1. Freshwater species face greater threats than terrestrial ones; however, conservation efforts for freshwater species lag far behind those for terrestrial ecosystems. Traditionally, conservation planning is often implemented independently for terrestrial and freshwater realms, ignoring potential incidental conservation benefits and cobenefits across them. 2. We established datasets of terrestrial and freshwater species in the Three Parallel Rivers Region, predicted their distributions using Biomod2, applied Marxan Connect to identify priority conservation areas (PCAs) for each taxonomic group and evaluated their incidental conservation benefits and cobenefits. 3. Terrestrial PCAs were concentrated in mountain valleys (e.g. Gaoligong and Cangshan Mountains), while freshwater PCAs were clustered along four major rivers and lakes. Despite low correlations in irreplaceability (r = 0.34-0.45, p < 0.001) and limited spatial similarity (Jaccard = 0.35-0.43), both PCA types yielded incidental benefits and cobenefits. Each PCA protected >= 80% of species and 52% of area in its own realm, while incidentally covering >= 79% of species and 42% of area in the other. Notably, terrestrial PCAs protected more freshwater species, whereas freshwater PCAs covered larger area for terrestrial species. Importantly, the greatest cross-realm cobenefits were achieved by the TS-FS PCAs, which yielded the highest coverage for both terrestrial (88.35% and 62.02%) and freshwater species (81.71% and 56.77%) and exhibited a strong synergistic relationship (y = 0.24x(1.31), R-2 = 0.85), while featuring overlapping PCAs that protected the highest proportions of species number (80.45%-91.28%) and area (46.25%-63.04%) across taxonomic groups. 4. Synthesis and applications. Single-realm planning can yield cross-realm cobenefits, demonstrating strong potential for integrated conservation strategies. Integrating these cobenefits into cross-realm planning is key to advancing the '30 & times; 30' biodiversity targets.

Effective management of ecological risks in canal watersheds, crucial for sustaining ecosystem services and maintaining ecological integrity, is facing significant challenges due to complex human-environment interactions. Traditional zoning approaches rely primarily on static indicators, potentially overlooking the spatial dynamics of ecosystem services (ESs). To address this, this study integrates ecosystem service flows (ESFs), which are dynamic, spatially explicit representations of ES delivery, into ecological risk zoning within the Pinglu Canal Watershed in China. First, we quantified and mapped the spatial patterns of four key ESFs: water yield (WY), habitat quality (HQ), crop production (CP), and tourism & recreation (TR). Next, we evaluated ecological risks by building causal networks linking risk sources, exposure processes, and ES responses. Using these integrated risk-flow results, we delineated management zones through k-means clustering. The main conclusions are as follows: (1) Six ecological management zones were identified (33.93%, 7.48%, 2.62%, 32.63%, 22.28%, and 1.06% of the watershed), each characterized by distinct ecological functions and dominant risk attributes. Specifically, Cluster 6 requires integrated cross-sectoral management due to overlapping agricultural, biodiversity, and tourism pressures, while Cluster 3 highlights the need for land-use control and ecological corridor restoration. Other zones serve regulatory (Clusters 1 and 4), buffering (Cluster 5), or monitoring (Cluster 2) functions, forming a gradient governance framework. (2) While previous studies often assessed ecological risks based on static ES, this study integrates dynamic ESFs to account for spatial continuity and flow direction. This enables the identification of not only areas where ESs are under pressure but also the manners in which ecological risks spatially disrupt ES flow paths. (3) This integration provides a basis for targeted ecological risk coordinated management, applicable not only to the Pinglu Canal but also to similar watersheds globally.

The modeling of ecological risks to aquatic ecosystems often introduces additional analytical steps by relying on proxy indicators for clustering analyses, which can increase uncertainty and may not accurately capture the actual risks to ecosystems. In response, we have developed a model in which ecological risk responses are directly clustered. This modeling approach was applied to the Pinglu Canal during its operational period. First, we identified eight aquatic ecological risk response bundles (AERRBs) through clustering analysis, revealing distinct spatial patterns of ecological risk responses. Next, a Bayesian Network (BN) was developed to model the causal relationships among the risk sources, receptors, and responses, offering a dynamic adaptive framework for understanding and managing risks. By adjusting the response nodes in the BN based on the characteristics of each risk response bundle, we simulated risk changes under various scenarios, enabling targeted risk management strategies in the canal. The main conclusions are as follows. (1) Bundles 3, 6, and 8 account for 43.54%, 16.57%, and 20.94% of the total area of the Pinglu Canal, respectively, making them the most prominent AERRBs. (2) The Xijin Reservoir zone is dominated by bundles 6 and 8. The cross-ridge section of the Pinglu Canal is dominated by bundle 3 in K30–K37 and bundle 8 in the other sections. The main ecological conservation zone was dominated by bundle 2 in the main waterway, and bundle 3 in the ecological conservation and drinking water source protection zones. The densely populated zone was predominantly influenced by bundles 5 and 6, whereas the concentrated wetland distribution zone was influenced by bundles 6 and 7. (3) The aggregation of aquatic ecological risks represented by AERRBs, combined with the causal inference functionality of BN, facilitated the management of different ecological risk sources and receptors in key canal sections. Moreover, this method, which integrates the spatial distribution pattern of ecological risk responses and causal analysis, is also applicable to the aquatic ecological risk management of canals worldwide.

The identification of climate refugia is a crucial conservation strategy under climate change. However, the debate is open on the appropriate climate indicators for refugia delineation. This study proposes an approach for identifying refugia by incorporating climate heterogeneity, rarity (including endemism), and temporal stability (quantified via climate velocity). The impact of the integration of climate rarity and temporal stability on the spatial pattern of refugia was specifically analyzed. A case study was conducted within the global biodiversity hotspot of Yunnan in southwest China. Using the Zonation model, 3 scenarios were implemented: climate heterogeneity integrating rarity (CHIR), climate heterogeneity integrating stability (CHIS), and climate heterogeneity integrating rarity and stability (CHIRS). CHIS and CHIRS were implemented under the climate scenarios SSP126 and SSP585, respectively. All refugia were identified as the top 30% of areas in Yunnan with respect to irreplaceability. Finally, the effects of climate and land-cover changes were examined by analyzing coverage changes between current and projected climate velocity levels as well as forest land and human land uses within these refugia. Results showed that CHIRS-refugia had 23.6% and 43.9% less coverage of very fast climate velocities than CHIR-refugia under SSP126 and SSP585, respectively. Compared to CHIR-refugia, forest cover within CHIRS-refugia under SSP126 and SSP585 was 2.5% and 6.8% higher in 2060, respectively, while human land-use coverage was 2.4% and 6.0% lower, respectively. These results indicate that CHIRS-refugia can provide an optimal portfolio of potential climate refugia, which can promote long-term biodiversity conservation under future climate change.

Expanding transboundary protected areas is crucial to biodiversity conservation and maintenance of ecosystem services. Quantifying border gradients of species richness is essential to aligning ecological and management boundaries to enable the gradual integration of biodiversity hotspots in conservation strategies that include cross-border areas that buffer the effects of extensive infrastructure. For the China-Myanmar border region (Gaoligong Mountains), we developed a conservation plan based on threatened species, ecosystem services, border gradient characteristics (i.e., spatial changes in ecological variables with increasing distance from a boundary), and ecological connectivity of protected areas. Although 20.2% of existing protected areas were in the north, 27.7% of identified priority conservation areas were outside these protected areas and only 2.93% were protected. Threatened plant and animal richness exhibited a positive spatial correlation (coefficient = 0.12), but their richness hotspots were spatially mismatched. The richness of threatened species was positively correlated with carbon storage and soil retention, but negatively correlated with water retention. In contrast, threatened animal diversity showed the opposite pattern. Threatened species richness decreased as distance from the border increased (strong linear relationship, R2 = 0.95 for plants and 0.59 for animals). Based on our results, we propose an ecological gradient-based conservation strategy that prioritizes areas where the richness of threatened species overlaps in the central and southern regions and that protects biodiversity hotspots and creates corridors. The framework is applicable to other transboundary regions and supports global biodiversity conventions.

Multiple stressors in aquatic ecosystems can interact to amplify risks and drive their spread across space. Conventional single-stressor models overlook these dynamics, impeding the identification of risk spatial spread pathways (RSSPs) and evidence-based management. Decision-makers must identify such synergies by establishing stressor–receptor–response relationships and mapping RSSPs from stressors to receptors to develop targeted management strategies. Focusing on the Pinglu Canal, this study employs a Bayesian network (BN) to establish a stressor–receptor–response network of 11 stressors, 4 receptors and 6 responses. These responses reflect multiple stressors synergies and form the basis for generating the RSSP resistance surface. By applying the minimum cumulative resistance (MCR) model, we delineate RSSPs between main stressors and receptors, providing a spatially explicit method to inform ecosystem-based risk management. The key findings are as follows: (1) Six responses were quantitatively assessed: aquatic eutrophication, increased habitat fragmentation, decreased fish habitat suitability, increased organic pollution, increased heavy metal exposure levels, and mangrove degradation. Among these, increased habitat fragmentation was the most prominent response observed along the Pinglu Canal. (2) Thirty-nine RSSPs were identified, with two high-risk segments (K86–K101 and K115–K122) prioritized due to the co-occurrence of all 6 responses, providing guidance for targeted management. The BN-MCR model bridges multiple stressors synergy analysis and RSSP identification, offering a replicable strategy for canal ecosystems under intensive human-altered aquatic stressors.

Aim: Subjective study area delineation in conservation planning often overlooks species occurrences integrality, truncating the ecological niches. Incorporating occurrences from expanded areas into species distribution prediction within the study area provides an innovative approach to mitigate negative impacts, but the conservation effectiveness of this approach requires further clarification. Location: We selected the Southeast Himalaya Biodiversity Priority Conservation Area and the Himalayas biogeographic region as the target and expanded areas, respectively. Methods: We set three scenarios by extracting species occurrences from the target area, expanded area, and both areas (scenarios 1, 2, and 3, respectively). Using MaxEnt for predicting the potential distributions of species (SPDs) and Zonation for identifying priority conservation areas (PCAs) across the three scenarios, we evaluated the SPD prediction accuracy, conservation effectiveness, and ecological representativeness. Results: Incorporating data from the expanded area in scenarios 2 and 3 improved the prediction accuracy and covered a wider SPD range than scenario 1. High-richness areas and PCAs in scenarios 2 and 3 were identified in the Kangrigebu South Wing Mountains, Salween and Lancangjiang Incisive Mountains, and southern Brahmaputra Great Turn and Upper Salween Incisive Mountains. These PCAs improved the coverage of the SPD areas (77.74%-82.20%) and priority forest and wetland ecosystems (11.86%-12.84%). In contrast, the PCAs in scenario 1 had a relatively larger distribution in the Himalayas Central Mountains, covering a higher proportion of their own SPD areas (83.75%) and priority steppe ecosystem (31.55%). Overall, scenarios 2 and 3 demonstrated greater conservation effectiveness and ecological representativeness, with minimal differences between them, and both outperformed scenario 1. Main Conclusions: Our study proposed an innovative approach that expanded the study area to biogeographic regions and supplemented species occurrences from these expanded areas, thereby improving the prediction accuracy of SPDs and conservation effectiveness, while providing an easily implementable and generalizable framework in data deficient areas.

Global warming is increasing compound drought and heatwave events. This elevates vegetation loss probability. Despite spatial shifts in vegetation loss probability being crucial for predicting spatial redistribution patterns of vegetation vulnerability across terrestrial ecosystems, they remain poorly understood under compound drought and heatwave events. In this study, using a vine copula model, vegetation loss probability was quantified under compound drought and heatwave events. Spatial shift velocities of vegetation loss probability were examined using the concept of velocity change. Spatial shift velocities of vegetation loss probability would undergo substantial increase based on the satellite observations and future simulations. However, vegetation resistance to droughts buffers spatial shift velocities of vegetation loss probability (p < 0.01). These findings provide evidence that vegetation vulnerability patterns will undergo substantial spatial changes under compound drought and heatwave events, leading to a spatial redistribution of vegetation in disturbance-prone areas.

The intensity of anthropogenic disturbances profoundly affects the evolution of landscape patterns and habitat fragmentation, leading to the loss of biodiversity and degradation of ecosystem services. The construction of multiple objective synergistic ecological security patterns (ESPs) has emerged as a critical strategy for mitigating such ecological risks. However, traditional frameworks for constructing ESPs often remain constrained by their singular focus on protecting targeted species or key habitats while neglecting the spatially cumulative impacts of anthropogenic stressors, such as major infrastructure development, thereby affecting the effectiveness and adaptiveness of ESPs. In this study, an ESP framework was developed for analyzing the Pinglu Canal Economic Belt, Guangxi Autonomous Region. Specifically, the MaxEnt model was used to predict species habitat suitability for endangered mammals, amphibians, reptiles, and birds. These predictions were integrated with ecosystem service assessments to identify priority ecological source areas. Then, circuit theory models were used to generate resistance surfaces for extracting corridors and identify barrier areas, ecological pinch points, and high-risk habitat patches to systematically construct multiple objective ESPs. The result showed: (1) certain differences in the spatial consistency of the ecological source areas of mammals, amphibians, reptiles and birds, with 44.32 % overlap in spatial distribution; (2) Landscape elements demonstrated distinct distribution patterns. The ecological source areas and high-risk patches were concentrated in the northern and southern parts of the study area, whereas the ecological corridors and ecological pinch points were distributed mainly in the central region; (3) Within the canal basin, ecological source areas and ecological barrier areas, moderate-risk patches constituted substantial proportions of the basin, at 6.38 %, 6.00 %, and 7.40 %, respectively; (4) Aligning with the green development imperatives of the Pinglu Canal Economic Belt, the optimized ESP framework was conceptualized as a "two-shield, one-belt" configuration. Our study provides an effective ESP framework with global relevance for balancing infrastructure construction and ecological integrity in fragmented landscapes.

1. Effective conservation planning and conflict mitigation can hinge on accurately modelling wildlife movement paths (WMPs), yet progress is hindered by both a shortage of reliable methods and limited data. The critical challenge, therefore, is to devise limited-data models that faithfully reproduce elusive species’ movements and deliver actionable insights for human–wildlife conflict management. 2. We introduce the Enhanced Resource Selection Function–Vector-network Iterative Pathfinding Algorithm (ERSF-VIPA), a novel framework for simulating WMPs with limited data. Drawing on historical occurrence records of Asian elephants (Elephas maximus), we assume individuals make rational, goal-driven decisions based on local environmental knowledge. The ERSF employs a random forest on a hexagonal grid to estimate nonlinear resource-selection probabilities, while VIPA conducts an iterative, node-to-node search across that hexagonal vector network—scoring each candidate by combining selection probability with cubic distance coefficients to ensure ecological validity and energetic efficiency. 3. The model demonstrates high accuracy, with 90.3

Climate diversity is essential for safeguarding biological diversity against climate change. Two planning approaches based on continuous heterogeneity or discrete classification have previously been implemented to identify climatic refugia. However, little is known about the performance of the integration of the 2 measurements for identifying climatic refugia. Using the case of Yunnan in southwest China, we examined the relationship between 2 measurements of climatic heterogeneity: the continuous climatic heterogeneity index (CCHI) and the variety of climatic units (VCU). We then identified climatic-heterogeneity refugia focusing only on CCHIs and the comprehensive climate-diversity refugia integrating CCHIs with the rarity and endemism of climatic units. Last, we assessed the coverages of these 2 sets of refugia for current high conservation-value areas, indicated by 5 existing biodiversity priority conservation area (PCA) schemes. The composite-CCHI and VCU demonstrated substantial different distributions, and the climatic heterogeneity level assessed by VCU was higher than that of composite-CCHI. The composite-CCHI levels were significantly positively correlated with the coverage percentages of the 5 PCAs. The Jaccard similarity index between climatic-heterogeneity refugia and climate-diversity refugia at a 30% conservation target was 0.26. The climate-diversity refugia coverages for the 5 biodiversity PCAs were consistently higher than those of climatic-heterogeneity refugia. Existing nature reserves covered 18.6% of the 5% climate-diversity refugia. Our analyses suggest that CCHI is more effective than VCU in revealing climatic heterogeneity and indicating current high conservation-value areas. Integrating continuous climatic heterogeneity with the rarity and endemism of climatic units serves as an optimal approach for identifying climate-diversity refugia.

Aims:Priority assessment for natural vegetation conservation is an important foundation for formulating conservation plans and allocating conservation resources.Currently,only a single factor is often employed in the assessment of vegetation conservation,and studies on systematically combining multiple factors to assess priorities in vegetation conservation are scarce.This study therefore aims to assess the conservation priorities of natural vegetation types by integrating their threatened status and conservation values. Methods:We assessed the conservation priorities of 104 natural formations in Yunnan Province.First,we determined the threatened status of the formations based on their declining and restricted distribution(Criteria A and B)according to the IUCN Red List of Ecosystems.We then assessed the conservation value of each formation by calculating the weighted sum of three indicators,namely endangered species richness,canopy height,and carbon storage.We finally calculated the conservation priorities of the formations by integrating the data layers of threatened status and conservation value.Based on the priority assessment results,we further classified the formations into four types. Results:The results were as follows:(1)66.3％of the 104 formations were characterized as vulnerable(VU),endangered(EN),or critically endangered(CR),the restricted distribution range of the formations was the key factor influencing the assessment of their threatened status.(2)45.2％of all formations had a high or extremely high degree of conservation value,whereas 38.5％of all formations had a moderate conservation value;overall,vegetation quality was high.(3)The integrated assessment results suggest that priority conservation should focus on the 31 formations with high or extremely high conservation values characterized as threatened.Secondary priority conservation is recommended for the 26 formations with moderate conservation value and threatened status,whereas proactive conservation measures should be implemented for the 16 formations with extremely high or high conservation values but not currently threatened,and implementing general conservation for all other formations.(4)The coverage ratio of priority,secondary and proactive conservation vegetation in Yunnan's nature reserves was 19.5％,9.7％,and 16.9％respectively,with some conservation gaps. Conclusion:We assessed the conservation priorities of natural vegetation by considering both threatened status and conservation value.The methods and analysis developed in this study provide vital science-based support for regional conservation planning and actions in terms of ecosystem conservation.

The coverage of protected areas (PAs) remains far from the Kunming-Montreal target and degraded ecosystems are greatly limiting the conservation efficiency of PAs. Therefore, this paper proposes a method to identify conservation and restoration priority areas to supplement existing PAs. A case study was conducted focusing on Yunnan, southwestern China, which intersects with three world biodiversity hotspots. First, the spatial ranges for 3768 representative conservation plant species were mapped using species distribution models. Subsequently, planning units were classified into three restorability categories, namely no-need restoration, potentially restorable and non-restorable units, according to land cover changes between 2000 and 2020. Then, conservation and restoration priority areas were identified by applying a two-step systematic conservation planning process. Finally, replacement cost analysis was applied to compare the effectiveness of existing PAs and the overall 30 % priority areas. Northwestern, southwestern, and southeastern Yunnan have high biodiversity conservation values. Especially in eastern and southeastern Yunnan, large amounts of restoration priority areas were identified. Conservation and restoration priority areas account for 15.80 % and 3.69 % of Yunnan's land, respectively. Compared to existing PAs, conservation priority areas can increase the number of species covered from 2461 to 3277, and further to 3566 when including restoration priority areas. Compared to existing PAs, the mean species coverage in the overall 30 % priority areas has increased from 27.28 % to 72.69 %. Notably, 12.86 % of existing PAs were identified as restoration priority areas. This study indicates that in addition to conservation measures, implementing restoration strategies in high conservation-value areas is equally important.

Efficient conservation planning is a necessary approach for protecting endangered species with deficient distribution data and reducing biodiversity loss. Richness- and complementarity-based algorithms can improve conservation planning efficiency to different degrees, but differences in planning efficiency caused by data availability and algorithms are often disregarded and require clarification. Here, we classified endangered, endemic, and national key protected plant species based on their occurrence data availability for species distribution modeling. We implemented species richness- (SRA), species number complementarity- (SNCA) and species value complementarity-based algorithms (SVCA) to identify priority conservation areas (PCAs) in the Southeast Himalaya Biodiversity Priority Conservation Area. We established six scenarios and compared their planning efficiencies. The spatial distribution of PCAs and their conservation efficiency varied depending on data availability and optimization algorithms. The 17%, 30%, and 45% PCAs identified in the six scenarios differed in geographical pattern, whereas differences in species occurrences had less effect on their conservation efficiency. Specifically, the PCAs identified by using species with sufficient data as surrogates captured species with high conservation value. Additionally, SNCA was better suited for capturing species number, and SVCA was more cost-effective for protecting species area and species of high conservation value, but SRA distribution pattern presented less fragmented and well-connected. Nevertheless, 50.11% area of the optimized PCAs remained uncovered by existing protected areas. Our analysis reveals the influence of species occurrence data availability, and algorithms on planning efficiency, and provides a cost-effective solution for improving planning efficiency and optimizing protected area network in data deficient areas.

Climate heterogeneity is commonly associated with exceptionally high species richness, thus bolstering ecological resilience and maximizing long-term biodiversity benefits. However, few studies have been conducted to examine the implications of climatically heterogeneous areas (CHAs) for effective biodiversity conservation. In this study, we collected occurrence records of birds and vascular plants in a biodiversity hotspot in Yunnan, China, and delineated corresponding CHAs. The conservation effectiveness of CHAs for species diversity was demonstrated through a comparison of climate- and species-based prioritization schemes, incorporating surrogacy analysis and species representation. Despite significant spatial discrepancies with species-based conservation prioritization, we found that a prioritization scheme based on CHAs would effectively conserve more than 86.3% of Yunnan's birds and vascular plant species, regardless of spatial scale. The coverage of protected areas for priority conservation areas of two prioritization schemes is relatively low (<14.4%). Therefore, our study also underscores the significant conservation gaps for birds and vascular plants in Yunnan revealed by both prioritization schemes, with the latter emphasizing the crucial roles of mountainous regions, gorges, and particularly dry valleys along the Jinsha River and Yuanjiang River. These conservation gaps provide complementary and previously hidden potential conservation areas for the preservation of species diversity in Yunnan. Overall, our study demonstrates that incorporating CHAs into conservation prioritization represents a smart and effective approach for safeguarding species diversity, serving as a paradigm for integrating abiotic factors into conservation planning and providing valuable strategies to conserve species diversity in biodiversity hotspots.

Purpose Livestock dung deposition significantly impacts the greenhouse gas emissions, soil nutrient dynamics, soil microbial biomass, and enzyme activities of grasslands. As a typical process of microbial community coalescence, dung deposition may also affect grassland ecosystems by changing soil microbiomes. However, its effects on alpine grassland soil microbial abundance, diversity, and community profiles remain largely unexplored. Therefore, this study aimed at evaluating the influences of dung deposition-mediated community coalescence on alpine grassland soil microbiomes. Methods Soils were collected from 40 pairs of yak dung-covered patches and their adjacent grasslands along a transect on the Tibetan Plateau. Quantitative PCR and amplicon high-throughput sequencing were employed to determine the abundance, diversity, and community composition of soil microbes. Results Dung deposition-mediated community coalescence significantly altered multiple soil properties and the composition of soil microbial communities. Specifically, the relative abundance of diazotrophs, photoautotrophs, nitrifiers, pathogens, and lichens significantly decreased due to dung-soil microbial community coalescence, while the proportions of methanogens, denitrifiers, saprotrophs, cellulolytic microbes, and chemoheterotrophs showed opposite change trends. Soil microbial community structures were closely correlated with multiple soil and plant properties (e.g., nutrient contents and plant aboveground biomass) in both control and dung-covered soils. Additionally, dung deposition-mediated community coalescence dramatically decreased the complexity of soil microbial co-occurrence patterns but exerted negligible effects on network stability. Conclusions These findings suggest that yak dung deposition has the potential to increase greenhouse gas emissions, but it can also improve soil nutrient contents and decrease plant disease risks. This study highlights the dual effects of dung deposition on grassland ecosystems and provides critical information for guiding alpine grassland management.

Ecosystems are exposed to continuous shifts in droughts. This shows up as a point on a pixel that moves continuously in a specific direction and angle (spatial migration) to preserve its original climate niche. We currently do not have a good understanding whether ecosystems exposed to droughts are more sensitive to droughts temporal trends or spatial migrations. Using dataset of vapor pressure deficit (VPD), total water storage anomaly (TWSA), and standardized soil moisture index (SSMI), we tested the spatiotemporal sensitivity of vegetation phenology to extreme droughts and compound extreme droughts, respectively. On the temporal scale, we found that the differences in the sensitivity of vegetation phenology to VPD, TWSA, and SSMI were mainly reflected in the lag time. Compared to normal years, however, the start of growing season (SOS) was advanced and the end of growing season (EOS) was delayed in most pixels even under extreme preseason droughts. Such results can be attributed to the extreme atmospheric driving forces. On the spatial scale, the spatial migration velocity of SOS and EOS was best correlated with the extreme atmospheric drought (r = 0.5, p < 0.01) and the extreme soil drought in the third layer (r = 0.43, p < 0.01), respectively. Compared to extreme droughts, however, the effects of compound extreme droughts on the spatial migration of vegetation phenology only produced weak negative feedback (|r| < 0.1, p < 0.01). This indicate that the velocity of vegetation phenology was consistent with the velocity of single extreme droughts. Our results emphasize the temporal sensitivity of droughts on vegetation phenology and the accompanying effects of extreme droughts on the spatial shifts in vegetation phenology.

定期开展保护成效评估,是制定保护决策与提升保护地管理效果的重要基础.景观动态能够直观地指示保护地成效,但针对区域尺度保护地网络,采用多指标评价体系的景观保护成效研究还相对缺乏.以我国西南地区102个国家级和省级自然保护区为研究对象,从景观生态状况、自然生境格局与连通性以及人类干扰4个方面构建评价指标体系,应用熵权TOPSIS模型的综合分析方法,评估自然保护区在1990-2015年间的景观保护成效,并探讨自然保护区属性与保护成效间的关系.研究发现:(1)从1990-2000年至2010-2015年评估时段,保护成效上升的自然保护区数量约占77.5％,但低于保护成效综合评价指数平均值的自然保护区数量持续增加,表明自然保护区的整体景观成效趋于两极化发展.(2)自然保护区景观保护成效表现出明显的空间差异性,保护成效明显提升的自然保护区主要分布在川藏滇桂四省区,成效下降的自然保护区主要分布在黔、桂及两者交界区域.(3)不同类型与等级的自然保护区之间保护成效存在差异.类型上,整体成效表现为森林生态类较好、内陆湿地类次之、野生动植物类较差,而保护成效改善程度为野生动物类＞内陆湿地类＞森林生态类＞野生植物类;等级上,整体成效表现为国家级自然保护区优于省级.总的来说,西南自然保护区网络对区内自然生境景观的保护效益显著,但存在保护成效明显下降的自然保护区(如茂兰、麻阳河和赤水桫椤等)需要引起重视.研究能够为区域尺度保护地体系的成效提升与优化调整提供科学支持.

纳帕海湿地是横断山区和长江上游的生物地理区域核心部位,植物多样性丰富,但其种子植物多样性调查及其区系分析研究尚待开展.为此,2021 年 8 月采用样线和样方调查法,对该区域种子植物开展野外调查,并分析其种子植物的多样性和区系特征.结果显示:(1)种子植物共 40 科 91 属 166 种,禾本科、菊科、毛茛科、蔷薇科、蓼科、莎草科、玄参科为优势科,占总种数的 60.24%;委陵菜属、蓼属、马先蒿属、毛茛属为优势属,所含种数均在 8 种以上.此外,单种科、单种属占总科数、总属数比例均较大,占比分别为 52.50%、67.03%.(2)各水分生态型中,以中生植物为主,湿生植物次之,水生植物较少,分别占总种数的 53.01%、33.73%、12.05%,陆生植物最少,仅有 2 种;各生活型中,多年生草本优势明显,占比高达 78.92%,一年生草本较少,占比为 19.88%,另有灌木 2 种.(3)在科水平上区系类型较为单一,以世界分布型为主,占总科数的75%,热带性质科和温带性质科之比为 1︰1;在属水平上区系成分多样性高,包含 10 个分布区类型,其中,温带成分显著,具有部分热带成分,中国特有成分较低.研究结果表明:纳帕海湿地种子植物多样性丰富,以中生植物和多年生植物为主,区系成分较复杂,温带成分显著,具有明显的过渡性质.研究结果可为纳帕海或类似湿地的生态系统保护以及恢复、重建提供科学参考.