Journal Paper Digests 2021 #9
- Rethinking restoration indicators and end-points for post-mining landscapes in light of novel ecosystems
- Getting the message right on nature-based solutions to climate change
- Assessment of the interpretability of data mining for the spatial modelling of water erosion using game theory
Rethinking restoration indicators and end-points for post-mining landscapes in light of novel ecosystems
Degraded post-mining landscapes exhibit unique biotic and abiotic components and processes relative to predisturbance natural ecosystems. Yet the concept of pre-disturbance reference natural ecosystems and their associated soil quality indicators (SQIs) (e.g., pH, soil organic carbon) are prominently used for assessing restoration of post-mining landscapes. Limited reviews exist on the validity, limitations, opportunities and knowledge gaps associated with the application of the concept and SQIs on post-mining landscapes. Hence, evidence was examined to highlight constraints, opportunities and future research directions pertaining to the concept and SQIs. First, as novel, hybrid or designer ecosystems, severely degraded post-mining landscapes lack reference natural ecosystems. The framing of restoration is multi-dimensional, and dependent on spatial and temporal scales. Therefore, short-term data on SQIs often measured at point scale cannot adequately account for the multi-dimensionality and scales. Moreover, evidence linking SQIs to ecosystem functions, goods, values, services, and benefits on degraded post-mining landscapes remains weak. Potential redundancy exists among SQIs, because soil properties exhibit spatial and temporal correlation. The universality of SQIs remains unconfirmed, because data validating the measurement protocols and interpretation of SQIs across various biomes are scarce. A framework is presented proposing: (1) a shift from the concept of reference natural ecosystems to novel and designer ecosystems in restoration ecology, (2) the development of the next generation of hierarchical or ecosystem cascade indicators, and end-points addressing the multi-dimensionality and scale issues, and (3) a decision matrix for integrating novel, hybrid and designer ecosystems. The potential applications of novel tools such as drones, laser-based cameras, genomics, and big data analytics are highlighted. Such novel tools could unravel the complex linkages among biotic and abiotic components, and ecosystem function and services, which are currently difficult to investigate using conventional techniques. Finally, ten tentative hypotheses are presented on the restoration of degraded post-mining landscapes.
Getting the message right on nature-based solutions to climate change
Nature-based solutions (NbS)-solutions to societal challenges that involve working with nature-have recently gained popularity as an integrated approach that can address climate change and biodiversity loss, while supporting sustainable development. Although well-designed NbS can deliver multiple benefits for people and nature, much of the recent limelight has been on tree planting for carbon sequestration. There are serious concerns that this is distracting from the need to rapidly phase out use of fossil fuels and protect existing intact ecosystems. There are also concerns that the expansion of forestry framed as a climate change mitigation solution is coming at the cost of carbon rich and biodiverse native ecosystems and local resource rights. Here, we discuss the promise and pitfalls of the NbS framing and its current political traction, and we present recommendations on how to get the message right. We urge policymakers, practitioners and researchers to consider the synergies and trade-offs associated with NbS and to follow four guiding principles to enable NbS to provide sustainable benefits to society: (1) NbS are not a substitute for the rapid phase out of fossil fuels; (2) NbS involve a wide range of ecosystems on land and in the sea, not just forests; (3) NbS are implemented with the full engagement and consent of Indigenous Peoples and local communities in a way that respects their cultural and ecological rights; and (4) NbS should be explicitly designed to provide measurable benefits for biodiversity. Only by following these guidelines will we design robust and resilient NbS that address the urgent challenges of climate change and biodiversity loss, sustaining nature and people together, now and into the future.
Assessment of the interpretability of data mining for the spatial modelling of water erosion using game theory
This study undertook a comprehensive application of 15 data mining (DM) models, most of which have, thus far, not been commonly used in environmental sciences, to predict land susceptibility to water erosion hazard in the Kahorestan catchment, southern Iran. The DM models were BGLM, BGAM, Cforest, CITree, GAMS, LRSS, NCPQR, PLS, PLSGLM, QR, RLM, SGB, SVM, BCART and BTR. We identified 18 factors usually considered as key controls for water erosion, comprising 10 factors extracted from a digital elevation model (DEM), three indices extracted from Landsat 8 images, a sediment connectivity index (SCI) and three other intrinsic factors. Three indicators consisting of MAE, MBE, RMSE, and a Taylor diagram were applied to assess model performance and accuracy. Game theory was applied to assess the interpretability of the DM models for predicting water erosion hazard. Among the 15 predictive models, BGAM and PLS respectively returned the best and worst performance in predicting water erosion hazard in the study area. The most accurate model, BGAM predicted that 22%, 8.2%, 9.4% and 60.4% of the total area should be classified as low, moderate, high and very high susceptibility to soil erosion by water, respectively. Based on BGAM and game theory, the factors extracted from the DEM (e.g., DEM, TWI, Slope, TST, TRI, and SPI) were considered the most important ones controlling the predicted severity of soil erosion by water. We conclude that overall, game theory is a valuable technique for assessing the interpretability of predictive models because this theory through SHAP (Shapley additive explanations) and PFIM (permutation feature importance measure) addresses the important concerns regarding the interpretability of more complex DM models.