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Abstract Causal effects of biodiversity on ecosystem functions can be estimated using experimental or observational designs — designs that pose a tradeoff between drawing credible causal inferences from correlations and drawing generalizable inferences. Here, we develop a design that reduces this tradeoff and revisits the question of how plant species diversity affects productivity. Our design leverages longitudinal data from 43 grasslands in 11 countries and approaches borrowed from fields outside of ecology to draw causal inferences from observational data. Contrary to many prior studies, we estimate that increases in plot-level species richness caused productivity to decline: a 10% increase in richness decreased productivity by 2.4%, 95% CI [−4.1, −0.74]. This contradiction stems from two sources. First, prior observational studies incompletely control for confounding factors. Second, most experiments plant fewer rare and non-native species than exist in nature. Although increases in native, dominant species increased productivity, increases in rare and non-native species decreased productivity, making the average effect negative in our study. By reducing the tradeoff between experimental and observational designs, our study demonstrates how observational studies can complement prior ecological experiments and inform future ones. 

Abiotic environmental change, local species extinctions and colonization of new species often co‐occur. Whether species colonization is driven by changes in abiotic conditions or reduced biotic resistance will affect community functional composition and ecosystem management. We use a grassland experiment to disentangle effects of climate warming and community diversity on plant species colonization. Community diversity had dramatic impacts on the biomass, richness and traits of plant colonists. Three times as many species colonized the monocultures than the high diversity 17 species communities (~30 vs. 10 species), and colonists collectively produced 10 times as much biomass in the monocultures than the high diversity communities (~30 vs. 3 g/m²). Colonists with resource‐acquisitive strategies (high specific leaf area, light seeds, short heights) accrued more biomass in low diversity communities, whereas species with conservative strategies accrued most biomass in high diversity communities. Communities with higher biomass of resident C4 grasses were more resistant to colonization by legume, nonlegume forb and C3 grass colonists, but not by C4 grass colonists. Compared with effects of diversity, 6 years of 3°C‐above‐ambient temperatures had little impact on plant colonization. Warmed subplots had ~3 fewer colonist species than ambient subplots and selected for heavier seeded colonists. They also showed diversity‐dependent changes in biomass of C3 grass colonists, which decreased under low diversity and increased under high diversity. Our findings suggest that species colonization is more strongly affected by biotic resistance from residents than 3°C of climate warming. If these results were extended to invasive species management, preserving community diversity should help limit plant invasion, even under climate warming.

 

Diversity and nitrogen addition have positive relationships with plant productivity, yet climate‐induced changes in water availability threaten to upend these established relationships. Using long‐term data from three experiments in a mesic grassland (ranging from 17 to 34 yr of data), we tested how the effects of species richness and nitrogen addition on community‐level plant productivity changed as a function of annual fluctuations in water availability using growing season precipitation and the Standardized Precipitation‐Evapotranspiration Index (SPEI). While results varied across experiments, our findings demonstrate that water availability can magnify the positive effects of both biodiversity and nitrogen addition on productivity. These results suggest that productivity responses to anthropogenic species diversity loss and increasing nitrogen deposition could depend on precipitation regimes, highlighting the importance of testing interactions between multiple global change drivers.

 

Feedbacks are an essential feature of resilient socio-economic systems, yet the feedbacks between biodiversity, ecosystem services and human wellbeing are not fully accounted for in global policy efforts that consider future scenarios for human activities and their consequences for nature. Failure to integrate feedbacks in our knowledge frameworks exacerbates uncertainty in future projections and potentially prevents us from realizing the full benefits of actions we can take to enhance sustainability. We identify six scientific research challenges that, if addressed, could allow future policy, conservation and monitoring efforts to quantitatively account for ecosystem and societal consequences of biodiversity change. Placing feedbacks prominently in our frameworks would lead to (i) coordinated observation of biodiversity change, ecosystem functions and human actions, (ii) joint experiment and observation programmes, (iii) more effective use of emerging technologies in biodiversity science and policy, and (iv) a more inclusive and integrated global community of biodiversity observers. To meet these challenges, we outline a five-point action plan for collaboration and connection among scientists and policymakers that emphasizes diversity, inclusion and open access. Efforts to protect biodiversity require the best possible scientific understanding of human activities, biodiversity trends, ecosystem functions and—critically—the feedbacks among them. 

Global biodiversity is declining at rates faster than at any other point in human history. Experimental manipulations at small spatial scales have demonstrated that communities with fewer species consistently produce less biomass than higher diversity communities. Understanding the consequences of the global extinction crisis for ecosystem functioning requires understanding how local experimental results are likely to change with increasing spatial and temporal scales and from experiments to naturally assembled systems.

Scaling across time and space in a changing world requires baseline predictions. Here, we provide a graphical null model for area scaling of biodiversity–ecosystem functioning relationships using observed macroecological patterns: the species–area curve and the biomass–area curve. We use species–area and biomass–area curves to predict how species richness–biomass relationships are likely to change with increasing sampling extent. We then validate these predictions with data from two naturally assembled ecosystems: a Minnesota savanna and a Panamanian tropical dry forest.

Our graphical null model predicts that biodiversity–ecosystem functioning relationships are scale‐dependent. However, we note two important caveats. First, our results indicate an apparent contradiction between predictions based on measurements in biodiversity–ecosystem functioning experiments and from scaling theory. When ecosystem functioning is measured as per unit area (e.g. biomass per m²), as is common in biodiversity–ecosystem functioning experiments, the slope of the biodiversity ecosystem functioning relationship should decrease with increasing scale. Alternatively, when ecosystem functioning is not measured per unit area (e.g. summed total biomass), as is common in scaling studies, the slope of the biodiversity–ecosystem functioning relationship should increase with increasing spatial scale. Second, the underlying macroecological patterns of biodiversity experiments are predictably different from some naturally assembled systems. These differences between the underlying patterns of experiments and naturally assembled systems may enable us to better understand when patterns from biodiversity–ecosystem functioning experiments will be valid in naturally assembled systems.

Synthesis. This paper provides a simple graphical null model that can be extended to any relationship between biodiversity and any ecosystem functioning across space or time. Furthermore, these predictions provide crucial insights into how and when we may be able to extend results from small‐scale biodiversity experiments to naturally assembled regional and global ecosystems where biodiversity is changing.