Backgrounds & Aims：With the surging pressure on Biodiversity conservation and the proposal of "no net loss" (NNL), biodiversity offsetting (BO) as the final step of the Mitigation Hierarchy, has been rapidly implemented in Europe and America, becoming a way to address the economic pressure of biodiversity conservation, insufficient social participation, and the contradiction between land development and the encroachment on ecological land. 

Method & Results：This paper reviews the current research status at home and abroad and the overview of foreign biodiversity offset projects, analyzes the gap in research and practice in China, distinguishes the connotations of related concepts, and clarifies the implementation paths of the projects. By summarizing the key issues and strategies in four aspects, namely, quantification of biodiversity offset accounting abroad, enhancement of add-ons, improvement of mandatory frameworks, and guarantee of long-term nature, it provides theoretical basis and practical experience for biodiversity offset in China. 

Conclusions：Three suggestions are put forward: clarifying the status and implementation path of biodiversity offsetting, promoting the implementation of biodiversity offsetting, and strengthening the research on the optimization of biodiversity offsetting efficiency, with the aim of further improving China's biodiversity conservation system and constructing a highly adaptable biodiversity offsetting framework.

Aims: The increasing adoption of machine learning (ML) in ecological research has enabled the modeling of complex, nonlinear ecological patterns. However, the “black-box” nature of many ML models limits their interpretability, hindering the extraction of ecological insights. This review aims to introduce the core concepts, methods, and practical tools of interpretable machine learning (IML), and to demonstrate how these techniques can enhance ecological understanding from predictive models. 

Methods: We first clarify key distinctions among white-box and black-box models, global and local interpretability, and intrinsic versus post-hoc explanation frameworks. Using a simulated dataset representing plant diversity and environmental variables (e.g., elevation, temperature, soil moisture), we apply both white-box models (e.g., linear regression, decision trees) and black-box models (e.g., random forest) to illustrate major interpretability techniques, including regression coefficients, permutation importance, partial dependence plots (PDP), accumulated local effects (ALE), SHapley Additive exPlanations (SHAP) values, and Local Interpretable Model-agnostic Explanations (LIME). 

Results: White-box models offer direct and transparent interpretability through their model structure, while black-box models require additional tools to derive explanations. Our case study shows that both model types can yield consistent insights about variable importance and ecological relationships. Furthermore, methods such as ALE and SHAP effectively address common limitations in conventional approaches like PDP by accounting for feature interactions and dependencies. 

Conclusion: IML provides a valuable toolkit for improving model transparency and interpretability in ecological research. It serves as a crucial complement to traditional statistical modeling, enabling researchers to extract meaningful ecological interpretations from complex models. As ecological data and modeling complexity continue to grow, the integration of IML techniques will become increasingly important for hypothesis generation and ecological decision-making.

Background & Aims: Species distribution models (SDMs), often synonymous with ecological niche models (ENMs), have solidified their position as indispensable tools in modern macroecology, biogeography, species invasion and conservation. Their utility in predicting a species’ potential geographic range, evaluating the impacts of climate change, and guiding targeted conservation efforts has led to a remarkable surge in their popularity and application over the last three decades. However, this rapid expansion has also exposed a significant and persistent conceptual gap: a growing disconnect between the practical application of modeling techniques and the foundational ecological theory that should guide them. A primary source of this issue is the widespread confusion surrounding the concept of the “ecological niche”. This ambiguity has led to conceptual errors, inappropriate method use, and potentially flawed ecological inferences. This paper addresses this critical gap by systematically reviewing the core niche concepts, linking them to specific modeling paradigms, diagnosing prevalent issues in current research, and offering recommendations to promote a more theoretically grounded and robust application of SDMs. 

Review Results: The term “ecological niche” is not a single, unified concept. It encompasses three distinct yet complementary ideas. The Grinnellian niche defines a species’ existence based on the abiotic environmental conditions and habitat requirements that allow it to persist. As a “scenopoetic” or habitat-based framework, it is most closely aligned with standard SDMs, which statistically correlate species occurrence records with broad-scale climatic and environmental variables. The Eltonian niche, conversely, focuses on a species’ functional role within a community, emphasizing biotic interactions such as resource consumption, predation, and competition. This concept is central to community ecology and is better represented by methods like joint species distribution models (JSDMs) that account for residual correlations between species, or through explicit network analysis. The Hutchinsonian niche provides the most formal definition, conceptualizing the niche as an “n-dimensional hypervolume” encompassing all environmental and resource variables. Different modeling approaches correspond to these niche concepts. Standard correlative SDMs (e.g., MaxEnt, random forest) are primarily used to model the Grinnellian niche, generating a map of environmental suitability based on abiotic variables. To explore the Eltonian niche, JSDMs simultaneously model multiple species to infer interspecific interactions. The Hutchinsonian framework, particularly the concept of the hypervolume, is directly operationalized by analytical methods that quantify niche breadth, overlap, and centrality in multidimensional space. Mechanistic models, which use principles of physiology to predict survival and reproduction, offer a valuable complementary approach to approximate the fundamental niche. Despite these advances, the application of SDMs is fraught with common pitfalls. The most critical error is the fundamental vs. realized niche fallacy, where researchers mistakenly interpret the output of a standard SDM, which is trained on a species’ actual distribution, as a representation of its full fundamental niche. In reality, these models typically capture only a portion of the realized niche, constrained by unmeasured biotic factors and dispersal limitations. Additionally, many studies violate the core assumptions of SDMs, such as the assumption that species are in equilibrium with their environment or that sampling is unbiased. Ignoring biotic interactions and failing to account for non-equilibrium dynamics (e.g., recent invasions) further limits the accuracy and reliability of these models. 

Conclusion: To advance species distribution modeling, this paper advocates for a multi-pronged approach grounded in ecological theory. First, researchers must strive for greater conceptual clarity, explicitly stating which niche concept their study addresses and interpreting results within that defined framework. Second, there is a clear need for enhanced methodological rigor and integration, encouraging the development of hybrid models that combine the strengths of different modeling paradigms, such as incorporating biotic interactions or dispersal dynamics into standard SDMs. Furthermore, adherence to best practices in data collection, model selection, and rigorous validation is paramount. The future of the field lies in transcending simple correlative methods and embracing a more integrative science that synthesizes Grinnellian, Eltonian, and Hutchinsonian perspectives. By leveraging new data streams and grounding our work in a deep understanding of ecological theory, we can ask more complex questions and provide more robust guidance for biodiversity management in an era of rapid environmental change.

Background & Aims: Both the Kunming-Montreal Global Biodiversity Framework (KMGBF) and China’s National Biodiversity Conservation Strategy and Action Plan (NBSAP, 2023-2030) have highlighted the engagement of non-state actors in biodiversity conservation. Philanthropic funding provided to non-state actors for biodiversity conservation purposes can significantly fill the public funding gap. The Fifteenth Meeting of the Conference of the Parties (COP 15) to the Convention on Biological Diversity provided unique opportunities for Chinese non-state actors to conserve China’s biodiversity. In 2021, during the first phase of COP 15 in Kunming, a coalition of ten Chinese non-state actors pledged to moblize RMB 2.55 billion (USD 359 million) for biodiversity conservation by 2030. 

Method：The study provides a systematic analysis by tracking the spending, outputs and outcomes of the pledge made in COP15 over the past five years, offering a systematic analysis of how biodiversity philanthropic funding in China has been mobilized and what results have been attained. 

Results: During 2020-2024, 11 major biodiversity-related philanthrophic foundations invested a total of RMB 3.428 billion (USD 481 million) on biodiversity conservation activities. Among them, the seven foundations that made funding pledge at COP15 have spent RMB 1.548 billion (USD 217 million), equivalent to 60.73 % of the total pledged amount. This funding has enabled non-state actors to deliver tangible results in the conservation of threatened species, habitat, area-based conservation, and biodiversity mainstreaming in China. The study also identified limitations of China’s conservation philanthropic funding: a disproportionate share of funding has flowed to afforestation-based “ecological restoration”, while comparatively less has been allocated to aligning with national programmes or transforming private sector’s business models. 

Conclusion: The study recommends that Chinese philanthropic funding should deepen collaboration with government-led biodiversity conservation initiatives to achieve the targets and goals of the NBSAP, and that transforming private sectors’ business models toward nature positivity should also be prioritized.

Unequal sampling is a common issue in field-based community ecology. Choosing α-diversity metrics that remain robust when sample sizes vary among plots is critical for reliable biodiversity assessment. This study evaluated the performance of nine diversity indices, including five “observed” indices calculated directly from the data (species richness, Shannon index, Simpson index, Hurlbert’s rarefied richness and Fisher’s α) and four “richness-estimator” indices (Chao1, ACE, iNEXT, and TES). Using simulation, the performance of each index was evaluated under a gradient of minimum-sample thresholds, and for each case the accuracy and precision between-sites variance (linear regression R² ) was recorded. The simulation build up 20 sites in which “true” species richness (S) was linearly correlated to an environmental gradient (x) with a theoretical coefficient of determination R² = 0.80. Four unequal-sampling scenarios were then generated by imposing different minimum sample sizes per site. For each scenario, linear models were fitted between every diversity index and x, recording the corresponding R² . The results indicate that sample size (the number of individuals recorded at a sampling site, as well as the equivalent sampling completeness) is the primary factor determining index performance. As sample size increased, model R² of all diversity metrics significantly improved. Under extremely low sampling (minimum < 20 individuals; sampling coverage < 20 %),rarefied richness had a higher R² than other indices. When the minimum sample size reached 100 individuals, the estimator indices group outperformed the observed indices. This study further clarified the minimum sample size and the corresponding sampling completeness required for each index to recover the predetermined R² . Overall, rarefied richness is recommended for highly unequal, sample size-poor scenarios. In practice, rarefaction threshold should be set at a relatively high level (e.g., > 40 individuals) that can enhance the overall comparability among sampling sites, even if it results in the exclusion of extremely under sampled sites. Once sampling completeness is adequate, richness estimators are preferable, as they can generate extrapolated richness that are close to the true gradient.

Aims: Macrofungi hold significant ecological and socioeconomic value. To analyze the floristic composition and species diversity of macrofungi in the karst areas of southwestern China and clarify the influence of distinctive geomorphological features on their distribution, a comprehensive survey was conducted. The results are expected to provide a scientific basis for biodiversity conservation and sustainable utilization of macrofungal resources in the areas. 

Methods: Over a seven-year period, 12,451 macrofungal specimens were collected from Guizhou Province, Guangxi Zhuang Autonomous Region, and Yunnan Province using line transect and random survey methods. Species identification integrated morphological characterization and molecular phylogenetic analysis using ITS sequences. Taxonomic data were cross-referenced with literature to analyze floristic components and resource values. 

Results: A total of 1,505 macrofungal species (2 phyla, 10 classes, 27 orders, 118 families, 418 genera) were identified in the karst areas of southwestern China. Among these, 19 new species, 1 newly recorded species in China, and 1 new record for Guizhou have been published. Dominant families constituted 34.75% of total families (41/118), with Russulaceae, Polyporaceae, and Boletaceae exhibiting the highest species richness. At generic level, 17.46% of genera (73/418) demonstrated dominance, particularly Russula, Amanita, and Lactarius. 

Conclusions: The evaluation of macrofungal resource values identified 263 edible species, 176 medicinal species, and 237 toxic species, indicating considerable fungal diversity in this region. Floristic analysis revealed cosmopolitan distributions predominate at the family level (54.24%), while higher biogeographic diversity was observed at the genus level.

Background: With the accelerating pace of global urbanization, urban biodiversity is facing increasingly severe challenges, including habitat loss, ecological fragmentation, and the decline of native species. As the fundamental spatial and functional unit of urban spatial structure and ecological system, urban blocks not only support residential, commercial, and social functions, but also serve as critical interfaces where the built environment interacts with ecological systems. They play a vital role in maintaining ecosystem services, supporting habitat provision, and shaping local biodiversity patterns. In recent years, as urban regeneration progresses, how to effectively embed biodiversity enhancement goals into spatial planning at the scale of urban block has become a pressing and central issue in the field of urban ecological planning and sustainable design. 

Aims & Methods: While studies examining the impacts of urban block built environment on urban biodiversity have increased in recent years, systematic reviews and integrative theoretical frameworks remain insufficient, constraining theoretical development. To address this gap, this study conducts a bibliometric and content-based review of relevant domestic and international research, and categorizes built environment factors affecting urban biodiversity at the block scale into five categories: block greening, vegetation characteristics, land use, three-dimensional morphology, and mediating factors (human activity and climate factors). Based on this framework, the study comprehensively summarizes various impact factors and their relationships identified in existing research. 

Results: Findings reveal three key discoveries: (1) the urban block scale is critically important for biodiversity research, exhibiting distinct characteristics that differentiate it from other spatial scales; (2) the urban block built environment influences biodiversity through both direct and indirect pathways, creating complex impact mechanisms; (3) human activity in urban blocks demonstrate significant mediating effects, while climate factors play important moderating roles in shaping biodiversity patterns, suggesting these interactive effects warrant in-depth future investigation. 

Conclusion: Based on this, the study proposes four major directions for future research: (1) deepening research on the impacts of block three-dimensional morphology on urban biodiversity; (2) revealing mediating effects of human activities at block scale; (3) exploring regulatory mechanisms of climate factors on biodiversity patterns; (4) developing differentiated guidelines and standards for refined urban block regeneration. 

Perspectives: This review aims to provide a systematic theoretical foundation for urban block spatial planning oriented toward biodiversity enhancement, and to promote the construction of urban spatial systems characterized by ecological resilience and integration with nature.

Background & Aims: Meta-analysis is a crucial statistical tool for deriving generalized conclusions through the weighted analysis of data from case studies, with broad applications in ecology. However, for a long time, researchers have significant misconceptions regarding the fundamental principles and methodological framework of meta-analysis, which has contributed to its misuse, even erroneous application. 

Review Results: According to the standard steps of conducting a meta-analysis, this article summarizes the basic feature of meta-analysis and highlights critical considerations for its application, encompassing aspects such as: defining key concepts, literature search and screening, construction of effect sizes, selection of models, incorporation of special data structures, inclusion of explanatory variables, assessment of result reliability, and relevant software tools. 

Conclusion: The clarifications of related concepts and key points will aid in constructing more precise and appropriate meta-analysis models, thereby enhancing the reliability of results. Furthermore, continued advancements in meta-analysis methodology is poised to offer more robust and reliable technical approaches for addressing numerous fundamental scientific questions in ecology.

Aims: Forest litterfall, serving as a vector for material cycling, energy flow, and nutrient balance within forest ecosystems, is a vital component of these systems. This study aims to investigate the seasonal dynamics of litterfall production and the drivers of its spatial distribution in the seasonal rainforest of Fangcheng, Guangxi. 

Methods: Utilizing a 1-ha forest dynamics plot in Guangxi Fangchenggang Golden Camellia National Nature Reserve, as the study site, 40 litterfall collectors were deployed. Through 6 years of monitoring forest litterfall data, we analyzed the region’s litterfall composition, spatiotemporal distribution characteristics, and influencing factors. 

Results: The results indicate that the average annual forest litterfall production in Guangxi Fangchenggang Golden Camellia National Nature Reserve, was 7,506.38 ± 766.94 kg/ha, exhibiting significant interannual fluctuations. The proportion of each component was as follows: leaves (60.24%) > branches (19.34%) > miscellaneous debris (17.70%) > reproductive structures (2.72%). The monthly dynamics of total litterfall and each component showed similar patterns, all exhibiting a tri-modal distribution with peaks observed in April, August, and October. Within a 5 m neighborhood of the litterfall collectors, aspect, soil temperature, soil nutrients, species richness, and sum of basal area at breast height (IBA) were identified as the primary drivers of forest litterfall production in this region. These factors exert a direct positive influence on the total litterfall biomass. In contrast, soil nutrients indirectly impact total litterfall biomass through their relationship with the sum of basal area. 

Conclusion: Continuous 6-year monitoring results reveal that forest litterfall quantity in Guangxi Fangchenggang Golden Camellia National Nature Reserve, undergoes pronounced seasonal variation, and that biotic factors, abiotic factors and soil factors collectively influence the spatial distribution of litterfall.

Aim: The ex situ conservation botanical garden, as a relatively homogenized artificial environment, offers an ideal model for exploring environmental adaptation mechanisms in plant–pollinator interactions. This study aims to investigate how the translocation of fig trees (Ficus auriculata Lour.) to ex situ conservation botanical gardens influences the composition of fig volatiles, the structure of fig wasp communities, and the stability of their obligate mutualistic interactions relative to natural habitats. 

Method: We analyzed volatile compounds emitted during the female flowering phase of male fig trees using dynamic headspace sampling combined with gas chromatography–mass spectrometry (GC–MS). Concurrently, we collected fig wasp communities from male flowering phase figs in male trees to compare community structure and interaction networks between natural habitats and ex situ conservation botanical garden. 

Results: (1) Volatile compositions differed significantly between the two habitats. Natural habitat figs emitted more defense–related fatty acid derivatives, while ex situ conservation botanical garden figs predominantly released terpene compounds that attract pollinating wasps. (2) Fig wasp communities also exhibited marked differences. Natural habitats had higher wasp diversity and a greater proportion of non–pollinating wasps, whereas pollinators accounted for 99.07% of individuals in ex situ conservation botanical garden, compared to 76.55% in natural habitats. (3) The fig–fig wasp interaction network in natural habitats showed higher connectivity, modularity, and robustness, suggesting greater complexity and adaptability. In contrast, ex situ conservation botanical garden network exhibited stronger specialization and nestedness, with the homogenized environment increasing the sensitivity of fig wasp communities to environmental change. 

Conclusion: This study is the first to systematically demonstrate how the fig–fig wasp interaction network adapts to artificial environments through changes in chemical signaling and community structure. The results confirm that habitat simplification can reshape obligate mutualisms by altering volatile profiles and niche partitioning. These findings enhance our understanding of the environmental adaptability of specialized mutualistic systems and offer important insights for the ex situ conservation of fig trees and biodiversity management strategies.

Background & Aim: As the threat posed by invasive alien species to China's ecosystems becomes increasingly severe, improving the governance system has emerged as a crucial issue at present. Although existing studies have explored legal and administrative management issues in the governance of invasive alien species, most of these studies focus on governance discussions within a single field and fail to conduct systematic analysis from the perspective of the overall policy system. Therefore, in-depth research on the structural dilemmas existing in the policy system for invasive alien species governance—especially the exploration of its holistic construction—still holds significant academic value and practical significance. 

Method: Employing literature review and case analysis methods, this paper compiles public interest litigation cases related to invasive alien species in recent years, introduces judicial cases as practical illustrations, and further demonstrates their actual harms to the ecological environment, public interests, and biological security. 

Results: This paper sorts out the laws and policies regarding invasive alien species in China, and reveals the problems existing in current practice, such as the imperfection of the legal system, the lack of top-level design, the inadequacy of the name list system, the insufficient coordination of the implementation mechanism, and the weakness of basic guarantees and technical support. 

Conclusion: This paper proposes a holistic improvement pathway, which includes the following aspects: at the legislative level, it suggests improving legislation through the “code-specialized law” model, strengthening accountability, and optimizing government assessment mechanisms; at the implementation level, it is necessary to establish regulatory authorities, set up a regional joint meeting mechanism, and optimize name-list management; and at the support and guarantee level, efforts should be made to promote the marketization of ecological compensation, enhance interdisciplinary collaboration, and implement the “project bidding for talent recruitment” mechanism. The purpose of this paper is to provide theoretical and practical references for the governance of invasive alien species.

Background & Aims: This study aims to address the critical gap in the implementation of the Convention on Biological Diversity (CBD) by systematically analyzing the structural deficiencies of the existing financial mechanism centered on the Global Environment Facility (GEF). It seeks to develop and rigorously evaluate alternative options for a new financial mechanism that operates under the authority of the Conference of the Parties (COP), as mandated by Article 21 of the Convention. The primary objective is to provide a robust, evidence-based framework to inform the ongoing international negotiations, particularly following the impasse at COP17. 

Method: The research employs a qualitative comparative policy analysis, grounded in the negotiation progress of CBD, in which the author was directly involved. A structured multi-criteria assessment framework was developed, evaluating proposed mechanisms against eight key dimensions: legal coherence with the CBD, governance effectiveness, financial sustainability, operational efficiency, accessibility for Least Developed Countries, inclusivity, political acceptability, and implementation feasibility. 

Results: The analysis confirms significant structural flaws in the interim GEF arrangement, including a governance misalignment with the COP, complex access procedures, and limited recipient country ownership. Three distinct alternative models were formulated and assessed: Establishing a new Global Biodiversity Fund demonstrates high legal coherence and long-term effectiveness but faces substantial political and implementation hurdles; Deeply reforming the existing GEF offers practical feasibility but yields uncertain and potentially limited outcomes in addressing core governance issues; A hybrid transitional mechanism within the GEF structure emerges as the most viable compromise, balancing political acceptability with a meaningful step toward a more accountable and accessible system. 

Conclusion & Recommendation: The study concludes that a hybrid transitional mechanism represents a possible viable pathway for breaking the current negotiation deadlock. It provides a concrete foundation for a consensus at COP17 while establishing a clear trajectory for future evolution. The findings underscore that the design of any new financial mechanism must strategically balance legal ideals with political and operational realities. This research provides a critical technical foundation for Parties to make informed decisions, ultimately supporting the establishment of a fair, efficient, and effective financial mechanism for implementing the Kunming-Montreal Global Biodiversity Framework.

Background & Aim：Human activities have substantially altered natural nitrogen (N) regimes, resulting in a marked increase in reactive nitrogen entering terrestrial and aquatic ecosystems. Nitrogen inputs, together with the key ecological processes they drive—such as atmospheric deposition, nitrogen transformation, and changes in nitrogen availability—play a central role in shaping ecosystem structure and functioning. Excess nitrogen inputs disrupt ecological balance through soil acidification, aquatic eutrophication, habitat degradation, and the spread of invasive species, thereby exerting persistent pressure on biodiversity. As biodiversity loss has become a global environmental concern, increasing attention has been directed toward understanding how externally driven nitrogen inputs influence ecological processes most closely linked to species coexistence, community assembly, and ecosystem stability. Rather than treating N inputs driven processes as a closed biogeochemical system, recent studies have emphasized nitrogen inputs as a dominant external driver with direct ecological relevance. At the same time, advances in molecular techniques, remote sensing, and large-scale ecological monitoring have provided new opportunities to examine nitrogen–biodiversity relationships across spatial and organizational scales. 

Progresses：A growing body of evidence demonstrates that the ecological consequences of nitrogen inputs are strongly context dependent. In ecosystems characterized by low background nitrogen availability, moderate nitrogen enrichment may temporarily enhance primary productivity and, in some cases, support short-term increases in local biodiversity. In contrast, sustained or excessive nitrogen inputs are consistently associated with negative biodiversity outcomes, including species loss, community homogenization, and functional simplification. These effects are mediated by multiple interacting processes, such as shifts in soil physicochemical conditions, altered nitrogen availability and stoichiometric balance, and changes in microbially regulated nitrogen transformation pathways. Nitrogen enrichment often favors fast-growing, resource-acquisitive species, intensifying competitive exclusion and reducing niche differentiation, while simultaneously restructuring microbial communities and their functional capacities. Importantly, biodiversity itself can influence nitrogen dynamics. Functional diversity among plants, microorganisms, and soil fauna contributes to more efficient nitrogen use, reduced nitrogen losses, and greater ecosystem resistance to external nitrogen stress. Building on these insights, research on nitrogen inputs and associated ecological processes has increasingly informed biodiversity monitoring, ecological risk assessment, and ecosystem restoration, highlighting the practical relevance of process-oriented nitrogen research. 

Perspectives：Despite substantial progress, significant challenges remain in translating scientific understanding of nitrogen–biodiversity interactions into effective management and policy actions. Data relevant to nitrogen inputs and biodiversity responses are often fragmented across ecosystems, spatial scales, and disciplinary domains, limiting integrative analysis. In addition, monitoring approaches and indicators remain insufficiently standardized, constraining comparisons among regions and long-term assessments. Nitrogen management policies are frequently developed in isolation from biodiversity objectives, reducing the potential for synergistic outcomes. Future research should therefore focus on integrating multi-source datasets, including field observations, molecular information, and remote sensing products, to better capture the dynamics of nitrogen-driven biodiversity change. Developing standardized, process-based monitoring frameworks will be essential for linking nitrogen inputs to biodiversity responses in a policy-relevant manner. Strengthening coordination across sectors—particularly agriculture, environmental management, and biodiversity conservation—will further support the incorporation of nitrogen considerations into governance and decision-making. By adopting a process-informed perspective that explicitly connects nitrogen inputs, key ecological processes, and biodiversity outcomes, research in this field can provide more robust support for ecosystem restoration, adaptive management, and the implementation of the Kunming–Montreal Global Biodiversity Framework.

Aims: Environmental DNA (eDNA) technology has been increasingly applied in biodiversity research. However, its rapid development has also sparked methodological debates. A key issue involves the selection of bioinformatic pipelines, particularly for extremely biodiverse taxa such as invertebrates. Bioinformatic pipelines significantly affect eDNA-based biodiversity profiles, yet a systematic comparative evaluation of relevant pipelines is currently lacking. Therefore, this study aims to compare and evaluate bioinformatic pipelines commonly used for analyzing eDNA-derived invertebrate sequencing data. 

Method: Invertebrate metabarcoding sequencing was carried out on freshwater eDNA samples, and the performance of various bioinformatic pipelines in processing invertebrate sequences was comparatively assessed. Four commonly used clustering or denoising methods (UPARSE, Swarm, UNOISE, and DADA2) and three taxonomic assignment methods (BOLDigger, BLASTN, and Naïve Bayesian Classifier) were selected, together constituting 12 bioinformatic pipelines. 

Results: Of the 12 evaluated pipelines, the combination of DADA2 denoising and BOLDigger taxonomic assignment yielded the largest number of invertebrate molecular operational taxonomic units (MOTUs), along with the highest levels of taxonomic coverage and resolution. Among the four clustering or denoising methods, UNOISE and DADA2 denoising yielded more invertebrate MOTUs than UPARSE and Swarm clustering. Among the three taxonomic assignment methods, BOLDigger and BLASTN yielded higher taxonomic coverage and resolution than Naïve Bayesian Classifier. 

Conclusion: These findings have significant implications for eDNA-based research of freshwater invertebrate biodiversity. Furthermore, our results suggest that bioinformatic pipelines should be adjusted according to different study taxa and barcode regions to obtain accurate and reliable biodiversity data.

Aims: Hainan Tropical Rainforest National Park is a key biodiversity hotspot in China, yet it has experienced increasing pressure in recent decades from land-use changes including deforestation, agricultural expansion, and urbanization. However, a systematic understanding of the structural characteristics of stream fish communities in this region and their specific responses to these changes remains limited. 

Methods: From 2024 to 2025, we collected data of fish assemblages and environmental variables across 40 stream sites in and around the Limushan and Jianfengling. We compared spatial patterns in fish community composition and diversity between the two regions using multiple statistical approaches. Similarity percentage (SIMPER) analysis was applied to identify key species contributing most to community dissimilarities. We used random forest models to identify the key environmental factors of stream fish diversity under land-use change and to reveal their nonlinear response patterns. Results: Fish community composition differed significantly between Limushan and Jianfengling, with Onychostoma lepturum, Aphyocypris normalis, Opsariichthys hainanensis, and Microphysogobio kachekensis identified as the key species driving these distinctions. Species richness was significantly higher in Limushan than in Jianfengling, while fish diversity in Jianfengling also exhibited lower inter-site variability, indicating a more homogeneous spatial distribution. The major environmental drivers of fish diversity and their response patterns showed clear regional specificity and pronounced significant nonlinear relationships with land use and associated environmental variables. In Limushan, elevation and the percentage of rainfed cropland were the dominant factors, whereas in Jianfengling, the percentage of herbaceous cover cropland and impervious surfaces, pH, and conductivity played more prominent roles. 

Conclusion: This study demonstrates significant spatial variation in stream fish community composition and diversity within Hainan Tropical Rainforest National Park, with diversity indices exhibiting nonlinear responses to land-use changes and environmental variables. These findings advance our understanding of how tropical rainforest stream fish communities are influenced by land-use change and provide a scientific basis for biodiversity conservation and sustainable landscape planning in tropical rainforest regions.

Aim: Spatial distribution of species represents a core issue in population biology and macroecology. Commonly used metrics for quantifying species aggregation include the negative binomial distribution parameter k for quadrat data and neighborhood density indices (such as Ω proposed by Condit et al, 2000 and k_ff proposed by Wiegand et al, 2025) for point pattern data. However, how the choice of index and its estimation uncertainty jointly affect the inference of the aggregation–abundance relationship remains unclear. 

Methods: We use a spatially explicit neutral model to simulate community data, then computed the aforementioned aggregation indices along with their standard errors. Finally, we analyzed how the index selection and its estimation error influence the power-law relationship between aggregation and species abundance. 

Results: (1) The three aggregation indices are strongly correlated under high aggregation intensity. Under weak aggregation, however, k remains effective in discriminating interspecific differences in aggregation, whereas the two point-pattern indices lack such discriminative power. (2) Estimation errors of different indices are weakly positively correlated. The standard error of k for rare and weakly aggregated species estimated by maximum likelihood is large, consistent with simulation results. In contrast, standard errors of point pattern indices based on resampling methods are generally smaller. (3) The aggregation–abundance relationship varies depending on the chosen index and whether weighted regression is applied. For 1/k, weighted regression consistently recovers the theoretical exponent of –1 predicted by neutral theory, whereas unweighted regression yields shallower slopes, especially in communities with weak aggregation. The exponents for the two point-pattern indices vary with the mean aggregation intensity of the community. 

Main conclusions: Ignoring the uncertainty in aggregation estimates can significantly bias inferences of the aggregation–abundance relationship, potentially leading to the incorrect rejection of the neutral null hypothesis (i.e., an increased risk of Type I error). The relationships derived from the two point-pattern-based indices reflect mean community aggregation but are not suitable for directly testing neutral assembly mechanisms. We recommend using the maximum likelihood estimate of the negative binomial parameter k and its standard error to measure species aggregation from quadrat data and incorporating uncertainty into subsequent analyses. For other aggregation indices, there is a need to develop predictions of their relation with species abundance from theories such as the simple neutral case.