Fine-Scale Evaluation of Giant Panda Habitats and Countermeasures against the Future Impacts of Climate Change and Human Disturbance (2015–2050): A Case Study in Ya’an, China

Examining the rationality of Giant Panda National Park's zoning designations and management measures for habitat conservation: Insights from interpretable machine learning methods

Examining the rationality of zoning designations and management measures in the initial establishment of national parks in China is of great significance for supporting decision-making regarding habitat conservation. There exists a research gap in exploring the threshold effects of both environmental and human-related factors on habitat distribution in the context of national parks. However, it may be a challenge because of the limited species distribution data. Our study aims to put forward an analytical framework that integrates species distribution models (SDMs) with interpretable machine learning methods. A case study was performed in the Sichuan region of the Giant Panda National Park (GPNP). We constructed a SDM based on the Random Forest algorithm and made use of accessible remote sensing and big data to predict the distribution of giant panda habitat (GPH) in 2020. Interpretable machine learning methods, namely Partial dependence plots (PDPs) and SHapley Additive exPlanations (SHAP), were utilized to uncover the underlying mechanisms of environmental and anthropogenic variables influencing the GPH distribution. Through GIS overlay analysis, areas where conflicts between human settlements, transportation infrastructure, and GPH exist were identified. Our findings indicated a potential 28.44 % decrease in GPH from 2014 to 2020. Environmental factors such as temperature, topography, and vegetation type, as well as anthropogenic factors including distance to built-up areas and transportation infrastructure, notably distance to national roads, provincial roads and city arterial roads, influenced the GPH distribution with threshold effects significantly. The overlay analysis revealed escalated conflicts between human settlements, transportation infrastructure, and GPH in 2020 compared to 2014. Currently, the Sichuan region of the GPNP implements two zones: a core protection zone and a general control zone, covering 63.71 % of the GPH, while 36.29 % remains outside the management scope. Drawing from the analysis above, this study provided suggestions for the adjustment of zoning designations and management measures in the GPNP.

Potential Current and Future Distribution of the Long-Whiskered Owlet (Xenoglaux loweryi) in Amazonas and San Martin, NW Peru

Simple Summary

The long-whiskered owlet (Xenoglaux loweryi) is threatened by human activities and is currently listed as vulnerable by the IUCN. Here, we geo-referenced long-whiskered owlet records, identified key environmental variables affecting their distribution, and predicted their current and future distribution (2050 and 2070) in the Amazonas and San Martin areas of northwestern Peru. Under current conditions, areas with “high”, “moderate”, and “low” probability for the distribution of X. loweryi cover about 0.16% (140.85 km²), 0.46% (416.88 km²), and 1.16% (1048.79 km²) of the study area, respectively. Moreover, under future conditions, the “high”, “moderate”, and “low” probability areas showed profits and losses in terms of habitat suitability. Importantly, the natural protected areas in Amazonas and San Martin, both in current and in the future conditions, do not cover most of the pivotal habitats for X. loweryi. Furthermore, it was evident that the combination of climate change and anthropogenic activities will lead to further habitat loss for this species. Therefore, to effectively conserve this species over time, it is strongly recommended that areas with “high” (and even “moderate”) probability and the main ecosystems that this species inhabits be designated as priority areas for research and conservation (including in natural protected areas).

Abstract

The IUCN has listed the long-whiskered owlet (Xenoglaux loweryi) as vulnerable due to the presence of few geographic records, its restricted range, and anthropogenic threats. Its natural history and ecology are largely unknown, and its distribution is widely debated; therefore, there is an urgent need for the real-time conservation of X. loweryi. In this study, 66 geo-referenced records of X. loweryi, 18 environmental variables, and the maximum entropy model (MaxEnt) have been used to predict the current and future (2050 and 2070) potential distribution of X. loweryi in the Amazonas and San Martin regions of northwestern Peru. In fact, under current conditions, areas of “high”, “moderate”, and “low” potential habitat suitability cover 0.16% (140.85 km²), 0.46% (416.88 km²), and 1.16% (1048.79 km²) of the study area, respectively. Moreover, under future conditions, the “high”, “moderate”, and “low” probability areas present profits and losses in terms of habitat suitability. Based on the environmental variables, this species mostly inhabits areas with a forest fraction with presence of trees with an emergent tree canopy of ~10–30 metres and depends on Yunga montane forest habitats with high humidity but it is not dependent on bare cover area, crops, or grasslands. Nevertheless, most of the current and future distribution areas are not part of the protected natural areas of Amazonas and San Martin. Additionally, the combination of climate change and anthropogenic activities contribute to further losses of this species habitat. Therefore, from the management point of view, corrective and preventive actions will help to preserve this species over time.

Predicting Habitat Suitability and Conserving Juniperus spp. Habitat Using SVM and Maximum Entropy Machine Learning Techniques

Support vector machine (SVM) and maximum entropy (MaxEnt) machine learning techniques are well suited to model the habitat suitability of species. In this study, SVM and MaxEnt models were developed to predict the habitat suitability of Juniperus spp. in the Southern Zagros Mountains of Iran. In recent decades, drought extension and climate alteration have led to extensive changes in the geographical occurrence of this species and its growth and regeneration are extremely limited in this area. This study evaluated the habitat suitability of Juniperus through spatial modeling and predicts appropriate regions for future cultivation and resource conservation. We modeled the natural habitat of Juniperus for an area of 700 ha in Sepidan Area in the Fars province using (1) data regarding the presence of the species (295 samples) collected through field surveys and GPS, (2) habitat soil information and indices derived from 60 soil samples collected in the study area, and (3) climatic and topographic datasets collected from various sources. In total, 15 conditioning factors were used for this spatial modeling approach. Receiver operator characteristic (ROC) curves were applied to estimate the accuracy of the habitat suitability models produced by the SVM and MaxEnt techniques. Results indicated logical and similar area under the curve (AUC)-ROC values for the SVM (0.735) and MaxEnt (0.728) models. Both the SVM and MaxEnt methods revealed a significant relationship between the Juniperus spp. distribution and conditioning factors. Environmental factors played a vital role in evaluating the presence of Juniperus sp. as Max and Min temperatures and annual mean rainfall were the three most important factors for habitat suitability in the study area. Finally, an area with high and very high suitability for the future cultivation of Juniperus sp. and for landscape conservation was suggested based on the SVM model.

Sustainable harvest, people and pandas: Assessing a decade of managed wild harvest and trade in Schisandra sphenanthera

ETHNOPHARMACOLOGICAL RELEVANCE

Endemic to China, the distribution of Schisandra sphenanthera Rehder & E.H. Wilson includes giant panda (Ailuropoda melanoleuca David, 1869) habitats in forests of the Minshan and Qinling Mountains, both inside and outside conservation areas. The fruit is used in indigenous medicines of the Qiang, Tibetan and Yi ethnic minorities of Sichuan. Also used in traditional Chinese medicine (TCM), indications for use are prescribed in the Chinese Pharmacopoeia. For continued access and medical use, survival of healthy populations depends on forests. Biodiversity conservation programmes implemented in forests within the panda landscapes that also benefit rural and indigenous communities, link future access to wild medicinal plants with survival of the panda and resilient rural economies.

AIMS OF THE STUDY

This study aimed at assessing the project activities, institutional-level outcomes and achievements, 10 years on, of the 5-year (2007-2011) UNDP- and WWF- supported EU-China Biodiversity Programme for sustainable, "panda-friendly" schisandra.

MATERIALS AND METHODS

Our study combined analysis of quantitative data such as purchase records coupled with qualitative data obtained from field work, project documents, site-visit reports, certification documents and published articles about the project.

RESULTS

At start of project, interested companies were identified to support economic viability of the sustainable wild harvesting and a "panda friendly" pro-conservation model that provided an incentive to maintain habitat outside formal protected areas. Criteria of relevant sustainability standards, the Organic Wild-crop Harvesting Practice Standard and FairWild Standard, were applied while a new standard was drafted, the Giant Panda Friendly Products Standard. The initial pilot project involving 1 village determined feasibility leading to formation of the Pingwu Shuijing TCM Cooperative which, by 2016, scaled out to membership of 22 villages. From the cooperative's first commercial sale of S. sphenanthera Rehder & E.H.Wilson fruits of 0.5 MT in 2009, annual quantities steadily increased up to 30 MT sold in 2017. The cooperative achieved organic certification in 2012. In 2016, governmental authorisation for the certification of Chinese operations implementing the FairWild Standard was granted. In 2017, the Giant Panda Friendly Products Standard became an official Chinese standard with the cooperative becoming the first panda-friendly certified operation in 2018.

CONCLUSIONS

A decade after the project first started, there is strong evidence for the pro-conservation micro- and small enterprise model. For example, through the establishment of a TCM cooperative with members in 22 villages engaged in sustainable resource management, harvesting and equitable trade of TCM ingredients with organic and panda-friendly branding. The project benefited from multi-disciplinary collaboration of experts in ethnoecology, TCM, panda biology and habitat, nature conservation, sustainability standards and international trade. Inviting interested companies at the start enabled a transition from a funded-project to annual contracts for sustainably harvested TCM herbal drugs. At end of project (2011), the companies and NGOs remained engaged and motivating for completion of activities started during the project. Major eventual outcomes rooted in the initial project included Chinese government authorisation of the FairWild Standard (2016) and Giant Panda Friendly Products Standard (2017).