Future threats to biodiversity and pathways to their prevention.

Tens of thousands of species are threatened with extinction as a result of human activities. Here we explore how the extinction risks of terrestrial mammals and birds might change in the next 50 years. Future population growth and economic development are forecasted to impose unprecedented levels of extinction risk on many more species worldwide, especially the large mammals of tropical Africa, Asia and South America. Yet these threats are not inevitable. Proactive international efforts to increase crop yields, minimize land clearing and habitat fragmentation, and protect natural lands could increase food security in developing nations and preserve much of Earth's remaining biodiversity.

Impact of multiple small and persistent threats on extinction risk.

Many species may face multiple distinct and persistent drivers of extinction risk, yet theoretical and empirical studies tend to focus on the single largest driver. This means that existing approaches potentially underestimate and mischaracterize future risks to biodiversity. We synthesized existing knowledge on how multiple drivers of extinction can interact to influence a species' overall extinction probability in a probabilistic model of extinction risk that incorporated the impacts of multiple drivers of extinction risk, their interactions, and their accumulative effects through time. We then used this model framework to explore how different threats, interactions between them, and time trends may affect a species' overall extinction probability. Multiple small threats together had potential to pose a large cumulative extinction risk; for example, 10 individual threats posed a 1% extinction risk each and cumulatively posed a 9.7% total extinction risk. Interactions among drivers resulted in escalated risk in some cases, and persistent threats with a small (1%) extinction risk each decade ultimately posed large extinction risk over 100 (9.6% total extinction risk) to 200 years (18.2% total extinction risk). By estimating long-term extinction risk posed by several different factors and their interactions, this approach provides a framework to identify drivers of extinction risk that could be proactively targeted to help prevent species currently of least concern from becoming threatened with extinction.

Muchas especies pueden enfrentarse a múltiples impulsores distintivos y persistentes del riesgo de extinción, aunque los estudios teóricos y empíricos tienden a enfocarse en el impulsor más relevante. Esto significa que las estrategias existentes tienen el potencial de subestimar y caracterizar erróneamente los riesgos para la biodiversidad en el futuro. Sintetizamos el conocimiento existente sobre cómo los múltiples impulsores de la extinción pueden interactuar para influir sobre la probabilidad general de extinción de una especie en un modelo probabilístico del riesgo de extinción, el cual incorporó los impactos de los múltiples impulsores del riesgo de extinción, sus interacciones y sus efectos acumulativos a través del tiempo. Después usamos este modelo para explorar cómo las diferentes amenazas, las interacciones entre ellas y las tendencias temporales pueden afectar la probabilidad general de extinción de una especie. El conjunto de múltiples amenazas pequeñas tuvo el potencial de representar un gran riesgo de extinción acumulativo; por ejemplo, cada una de diez amenazas individuales representó 1% de riesgo de extinción, y acumuladas representaron un riesgo total de extinción de 9.7%. Las interacciones entre los impulsores resultaron en un riesgo escalado en algunos casos, y las amenazas persistentes con un riesgo pequeño (1%) de extinción durante cada década al final representaron un gran riesgo de extinción después de 100 (9.6% del riesgo total de extinción) y 200 años (18.2% del riesgo total de extinción). Mediante la estimación del riesgo de extinción a largo plazo que presentan los diferentes factores y sus interacciones, esta estrategia proporciona un marco para identificar los impulsores del riesgo de extinción que podrían focalizarse proactivamente para ayudar a prevenir que las especies que actualmente están en menor riesgo se conviertan en especies amenazadas.

Species loss due to nutrient addition increases with spatial scale in global grasslands.

The effects of altered nutrient supplies and herbivore density on species diversity vary with spatial scale, because coexistence mechanisms are scale dependent. This scale dependence may alter the shape of the species-area relationship (SAR), which can be described by changes in species richness (S) as a power function of the sample area (A): S = cAz , where c and z are constants. We analysed the effects of experimental manipulations of nutrient supply and herbivore density on species richness across a range of scales (0.01-75 m2 ) at 30 grasslands in 10 countries. We found that nutrient addition reduced the number of species that could co-occur locally, indicated by the SAR intercepts (log c), but did not affect the SAR slopes (z). As a result, proportional species loss due to nutrient enrichment was largely unchanged across sampling scales, whereas total species loss increased over threefold across our range of sampling scales.

Diversity-dependent soil acidification under nitrogen enrichment constrains biomass productivity.

In most plant communities, the net effect of nitrogen enrichment is an increase in plant productivity. However, nitrogen enrichment also has been shown to decrease species richness and to acidify soils, each of which may diminish the long-term impact of nutrient enrichment on productivity. Here we use a long-term (20 year) grassland plant diversity by nitrogen enrichment experiment in Minnesota, United States (a subexperiment within the BioCON experiment) to quantify the net impacts of nitrogen enrichment on productivity, including its potential indirect effects on productivity via changes in species richness and soil pH over an experimental diversity gradient. Overall, we found that nitrogen enrichment led to an immediate positive increment in productivity, but that this effect became nonsignificant over later years of the experiment, with the difference in productivity between fertilized and unfertilized plots decreasing in proportion to nitrogen addition-dependent declines in soil pH and losses of plant diversity. The net effect of nitrogen enrichment on productivity could have been 14.5% more on average over 20 years in monocultures if not for nitrogen-induced decreases in pH and about 28.5% more on average over 20 years in 16 species communities if not for nitrogen-induced species richness losses. Together, these results suggest that the positive effects of nutrient enrichment on biomass production can diminish in their magnitude over time, especially because of soil acidification in low diversity communities and especially because of plant diversity loss in initially high diversity communities.

Empirical evidence of widespread exaggeration bias and selective reporting in ecology.

In many scientific disciplines, common research practices have led to unreliable and exaggerated evidence about scientific phenomena. Here we describe some of these practices and quantify their pervasiveness in recent ecology publications in five popular journals. In an analysis of over 350 studies published between 2018 and 2020, we detect empirical evidence of exaggeration bias and selective reporting of statistically significant results. This evidence implies that the published effect sizes in ecology journals exaggerate the importance of the ecological relationships that they aim to quantify. An exaggerated evidence base hinders the ability of empirical ecology to reliably contribute to science, policy, and management. To increase the credibility of ecology research, we describe a set of actions that ecologists should take, including changes to scientific norms about what high-quality ecology looks like and expectations about what high-quality studies can deliver.

Clarifying the effect of biodiversity on productivity in natural ecosystems with longitudinal data and methods for causal inference.

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.

Exploring the impact of trait number and type on functional diversity metrics in real-world ecosystems.

The use of trait-based approaches to understand ecological communities has increased in the past two decades because of their promise to preserve more information about community structure than taxonomic methods and their potential to connect community responses to subsequent effects of ecosystem functioning. Though trait-based approaches are a powerful tool for describing ecological communities, many important properties of commonly-used trait metrics remain unexamined. Previous work in studies that simulate communities and trait distributions show consistent sensitivity of functional richness and evenness measures to the number of traits used to calculate them, but these relationships have yet to be studied in actual plant communities with a realistic distribution of trait values, ecologically meaningful covariation of traits, and a realistic number of traits available for analysis. Therefore, we propose to test how the number of traits used and the correlation between traits used in the calculation of functional diversity indices impacts the magnitude of eight functional diversity metrics in real plant communities. We will use trait data from three grassland plant communities in the US to assess the generality of our findings across ecosystems and experiments. We will determine how eight functional diversity metrics (functional richness, functional evenness, functional divergence, functional dispersion, kernel density estimation (KDE) richness, KDE evenness, KDE dispersion, Rao's Q) differ based on the number of traits used in the metric calculation and on the correlation of traits when holding the number of traits constant. Without a firm understanding of how a scientist's choices impact these metric, it will be difficult to compare results among studies with different metric parametrization and thus, limit robust conclusions about functional composition of communities across systems.

Causal assumptions and causal inference in ecological experiments.

Causal inferences from experimental data are often justified based on treatment randomization. However, inferring causality from data also requires complementary causal assumptions, which have been formalized by scholars of causality but not widely discussed in ecology. While ecologists have recognized challenges to inferring causal relationships in experiments and developed solutions, they lack a general framework to identify and address them. We review four assumptions required to infer causality from experiments and provide design-based and statistically based solutions for when these assumptions are violated. We conclude that there is no clear demarcation between experimental and non-experimental designs. This insight can help ecologists design better experiments and remove barriers between experimental and observational scholarship in ecology.