Integrating Habitat Suitability and the Near-Nature Restoration Priorities into Revegetation Plans Based on Potential Vegetation Distribution

Zheng, Chengshi; Wen, Zhu; Liu, Yangyang; Guo, Qian; Jiang, Yanmin; Ren, Hao; Fan, Yongming; Yang, Yuting

doi:10.3390/f12020218

Cited by 17 publications

(6 citation statements)

References 62 publications

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“…Meanwhile, grazing inhibition increased perennial grasses such as L. chinensis , Stipa krylovii , and C. squarrosa (Figure 3). However, grazing exclusion decreased Amaryllidaceae, suggesting that different vegetation species have different adaptations to habitats (Zheng et al, 2021). As the species richness of a plant community increases, so does its capacity to utilize natural resources effectively (Valdez et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Impact of precipitation and grazing on recovery of plant vegetation in temperate grasslands

Lin,

Zhao,

Zhang

et al. 2023

Land Degrad Dev

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Grasslands support a rich diversity that plays a pivotal role in ecological balance, carbon sequestration, and climate regulation. Grassland ecosystems are facing a loss of biodiversity and a decline in soil function due to precipitation variations caused by climate change. However, variations in grassland species diversity are not clearly known, especially under the combined effects of precipitation and grazing. Based on a 5‐year split‐plot experiment with two grazing regimes (i.e., moderate grazing and grazing prohibition) along with three precipitation controls (ambient precipitation and ±50% precipitation) in Inner Mongolian grasslands, we researched the response of precipitation variability and grazing prohibition to vegetation diversity indices and investigated the response of vegetation community composition and biomass to these factors. Five years results showed that increasing precipitation resulted in a significant increase in the coverage of perennial forbs, annual and biennial plants, and Gramineae. Grazing prohibition and increased precipitation significantly increased biomass and species richness compared to all other treatment combinations. Interestingly, grazing exclusion significantly reduced the percentage of Amaryllidaceae, especially in the presence of altered precipitation. The vegetation community composition of a typical grassland was correlated with soil volumetric water content (VWC), bulk density (SBD) and pH, soil organic carbon (SOC), and total nitrogen (TN). VWC, SBD, and TN were found to be the most significant factors affecting vegetation diversity under grazing. Partial canonical ordination revealed that soil moisture, soil bulk density, and SOC had the most significant correlation with vegetation diversity under both grazing prohibition and increased precipitation treatment conditions. These results suggested that alterations in precipitation can change the species composition and ecosystem function of plant communities, leading to a close relationship between species diversity and soil properties. Our results underline the importance of species diversity for soil characteristics and environmental factors in a typical Eurasian steppe ecosystem.

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Section: Discussionmentioning

confidence: 99%

Impact of precipitation and grazing on recovery of plant vegetation in temperate grasslands

Lin,

Zhao,

Zhang

et al. 2023

Land Degrad Dev

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“…2022, 14, x FOR PEER REVIEW 3 of 20 machine learning classification algorithms have recently been established as state-of-theart methods for suitability prediction in various disciplines, such as agriculture [9], forestry [23], nature and environment conservation [24], including land and marine contamination studies [25,26]. While these methods have been successfully utilized in previous studies [27,28], habitat suitability prediction methods according to environmental criteria have been relatively unexplored, especially for the purpose of extending the habitat of endangered flora species [29].…”

Section: Study Area and Fieldworkmentioning

confidence: 99%

A UAS and Machine Learning Classification Approach to Suitability Prediction of Expanding Natural Habitats for Endangered Flora Species

et al. 2022

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In this study, we propose integrating unmanned aerial systems (UASs) and machine learning classification for suitability prediction of expanding habitats for endangered flora species to prevent further extinction. Remote sensing imaging of the protected steppe-like grassland in Bilje using the DJI P4 Multispectral UAS ensured non-invasive data collection. A total of 129 individual flora units of five endangered flora species, including small pasque flower (Pulsatilla pratensis (L.) Miller ssp. nigricans (Störck) Zämelis), green-winged orchid (Orchis morio (L.)), Hungarian false leopardbane (Doronicum hungaricum Rchb.f.), bloody cranesbill (Geranium sanguineum (L.)) and Hungarian iris (Iris variegate (L.)) were detected and georeferenced. Habitat suitability in the projected area, designated for the expansion of the current area of steppe-like grassland in Bilje, was predicted using the binomial machine learning classification algorithm based on three groups of environmental abiotic criteria: vegetation, soil, and topography. Four machine learning classification methods were evaluated: random forest, XGBoost, neural network, and generalized linear model. The random forest method outperformed the other classification methods for all five flora species and achieved the highest receiver operating characteristic (ROC) values, ranging from 0.809 to 0.999. Soil compaction was the least favorable criterion for the habitat suitability of all five flora species, indicating the need to perform soil tillage operations to potentially enable the expansion of their coverage in the projected area. However, potential habitat suitability was detected for the critically endangered flora species of Hungarian false leopardbane, indicating its habitat-related potential for expanding and preventing further extinction. In addition to the current methods of predicting current coverage and population count of endangered species using UASs, the proposed method could serve as a basis for decision making in nature conservation and land management.

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“…Although other studies have also identified a low regeneration potential for the Paraiba Valley (Pandovezi et al 2018), we suggest that future studies could evaluate our estimated MBC by comparing it with field data. Zheng et al (2021) and Vergarechea et al (2019) use the observed data for calibrating models that are looking for estimating the regeneration potential.…”

Section: Limitations and Suggestions For Future Studiesmentioning

confidence: 99%

“…It relies on favorable biophysical conditions for native seedling establishment and growth, the spontaneous arrival of new species over time, and presence of species with differing and complementary ecological behaviors (Rodrigues et al 2011). For example, shrubs and herbaceous plant species in parts of the Loess Plateau in China present different potentially suitable habitats, but both need to be considered the pioneer plants of revegetation in future revegetation plans (Zheng et al 2021). In general, one challenge for employing passive restoration methods is the difficulty to reliably predict the future species composition (Vickers et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Combining regional to local restoration goals in the Brazilian Atlantic forest

et al. 2021

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To achieve regional and international large-scale restoration goals with minimum costs, several restoration commitments rely on natural regeneration, a passive and inexpensive strategy. However, natural regeneration potential may vary within the landscape, mainly due to its historical context. In this work, we use spatially explicit restoration scenarios to explore how and where, within a given region, multiple restoration commitments could be combined to achieve cost-effectiveness outcomes. Our goal is to facilitate the elaboration of forest restoration plans at the regional level, taking into consideration the costs for active and passive restoration methods. The approach includes (1) a statistical analysis to estimate the natural regeneration potential for a given area based on alternative sets of biophysical, land cover, and/or socioeconomic factors and (2) the use of a land change allocation model to explore the cost-effectiveness of combining multiple restoration commitments in a given area through alternative scenarios. We test our approach in a strategic region in the Brazilian Atlantic Forest Biome, the Paraiba Valley in São Paulo State. Using the available data for 2011, calibrated for 2015, we build alternative scenarios for allocating natural regeneration until 2025. Our models indicate that the natural regeneration potential of the region is actually very low, and the cost-effectiveness outcomes are similar for all scenarios. We believe our approach can be used to support the regional-level decision-making about the implementation of multiple commitments aiming at the same target area. It can also be combined with other approaches for more refined analysis (e.g., optimization models).

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Integrating Habitat Suitability and the Near-Nature Restoration Priorities into Revegetation Plans Based on Potential Vegetation Distribution

Cited by 17 publications

References 62 publications

Impact of precipitation and grazing on recovery of plant vegetation in temperate grasslands

Impact of precipitation and grazing on recovery of plant vegetation in temperate grasslands

A UAS and Machine Learning Classification Approach to Suitability Prediction of Expanding Natural Habitats for Endangered Flora Species

Combining regional to local restoration goals in the Brazilian Atlantic forest

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