to a model of dryland vegetation dynamics (Mayor et al. 2013), and we found that for a gradual increase of environmental pressure (e.g., aridity, grazing), these feedbacks decrease the amount of pressure required to cause a critical shift to a degraded state. However, the analysis of the runoff dynamics over the study period reported here showed no significant increasing trend for any of the experimental plots, which suggest that no degradation loop has been triggered so far. Instead, our results suggest that the disturbance and stress experienced by the experimental plots changed their state to a higher level of degradation from which they do not seem to be recovering, neither further degrading. Similarly, the rills initially developed on the plots did not show a consistent increasing trend in their incision level, but periods of incision and deposition that kept their profiles without major net changes.

A previous long-term study that assessed erosion losses after disturbances of varying intensities found irreversible loss of soil productivity below vegetation cover values around 20%, with successful recovery over time for cover values above this value (Gao et al., 2011). In agreement with this study and with the conceptual framework of sudden shifts in ecosystems (e.g., Scheffer et al 2001, Rietkerk et al. 2004), we hypothesized that once the pressure is ceased, the extant vegetation could either stabilize at its degraded state or recover pre-disturbance cover values and its healthy state, depending on the plant cover value that resulted from the disturbance. However, as discussed above, we found no evidence that pointed to the recovery of pre-disturbance vegetation cover for any of the plots. Only at the end of the monitoring period, 3.5 years after treatment application, we found a slight increasing trend in vegetation cover for all the three plots. However, the final values measured (∼ 30% cover for IC30 and IC15 plots and ∼ 40% for IC45) are still very far from pre-disturbance cover values in the area (61% ± 7%). The variation in the number of species matched the variation in cover, which suggests that decreasing cover resulted in the local extinction or reduction of the least suitable species for the new conditions created.

Sediment concentration in runoff showed a slight and nonsignificant decrease with time, which could be related to the final slight recovery in vegetation cover, but it could also reflect some exhaustion in the amount of easily detachable soil material with time, a rather common process in closed plots (Boix-Fayos et al. 2007).

It could be argued that the vegetation-removal gradient applied was a relatively mild pressure gradient, as it was applied only once and the soil was not excessively altered, as per triggering the hypothesized degradation loop. It must be stressed though that the combined effect of the experimental treatment and the severe drought that occurred in the area in 2013-2014 reduced vegetation cover to very low values, particularly in IC15 and IC30 plots (∼20%), low enough to represent the impact of high environmental pressure over the system. However, despite the clear and nonlinear increase in resource losses (runoff, soil) that resulted from the decrease in cover, the total amount of water lost through runoff only represented between 0.4 and 1.6% of total water input from rainfall, which probably is not enough relevant for triggering a degradation loop in the timeframe of our experiment.

It is important to note that while the vegetation-removal treatment implied a clear stress gradient between plots, the natural drought implied a homogeneous stress for all three plots, yet the impact could have varied as a function of plant biomass/cover (see below). Therefore, the effect of the drought might have masked to some extent the differences in stress due to the vegetation-removal levels.

Initially, the new degradation level of the experimental plots reflected the degree of vegetation removal. Thus, initial values (i.e., monitoring values during the first year after treatment application) of vegetation cover and bare-soil LFA infiltration and nutrient cycling indices linearly decreased from IC45 to IC30 and IC15. However, the natural drought occurred in the area during the second year of the monitoring period appeared to have larger adverse impacts in plots with higher vegetation cover, which seems to have driven both vegetation cover and bare-soil functional status to certain convergent trend between plots. The LFA stability index did not show as clear dynamics and between-plots differences as the other two indices, which can be explained by a lower sensitivity of this index in Mediterranean conditions (Mayor and Bautista 2012).

It is worth mentioning that the differences found in the functional conditions of the bare-soil interpatches as a function of the distance to vegetation patches (i.e., isolated bare-soil areas versus bare-soil areas next to vegetation patches) support the assumptions of dryland vegetation models that incorporate local facilitation as one of the critical processes that drive dryland vegetation dynamics and response to environmental change (e.g., Kéfi et al. 2007a), yet the functional differences found in our work do not seem to be as contrasting as assumed by these models. Our results also highlight the interacting role of drought and vegetation cover as control factors of soil functioning.

At the patch scale, larger bare-soil connectivity also implies larger inter-patch areas, which is beneficial for the performance of the downslope patch. It is known that the transfer of resources from bare-soil interpatches to downslope vegetation patches contribute to plant productivity (Aguiar and Sala 1999, Yu et al. 2008, Turnbull et al. 2012, Urgeghe and Bautista 2015, see also »Potential for shifts). Enhanced patch growth would then contribute to reduce the size of the bare-soil areas, which in turn would reduce the amount of resources transferred to downslope patches. In agreement with this rationale, it has been proposed that the relative amount of source and sink areas in functional landscapes remains within a certain range of variation around a hypothesized optimum source:sink ratio that maximizes both the availability of resources and the growth of vegetation patches (Puigdefábregas et al. 1999, Urgeghe at al. 2010).

We hypothesized that increased inputs of resources from upslope bare-soil inter-patches enhance the performance and growth of the individual downslope patches, which in turn reduce overall bare-soil connectivity, contributing to prevent or delay a shift towards a bare-soil state. Our study proved that runon inputs to individual patches increased with increased size of the upslope interpatch area, which resulted in larger runon input for patches in plots IC15, followed IC30 and IC45. The relationship between soil water gains and upslope interpatch area tended to a plateau, so that larger upslope areas beyond certain value did not further increase water gains. This could be due to a negative relationship between drainage-catchment size and runoff yield per unit area (e.g., Mayor et al., 2011), which is explained by the area-dependent opportunity for runoff to infiltrate before it reaches a downslope vegetation patch (Wilcox et al., 2003). It could also be that large water inputs from large upslope interpatches exceed the maximum capacity of plant patches to capture runon.

Regarding plant performance, we found that before the drought the larger the individual patch in IC30 and IC15 plots the greater the patch growth, suggesting a larger capacity for capturing and benefiting from runon. However, during and after the drought, these relationships shifted to decreasing trends (lower growth with increasing initial size), particularly for patches in IC30 plot, which could be explained by the expected lower growth rate with increasing plant age, as size co-varies with age, but could also be explained by a higher impact of drought and/or enhanced global resource loss on the largest patches or on patches that grew more before the drought.

These results highlight the importance of the redistribution of resources and feedbacks that operate at the patch scale, which contribute to maintain patch productivity under very low plant cover, and could counterbalance net losses from the system, preventing or delaying the shift to a bare-soil state and promoting instead different communities and patterns with different overall resource availability and redistribution (Puigdefábregas et al. 1999, Rietkerk et al. 2004). However, our results also highlight the complexity of the interactions between endogenous feedbacks and external pressures as control factors of dryland dynamics (D’Odorico et al., 2012). Thus, for example, the influence of patch size in plant performance depended on the level of drought stress.

Conclusions and final remarks

Overall, the results reported here support the conjecture that a reduction in plant cover beyond certain threshold values nonlinearly increases resource (water, soil) losses from the system and may trigger a change to a degraded state that functions under less resource-wise conservative conditions. For our case study, there was an area of particular sensitivity within the transition from 40% to 30 % vegetation cover, in which small changes in the cover percentage resulted in a turning point in the hydrological response of the system.  The pressure exerted over the system resulted from a gradient of vegetation removal combined with the impact of a severe drought naturally occurring in the area. All over the study period, and once the pressure ceased, there were no evidences pointing to any recovery trend towards pre-disturbance levels. However, our results did also not provide evidence for the onset of any degradation loop that could further degrade the systems. The three experimental plots shifted to degraded states with vegetation cover around 25-35% at the end of the study period (half of the pre-disturbance vegetation cover value). The assessment of the stability of these new degraded states falls beyond the timeframe of this work.

The major strengths of the research reported here include

1. the use of a manipulative approach to establish a pressure gradient, which allowed a large degree of control in the level of stress exerted over the systems, and
2. the comprehensive set of processes and responses addressed at the global (plot) and local (patch) scales, which provided a rather complete picture of the system behaviour and dynamics.

However, a clear limiting factor for this work is the relatively short duration of the experiment, as the systems have proven to be very dynamic and our research would benefit from a longer monitoring period that provides insights about longer-term dynamics. Acknowledging this, we extended the monitoring period beyond the original window scheduled, and we will continue the monitoring till the end of CASCADE project duration, incorporating any potential new finding to the final dissemination products of CASCADE. Nevertheless, the study period (∼3.5 years) reported here has provided a solid temporal framework for addressing most of the questions formulated in this part of the CASCADE research, and has yielded critical findings and insights that are relevant for improving modelling and theoretical developments on dryland dynamics. Thus, for example, our results on differential bare-soil functioning dynamics depending on the distance to a plant patch; the positive power-type relationship between upslope interpatch area and resource inputs from runon; or the changes in resource loss with decreasing cover are essential inputs for parametrizing and calibrating dryland dynamics models. From the management point of view, our results highlight the need for being conservative regarding the minimum vegetation cover that should be maintained through proper resource exploitation.
Finally, by incorporating the use of RPA (drone) systems to the monitoring of our experimental plots, we were able to very precisely assess vegetation patterns and surface conditions. This tool will largely enhance our capacity for assessing further dynamics in the system and eventually test the degree of stability of the new conditions generated.

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