Small habitat patches have been historically neglected in conservation, primarily because extinction risk is higher in small patches. Nevertheless, sets of small patches usually harbor more species than one or a few larger patches of equal total area. Resolving this inconsistency is key to policy and practice in biodiversity conservation. Our analysis of 32 datasets (603 patches and 2290 taxa) provides two novel lines of evidence confirming that small patches have disproportionately high value for biodiversity. First, sets of small patches harbor more species than large patches even when considering only species of conservation concern. Second, sets of small patches harbor more species than large patches even when the small patches are very small compared to the large patches. Therefore, higher extinction risk in small than large patches does not decrease the cumulative value of small patches for biodiversity. We contend that acknowledging the conservation value of small patches, even very small patches, will be a necessary step for stemming biodiversity loss in the Anthropocene.

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small “many analyst” study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future.

The widespread use of species traits in basic and applied ecology, conservation and biogeography has led to an exponential increase in functional diversity analyses, with > 10 000 papers published in 2010–2020, and > 1800 papers only in 2021. This interest is reflected in the development of a multitude of theoretical and methodological frameworks for calculating functional diversity, making it challenging to navigate the myriads of options and to report detailed accounts of trait‐based analyses. Therefore, the discipline of trait‐based ecology would benefit from the existence of a general guideline for standard reporting and good practices for analyses. We devise an eight‐step protocol to guide researchers in conducting and reporting functional diversity analyses, with the overarching goal of increasing reproducibility, transparency and comparability across studies. The protocol is based on: 1) identification of a research question; 2) a sampling scheme and a study design; 3–4) assemblage of data matrices; 5) data exploration and preprocessing; 6) functional diversity computation; 7) model fitting, evaluation and interpretation; and 8) data, metadata and code provision. Throughout the protocol, we provide information on how to best select research questions, study designs, trait data, compute functional diversity, interpret results and discuss ways to ensure reproducibility in reporting results. To facilitate the implementation of this template, we further develop an interactive web‐based application ( stepFD ) in the form of a checklist workflow, detailing all the steps of the protocol and allowing the user to produce a final ‘reproducibility report' to upload alongside the published paper. A thorough and transparent reporting of functional diversity analyses ensures that ecologists can incorporate others' findings into meta‐analyses, the shared data can be integrated into larger databases for consensus analyses, and available code can be reused by other researchers. All these elements are key to pushing forward this vibrant and fast‐growing field of research.

Recent international agreements have strengthened and expanded commitments to protect and restore native habitats for biodiversity protection (“area‐based biodiversity conservation”). Nevertheless, biodiversity conservation is hindered because how such commitments should be implemented has been strongly debated, which can lead to suboptimal habitat protection decisions. We argue that, despite the debates, there are three essential principles for area‐based biodiversity conservation. These principles are related to habitat geographic coverage, amount, and connectivity. They emerge from evidence that, while large areas of nature are important and must be protected, conservation or restoration of multiple small habitat patches is also critical for global conservation, particularly in regions with high land use. We contend that the many area‐based conservation initiatives expected in the coming decades should follow the principles we identify, regardless of ongoing debates. Considering the importance of biodiversity for maintenance of ecosystem services, we suggest that this would bring widespread societal benefits.

Ecological systems are quintessentially complex systems. Understanding and being able to predict phenomena typical of complex systems is, therefore, critical to progress in ecology and conservation amidst escalating global environmental change. However, myriad definitions of complexity and excessive reliance on conventional scientific approaches hamper conceptual advances and synthesis. Ecological complexity may be better understood by following the solid theoretical basis of complex system science (CSS). We review features of ecological systems described within CSS and conduct bibliometric and text mining analyses to characterize articles that refer to ecological complexity. Our analyses demonstrate that the study of complexity in ecology is a highly heterogeneous, global endeavor that is only weakly related to CSS. Current research trends are typically organized around basic theory, scaling, and macroecology. We leverage our review and the generalities identified in our analyses to suggest a more coherent and cohesive way forward in the study of complexity in ecology.