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: 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.

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.

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.

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: Traditional methods for calculating genetic diversity necessitate uniform sequence lengths within species. However, the sequences available in public databases often exhibit variability in length, thereby complicating the processes of haplotype identification and genetic diversity assessment. Although methods exist for estimating haplotype diversity and nucleotide diversity from sequences with unequal lengths, there is currently no effective methodology for calculating haplotype richness. 

Methods: In response to this issue, this research introduces a method to estimate haplotype richness for DNA sequences of varying lengths, utilizing the nucleotide differences between paired sequences (K_ij). Three analyses were conducted to validate the method’s performance: (1) For sequences that were of equal length, the results obtained from our method were compared with those from DnaSP. (2) The algorithm’s performance with sequences of different lengths was tested by generating simulated sequences of random lengths from equal-length datasets of birds, mammals, and amphibians, and its generalization capability was evaluated using a medicinal plant dataset. (3) The method was employed to assess the latitudinal gradient patterns of haplotype richness in birds, mammals, and amphibians. 

Results: For sequences with equal length, the new method’s results were not significantly different from those of DnaSP (birds: W = 22,018, P = 0.845; mammals: W = 23,096, P = 0.990; amphibians: W = 3,518.5, P = 0.977) but it surpassed it in haplotype identification when base deletions occurred, identifying an average of 1.333 ± 0.188 more haplotypes. (2) Random-length simulation trials confirmed the effectiveness (The mean relative error indicated an overall accuracy of 0.130 ± 0.106, while the variance of relative error showed a stability of 0.007 ± 0.007) and applicability of this method in estimating haplotype richness for sequences of varying lengths. (3) An examination of latitudinal haplotype richness patterns found that birds and mammals exhibited a significant decreasing trend in the Southern Hemisphere but a relatively stable decreasing trend in the Northern Hemisphere, whereas amphibians showed a continuous decline from south to north. 

Conclusions: This study advances the development of more precise quantitative methodologies and introduces novel analytical tools for the conservation of genetic diversity.

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: 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.

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.

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.

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.