Implications of scale dependence for cross-study syntheses of biodiversity differences

Biodiversity increases resistance of grasslands against plant invasions under multiple environmental changes

Biodiversity often helps communities resist invasion. However, it is unclear whether this diversity-invasion relationship holds true under environmental changes. Here, we conduct a meta-analysis of 1010 observations from 25 grassland studies in which plant species richness is manipulated together with one or more environmental change factors to test invasibility (measured by biomass or cover of invaders). We find that biodiversity increases resistance to invaders across various environmental conditions. However, the positive biodiversity effect on invasion resistance is strengthened under experimental warming, whereas it is weakened under experimentally imposed drought. When multiple factors are imposed simultaneously, the positive biodiversity effect is strengthened. Overall, we show that biodiversity helps grassland communities resist plant invasions under multiple environmental changes. Therefore, investment in the protection and restoration of native biodiversity is not only important for prevention of invasions under current conditions but also under continued global environmental change.

Meta-analysis reveals that the effects of precipitation change on soil and litter fauna in forests depend on body size

Anthropogenic climate change is altering precipitation regimes at a global scale. While precipitation changes have been linked to changes in the abundance and diversity of soil and litter invertebrate fauna in forests, general trends have remained elusive due to mixed results from primary studies. We used a meta-analysis based on 430 comparisons from 38 primary studies to address associated knowledge gaps, (i) quantifying impacts of precipitation change on forest soil and litter fauna abundance and diversity, (ii) exploring reasons for variation in impacts and (iii) examining biases affecting the realism and accuracy of experimental studies. Precipitation reductions led to a decrease of 39% in soil and litter fauna abundance, with a 35% increase in abundance under precipitation increases, while diversity impacts were smaller. A statistical model containing an interaction between body size and the magnitude of precipitation change showed that mesofauna (e.g. mites, collembola) responded most to changes in precipitation. Changes in taxonomic richness were related solely to the magnitude of precipitation change. Our results suggest that body size is related to the ability of a taxon to survive under drought conditions, or to benefit from high precipitation. We also found that most experiments manipulated precipitation in a way that aligns better with predicted extreme climatic events than with predicted average annual changes in precipitation and that the experimental plots used in experiments were likely too small to accurately capture changes for mobile taxa. The relationship between body size and response to precipitation found here has far-reaching implications for our ability to predict future responses of soil biodiversity to climate change and will help to produce more realistic mechanistic soil models which aim to simulate the responses of soils to global change.

Testing the hierarchy of predictability in grassland restoration across a gradient of environmental severity

Ecological restoration is critical for recovering degraded ecosystems but is challenged by variable success and low predictability. Understanding which outcomes are more predictable and less variable following restoration can improve restoration effectiveness. Recent theory asserts that the predictability of outcomes would follow an order from most to least predictable from coarse to fine community properties (physical structure > taxonomic diversity > functional composition > taxonomic composition) and that predictability would increase with more severe environmental conditions constraining species establishment. We tested this "hierarchy of predictability" hypothesis by synthesizing outcomes along an aridity gradient with 11 grassland restoration projects across the United States. We used 1829 vegetation monitoring plots from 227 restoration treatments, spread across 52 sites. We fit generalized linear mixed-effects models to predict six indicators of restoration outcomes as a function of restoration characteristics (i.e., seed mixes, disturbance, management actions, time since restoration) and used variance explained by models and model residuals as proxies for restoration predictability. We did not find consistent support for our hypotheses. Physical structure was among the most predictable outcomes when the response variable was relative abundance of grasses, but unpredictable for total canopy cover. Similarly, one dimension of taxonomic composition related to species identities was unpredictable, but another dimension of taxonomic composition indicating whether exotic or native species dominated the community was highly predictable. Taxonomic diversity (i.e., species richness) and functional composition (i.e., mean trait values) were intermittently predictable. Predictability also did not increase consistently with aridity. The dimension of taxonomic composition related to the identity of species in restored communities was more predictable (i.e., smaller residuals) in more arid sites, but functional composition was less predictable (i.e., larger residuals), and other outcomes showed no significant trend. Restoration outcomes were most predictable when they related to variation in dominant species, while those responding to rare species were harder to predict, indicating a potential role of scale in restoration predictability. Overall, our results highlight additional factors that might influence restoration predictability and add support to the importance of continuous monitoring and active management beyond one-time seed addition for successful grassland restoration in the United States.

Understanding 'it depends' in ecology: a guide to hypothesising, visualising and interpreting statistical interactions

Ecologists routinely use statistical models to detect and explain interactions among ecological drivers, with a goal to evaluate whether an effect of interest changes in sign or magnitude in different contexts. Two fundamental properties of interactions are often overlooked during the process of hypothesising, visualising and interpreting interactions between drivers: the measurement scale - whether a response is analysed on an additive or multiplicative scale, such as a ratio or logarithmic scale; and the symmetry - whether dependencies are considered in both directions. Overlooking these properties can lead to one or more of three inferential errors: misinterpretation of (i) the detection and magnitude (Type-D error), and (ii) the sign of effect modification (Type-S error); and (iii) misidentification of the underlying processes (Type-A error). We illustrate each of these errors with a broad range of ecological questions applied to empirical and simulated data sets. We demonstrate how meta-analysis, a widely used approach that seeks explicitly to characterise context dependence, is especially prone to all three errors. Based on these insights, we propose guidelines to improve hypothesis generation, testing, visualisation and interpretation of interactions in ecology.

Advanced methods and implementations for the meta-analyses of animal models: Current practices and future recommendations

Meta-analytic techniques have been widely used to synthesize data from animal models of human diseases and conditions, but these analyses often face two statistical challenges due to complex nature of animal data (e.g., multiple effect sizes and multiple species): statistical dependency and confounding heterogeneity. These challenges can lead to unreliable and less informative evidence, which hinders the translation of findings from animal to human studies. We present a literature survey of meta-analysis using animal models (animal meta-analysis), showing that these issues are not adequately addressed in current practice. To address these challenges, we propose a meta-analytic framework based on multilevel (linear mixed-effects) models. Through conceptualization, formulations, and worked examples, we illustrate how this framework can appropriately address these issues while allowing for testing new questions. Additionally, we introduce other advanced techniques such as multivariate models, robust variance estimation, and meta-analysis of emergent effect sizes, which can deliver robust inferences and novel biological insights. We also provide a tutorial with annotated R code to demonstrate the implementation of these techniques.

A robust and readily implementable method for the meta-analysis of response ratios with and without missing standard deviations

The log response ratio, lnRR, is the most frequently used effect size statistic for meta-analysis in ecology. However, often missing standard deviations (SDs) prevent estimation of the sampling variance of lnRR. We propose new methods to deal with missing SDs via a weighted average coefficient of variation (CV) estimated from studies in the dataset that do report SDs. Across a suite of simulated conditions, we find that using the average CV to estimate sampling variances for all observations, regardless of missingness, performs with minimal bias. Surprisingly, even with missing SDs, this simple method outperforms the conventional approach (basing each effect size on its individual study-specific CV) with complete data. This is because the conventional method ultimately yields less precise estimates of the sampling variances than using the pooled CV from multiple studies. Our approach is broadly applicable and can be implemented in all meta-analyses of lnRR, regardless of 'missingness'.

Broad-scale climate variation drives the dynamics of animal populations: a global multi-taxa analysis

Climate is a major extrinsic factor affecting the population dynamics of many organisms. The Broad-Scale Climate Hypothesis (BSCH) was proposed by Elton to explain the large-scale synchronous population cycles of animals, but the extent of support and whether it differs among taxa and geographical regions is unclear. We reviewed publications examining the relationship between the population dynamics of multiple taxa worldwide and the two most commonly used broad-scale climate indices, El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). Our review and synthesis (based on 561 species from 221 papers) reveals that population changes of mammals, birds and insects are strongly affected by major oceanic shifts or irregular oceanic changes, particularly in ENSO- and NAO-influenced regions (Pacific and Atlantic, respectively), providing clear evidence supporting Elton's BSCH. Mammal and insect populations tended to increase during positive ENSO phases. Bird populations tended to increase in positive NAO phases. Some species showed dual associations with both positive and negative phases of the same climate index (ENSO or NAO). These findings indicate that some taxa or regions are more or less vulnerable to climate fluctuations and that some geographical areas show multiple weather effects related to ENSO or NAO phases. Beyond confirming that animal populations are influenced by broad-scale climate variation, we document extensive patterns of variation among taxa and observe that the direct biotic and abiotic mechanisms for these broad-scale climate factors affecting animal populations are very poorly understood. A practical implication of our research is that changes in ENSO or NAO can be used as early signals for pest management and wildlife conservation. We advocate integrative studies at both broad and local scales to unravel the omnipresent effects of climate on animal populations to help address the challenge of conserving biodiversity in this era of accelerated climate change.

Biogeographic Patterns and Elevational Differentiation of Sedimentary Bacterial Communities across River Systems in China

Bacterial biodiversity is tightly correlated with ecological functions of natural systems, and bacterial rare and abundant subcommunities make distinct contributions to ecosystem functioning. However, the biogeographic pattern and elevational differentiation of sedimentary bacterial diversity have rarely been studied in cross-river systems at a continental scale. This study analyzed the biogeographic patterns and elevational differentiations of the entire, abundant, and rare bacterial (sub)communities as well as the underlying mechanisms across nine rivers that span distinct geographic regions and large elevational gradients in China. We found that bacterial rare and abundant subcommunities shared similar biogeographic patterns and both demonstrated strong distance-decay relationships, despite their distinct community compositions. However, both null model and variation partitioning analysis results showed that while environmental selection governed rare subcommunity assemblies (contribution: 51.9%), dispersal limitation (62.7%) controlled the assembly of abundant subcommunities. The disparity was associated with the broader threshold width of abundant taxa to water temperature and pH variations than rare taxa. Elevation-induced bacterial composition variations were more evident than latitude-induced ones. Some specific operational taxonomic units (OTUs), representing 16.4% of the total sequences, much preferentially and even exclusively lived in high-elevation or low-elevation habitats and demonstrated some adaptations to local conditions. Greater positive: negative link ratios in bacterial co-occurrence networks of low elevations than high elevations (P < 0.05) partly resulted from their harboring higher organic carbon: nitrogen ratios. Together, this study draws a biogeographic picture of sedimentary bacterial communities in a continental-scale riverine system and highlights the importance of incorporating elevation-associated patterns of microbial diversity into riverine microbial ecology studies. IMPORTANCE Bacterial diversity is tightly correlated with the nutrient cycling of river systems. However, previous studies on bacterial diversity are mainly constrained to one single river system, although microbial biogeography and its drivers exhibit strong spatial scale dependence. Moreover, elevational differentiations of bacterial communities across river systems have also rarely been studied. Bacterial rare and abundant subcommunities make distinct contributions to ecosystem functioning, and they share similar biogeographic patterns in some environments but not in others. Therefore, we explored the biogeography of the entire, abundant, and rare (sub)communities in nine rivers that cover a wide space range and large elevational gradient in China. Our results revealed that bacterial rare and abundant subcommunities shared similar biogeographic patterns but their assembly mechanisms were much different in these rivers. Moreover, bacterial communities showed evident differentiations between high elevations and low elevations. These findings will facilitate a better understanding of bacterial diversity features in river systems.

The latitudinal gradient in plant community assembly processes: A meta-analysis

Beta(β)-diversity, or site-to-site variation in species composition, generally decreases with increasing latitude, and the underlying processes driving this pattern have been challenging to elucidate because the signals of community assembly processes are scale-dependent. In this meta-analysis, by synthesising the results of 103 studies that were distributed globally and conducted at various spatial scales, we revealed a latitudinal gradient in the detectable assembly processes of vascular plant communities. Variations in plant community composition at low and high latitudes were mainly explained by geographic variables, suggesting that distance decay and dispersal limitations causing spatial aggregation are influential in these regions. In contrast, variation in species composition correlated most strongly with environmental variables at mid-latitudes (20-30°), reflecting the importance of environmental filtering, although this unimodal pattern was not statistically significant. Importantly, our analysis revealed the effects of different spatial scales, such that the correlation with spatial variables was stronger at smaller sampling extents, and environmental variables were more influential at larger sampling extents. We concluded that plant communities are driven by different community assembly processes in distinct biogeographical regions, suggesting that the latitudinal gradient of biodiversity is created by a combination of multiple processes that vary with environmental and species size differences.

Terrestrial ecosystem restoration increases biodiversity and reduces its variability, but not to reference levels: A global meta-analysis

Ecological restoration projects often have variable and unpredictable outcomes, and these can limit the overall impact on biodiversity. Previous syntheses have investigated restoration effectiveness by comparing average restored conditions to average conditions in unrestored or reference systems. Here, we provide the first quantification of the extent to which restoration affects both the mean and variability of biodiversity outcomes, through a global meta-analysis of 83 terrestrial restoration studies. We found that, relative to unrestored (degraded) sites, restoration actions increased biodiversity by an average of 20%, while decreasing the variability of biodiversity (quantified by the coefficient of variation) by an average of 14%. As restorations aged, mean biodiversity increased and variability decreased relative to unrestored sites. However, restoration sites remained, on average, 13% below the biodiversity of reference (target) ecosystems, and were characterised by higher (20%) variability. The lower mean and higher variability in biodiversity at restored sites relative to reference sites remained consistent over time, suggesting that sources of variation (e.g. prior land use, restoration practices) have an enduring influence on restoration outcomes. Our results point to the need for new research confronting the causes of variability in restoration outcomes, and close variability and biodiversity gaps between restored and reference conditions.

Removing climbers more than doubles tree growth and biomass in degraded tropical forests

Huge areas of tropical forests are degraded, reducing their biodiversity, carbon, and timber value. The recovery of these degraded forests can be significantly inhibited by climbing plants such as lianas. Removal of super‐abundant climbers thus represents a restoration action with huge potential for application across the tropics. While experimental studies largely report positive impacts of climber removal on tree growth and biomass accumulation, the efficacy of climber removal varies widely, with high uncertainty as to where and how to apply the technique. Using meta‐analytic techniques, we synthesize results from 26 studies to quantify the efficacy of climber removal for promoting tree growth and biomass accumulation. We find that climber removal increases tree growth by 156% and biomass accumulation by 209% compared to untreated forest, and that efficacy remains for at least 19 years. Extrapolating from these results, climber removal could sequester an additional 32 Gigatons of CO₂ over 10 years, at low cost, across regrowth, and production forests. Our analysis also revealed that climber removal studies are concentrated in the Neotropics (N = 22), relative to Africa (N = 2) and Asia (N = 2), preventing our study from assessing the influence of region on removal efficacy. While we found some evidence that enhancement of tree growth and AGB accumulation varies across disturbance context and removal method, but not across climate, the number and geographical distribution of studies limits the strength of these conclusions. Climber removal could contribute significantly to reducing global carbon emissions and enhancing the timber and biomass stocks of degraded forests, ultimately protecting them from conversion. However, we urgently need to assess the efficacy of removal outside the Neotropics, and consider the potential negative consequences of climber removal under drought conditions and for biodiversity.

Low statistical power and overestimated anthropogenic impacts, exacerbated by publication bias, dominate field studies in global change biology

Field studies are essential to reliably quantify ecological responses to global change because they are exposed to realistic climate manipulations. Yet such studies are limited in replicates, resulting in less power and, therefore, potentially unreliable effect estimates. Furthermore, while manipulative field experiments are assumed to be more powerful than non-manipulative observations, it has rarely been scrutinized using extensive data. Here, using 3847 field experiments that were designed to estimate the effect of environmental stressors on ecosystems, we systematically quantified their statistical power and magnitude (Type M) and sign (Type S) errors. Our investigations focused upon the reliability of field experiments to assess the effect of stressors on both ecosystem's response magnitude and variability. When controlling for publication bias, single experiments were underpowered to detect response magnitude (median power: 18%-38% depending on effect sizes). Single experiments also had much lower power to detect response variability (6%-12% depending on effect sizes) than response magnitude. Such underpowered studies could exaggerate estimates of response magnitude by 2-3 times (Type M errors) and variability by 4-10 times. Type S errors were comparatively rare. These observations indicate that low power, coupled with publication bias, inflates the estimates of anthropogenic impacts. Importantly, we found that meta-analyses largely mitigated the issues of low power and exaggerated effect size estimates. Rather surprisingly, manipulative experiments and non-manipulative observations had very similar results in terms of their power, Type M and S errors. Therefore, the previous assumption about the superiority of manipulative experiments in terms of power is overstated. These results call for highly powered field studies to reliably inform theory building and policymaking, via more collaboration and team science, and large-scale ecosystem facilities. Future studies also require transparent reporting and open science practices to approach reproducible and reliable empirical work and evidence synthesis.

Addressing context dependence in ecology

Context dependence is widely invoked to explain disparate results in ecology. It arises when the magnitude or sign of a relationship varies due to the conditions under which it is observed. Such variation, especially when unexplained, can lead to spurious or seemingly contradictory conclusions, which can limit understanding and our ability to transfer findings across studies, space, and time. Using examples from biological invasions, we identify two types of context dependence resulting from four sources: mechanistic context dependence arises from interaction effects; and apparent context dependence can arise from the presence of confounding factors, problems of statistical inference, and methodological differences among studies. Addressing context dependence is a critical challenge in ecology, essential for increased understanding and prediction.

A synthesis of land use impacts on stream biodiversity across metrics and scales

While land use intensification is a major driver of biodiversity change in streams, the nature of such changes, and at which scales they occur, have not been synthesized. To synthesize how land use change has altered multiple components of stream biodiversity across scales, we compiled data from 37 studies where comparative data were available for species' total and relative abundances from multiple locations including reference (less impacted) streams to those surrounded by different land use types (urban, forestry, and agriculture). We found that each type of land use reduced multiple components of within-stream biodiversity across scales, but that urbanization consistently had the strongest effects. However, we found that β-diversity among streams in modified landscapes did not differ from β-diversity observed among reference streams, suggesting little evidence for biotic homogenization. Nevertheless, assemblage composition did experience considerable species turnover between reference and modified streams. Our results emphasize that to understand how anthropogenic factors such as land use alter biodiversity, multiple components of biodiversity within and among sites must be simultaneously considered at multiple scales.

Responses of plant diversity to precipitation change are strongest at local spatial scales and in drylands

Mitigating and adapting to climate change requires an understanding of the magnitude and nature by which climate change will influence the diversity of plants across the world’s ecosystems. Experiments can causally link precipitation change to plant diversity change, however, these experiments vary in their methods and in the diversity metrics reported, making synthesis elusive. Here, we explicitly account for a number of potentially confounding variables, including spatial grain, treatment magnitude and direction and background climatic conditions, to synthesize data across 72 precipitation manipulation experiments. We find that the effects of treatments with higher magnitude of precipitation manipulation on plant diversity are strongest at the smallest spatial scale, and in drier environments. Our synthesis emphasizes that quantifying differential responses of ecosystems requires explicit consideration of spatial grain and the magnitude of experimental manipulation. Given that diversity provides essential ecosystem services, especially in dry and semi-dry areas, our finding that these dry ecosystems are particular sensitive to projected changes in precipitation has important implications for their conservation and management.

The responses of terrestrial ecosystems to changes in precipitation patterns are highly context-dependent. Here the authors perform a quantitative synthesis of field rainfall manipulation experiments, showing stronger effects of precipitation on plant diversity at small spatial scales and in arid biomes.

Disentangling Environmental Effects on the Tree Species Abundance Distribution and Richness in a Subtropical Forest

As a transitional vegetation type between evergreen broadleaved forest and deciduous broadleaved forest, evergreen-deciduous broadleaved mixed forest is composed of diverse plant species. This distinctive forest is generally distributed in mountainous areas with complex landforms and heterogeneous microenvironments. However, little is known about the roles of environmental conditions in driving the species diversity patterns of this forest. Here, based on a 15-ha plot in central China, we aimed to understand how and to what extent topographical characteristics and soil nutrients regulate the number and relative abundance of tree species in this forest. We measured environmental factors (terrain convexity, slope, soil total nitrogen, and phosphorus concentrations) and species diversity (species abundance distribution and species richness) in 20 m × 20 m subplots. Species abundance distribution was characterized by skewness, Berger-Parker index, and the proportion of singletons. The generalized additive model was used to examine the variations in diversity patterns caused by environmental factors. The structural equation model was used to assess whether and how topographical characteristics regulate species diversity via soil nutrients. We found that soil nutrients had significant negative effects on species richness and positive effects on all metrics of species abundance distribution. Convexity had significant positive effects on species richness and negative effects on all metrics of species abundance distribution, but these effects were mostly mediated by soil nutrients. Slope had significant negative effects on skewness and the Berger-Parker index, and these effects were almost independent of soil nutrients. Soil nutrients and topographical characteristics together accounted for 9.5-17.1% of variations in diversity patterns and, respectively, accounted for 8.9-13.9% and 3.3-10.7% of the variations. We concluded that soil nutrients were more important than topographical factors in regulating species diversity. Increased soil nutrient concentration led to decreased taxonomic diversity and increased species dominance and rarity. Convexity could be a better proxy for soil nutrients than slope. Moreover, these abiotic factors played limited roles in regulating diversity patterns, and it is possible that the observed patterns are also driven by some biotic and abiotic factors not considered here.

Novel Insights to Be Gained From Applying Metacommunity Theory to Long-Term, Spatially Replicated Biodiversity Data

Global loss of biodiversity and its associated ecosystem services is occurring at an alarming rate and is predicted to accelerate in the future. Metacommunity theory provides a framework to investigate multi-scale processes that drive change in biodiversity across space and time. Short-term ecological studies across space have progressed our understanding of biodiversity through a metacommunity lens, however, such snapshots in time have been limited in their ability to explain which processes, at which scales, generate observed spatial patterns. Temporal dynamics of metacommunities have been understudied, and large gaps in theory and empirical data have hindered progress in our understanding of underlying metacommunity processes that give rise to biodiversity patterns. Fortunately, we are at an important point in the history of ecology, where long-term studies with cross-scale spatial replication provide a means to gain a deeper understanding of the multiscale processes driving biodiversity patterns in time and space to inform metacommunity theory. The maturation of coordinated research and observation networks, such as the United States Long Term Ecological Research (LTER) program, provides an opportunity to advance explanation and prediction of biodiversity change with observational and experimental data at spatial and temporal scales greater than any single research group could accomplish. Synthesis of LTER network community datasets illustrates that long-term studies with spatial replication present an under-utilized resource for advancing spatio-temporal metacommunity research. We identify challenges towards synthesizing these data and present recommendations for addressing these challenges. We conclude with insights about how future monitoring efforts by coordinated research and observation networks could further the development of metacommunity theory and its applications aimed at improving conservation efforts.