Faculty Opinions recommendation of The experimental manipulation of atmospheric drought: Teasing out the role of microclimate in biodiversity experiments.

Review for "The Experimental Manipulation of Atmospheric Drought: Teasing Out the Role of Microclimate in Biodiversity Experiments"

Decision letter for "The Experimental Manipulation of Atmospheric Drought: Teasing Out the Role of Microclimate in Biodiversity Experiments"

Review for "The Experimental Manipulation of Atmospheric Drought: Teasing Out the Role of Microclimate in Biodiversity Experiments"

Decision letter for "The Experimental Manipulation of Atmospheric Drought: Teasing Out the Role of Microclimate in Biodiversity Experiments"

Drought occurrence is increasing due to anthropogenic climate change. Drought can negatively affect plants via reduced water below‐ground and increased evaporative demand or vapour pressure deficit (VPD) above‐ground. Past work has shown that plant diversity can ameliorate the negative effects of drought in plant communities, but these results are inconsistent between experimental and natural drought studies. Furthermore, while studies on the negative effects of reduced soil moisture on plant growth in drought experiments are abundant, the effects of predicted increases in atmospheric VPD have been neglected. We directly manipulated atmospheric relative humidity in a biodiversity and drought experiment at the California State University, Los Angeles (CA, USA) under three atmospheric conditions (ambient, dehumidified and humidified), two treatments of native perennial grass diversity (monoculture and eight species polyculture) and two soil drought treatments (control and drought). We assessed both polyculture plant community and individual species ( Poa secunda ) responses to atmospheric drought and soil drought. We found that soil drought only limits above‐ground biomass production when atmospheric conditions are also dry. We also found that P. secunda was limited by increased competition in polyculture when ambient atmospheric conditions were humid but was facilitated by diversity when atmospheric conditions were dry. Synthesis . Higher diversity ecosystems may be capable of protecting individual species from the negative effects of drought (facilitation). Without careful experimental manipulation of atmospheric drought, this important mechanism will be missed.

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.

Interactions between biotic and abiotic processes complicate the design and interpretation of ecological experiments. Separating causality from simple correlation requires distinguishing among experimental treatments, experimental responses, and the many processes and properties that are correlated with either the treatments or the responses, or both. When an experimental manipulation has multiple components, but only one of them is identified as the experimental treatment, erroneous conclusions about cause and effect relationships are likely because the actual cause of any observed response may be ignored in the interpretation of the experimental results. This unrecognized cause of an observed response can be considered a "hidden treatment." Three types of hidden treatments are potential problems in biodiversity experiments: (1) abiotic conditions, such as resource levels, or biotic conditions, such as predation, which are intentionally or unintentionally altered in order to create differences in species numbers for "diversity" treatments; (2) non-random selection of species with particular attributes that produce treatment differences that exceed those due to "diversity" alone; and (3) the increased statistical probability of including a species with a dominant negative or positive effect (e.g., dense shade, or nitrogen fixation) in randomly selected groups of species of increasing number or "diversity." In each of these cases, treatment responses that are actually the result of the "hidden treatment" may be inadvertently attributed to variation in species diversity. Case studies re-evaluating three different types of biodiversity experiments demonstrate that the increases found in such ecosystem properties as productivity, nutrient use efficiency, and stability (all of which were attributed to higher levels of species diversity) were actually caused by "hidden treatments" that altered plant biomass and productivity.

N-fixing tree species promote the chemical stability of soil organic carbon in subtropical plantations through increasing the relative contribution of plant-derived lipids

The causal relationship between the biodiversity of natural and modified environments and their net primary production has been a topic of significant scientific controversy and scrutiny. Early theoretical and empirical results indicated that production was sometimes significantly correlated with species richness when species richness was directly manipulated in experimental systems. Possible mechanisms for this phenomenon include statistical sampling effects, complementary resource use and mutualistic interactions. However, the interpretation of experimental results has sometimes confounded species richness with species composition, and disentangling the effects of species diversity from species identity has proved a formidable challenge. Here, I present a statistical method that is based on simple probability models and does not rely on the species composition of individual plots to distinguish among three phenomena that occur in biodiversity-production experiments: underyielding, overyielding and (a new concept) superyielding. In some cases, distinguishing these phenomena will provide evidence for underlying mechanisms. As a proof-of-concept, I first applied this technique to a simulated dataset, indicating the strengths of the method with both clear and ambiguous cases. I then analysed data from the BIODEPTH experimental biodiversity manipulations. No evidence of either overyielding or superyielding was detected in the BIODEPTH experiment.

We report data on leaf litter production and decomposition from a manipulative biodiversity experiment with trees in tropical Panama, which has been designed to explore the relationship between tree diversity and ecosystem functioning. A total of 24 plots (2025 m2) were established in 2001 using six native tree species, with 1‐, 3‐, and 6‐species mixtures. We estimated litter production during the dry season 2005 with litter traps; decomposition was assessed with a litter bag approach during the following wet season.Litter production during the course of the dry season was highly variable among the tree species. Tree diversity significantly affected litter production, and the majority of the intermediate diverse mixtures had higher litter yields than expected based on yields in monoculture. In contrast, high diverse mixtures did not show such overyielding in litter production. Litter decomposition rates were also highly species‐specific, and were related to various measures of litter quality (C/N, lignin/N, fibre content). We found no overall effect of litter diversity if the entire litter mixtures were analyzed, i.e. mixing species resulted in pure additive effects and observed decomposition rates were not different from expected rates. However, the individual species changed their decomposition pattern depending on the diversity of the litter mixture, i.e. there were species‐specific responses to mixing litter. The analysis of temporal C and N dynamics within litter mixtures gave only limited evidence for nutrient transfer among litters of different quality.At this early stage of our tree diversity experiment, there are no coherent and general effects of tree species richness on both litter production and decomposition. Within the scope of the biodiversity‐ecosystem functioning relationship, our results therefore highlight the process‐specific effects diversity may have. Additionally, species‐specific effects on ecosystem processes and their temporal dynamics are important, but such effects may change along the gradient of tree diversity.

Community genomics: a community-wide perspective on within-species genetic diversity.

Faculty Opinions recommendation of The experimental manipulation of atmospheric drought: Teasing out the role of microclimate in biodiversity experiments.

Author response for "The Experimental Manipulation of Atmospheric Drought: Teasing Out the Role of Microclimate in Biodiversity Experiments"

Faculty Opinions recommendation of The experimental manipulation of atmospheric drought: teasing out the role of microclimate in biodiversity experiments.

Species richness (SR) and phylogenetic diversity (PD) are highly correlated measures of plant diversity. Each, by itself, is significantly associated with plant community biomass in biodiversity experiments. As presented by Cadotte (2015) and as we present below, reasonable but alternative analyses that attempt to control for this correlation in different ways provide contradictory or inconclusive support for the hypothesis that PD is superior to SR as a predictor of community biomass. In Venail et al. (2015), we re-analysed data from 16 experimental manipulations of grassland SR to look at how SR and PD influence variation in plant community biomass through time. Using four types of analyses, we showed that, after statistically controlling for variation in SR, PD was not related to community biomass or to the temporal stability of biomass. We did, however, find that SR tends to increase the biomass production of plant communities after controlling for PD. In his comment, Cadotte expressed two concerns about our analyses. One is that we used non-random subsets of experiments, rather than the full data set, for some of our analyses (types 2, 3). We were clear in stating these analyses were based on non-random subsets that were specifically chosen to minimize the SR–PD correlation and avoid problems associated with multicollinearity. We acknowledge that our tests are conservative, a cost of which is that they sacrifice statistical power while, at the same time, minimizing the chance of drawing an incorrect conclusion. But we disagree with Cadotte's suggestion that our use of non-random data subsets led to 'biased' conclusions, and demonstrate later in this response that his claim of bias is unsubstantiated. Cadotte's second concern was that our analyses did not account for differences in biomass across studies. This is an important criticism to consider; we made a mistake by not controlling for variation in biomass. To address this issue, Cadotte used mixed models where study was included as a random effect, and ran analyses that standardized biomass among sites. Collectively, these led Cadotte to conclude 'All analyses strongly support previous literature claims about the value of PD and I further show that: (i) PD provides a more powerful explanation of variation in biomass production than species richness; (ii) PD explains variation in biomass production after controlling for richness; and (iii) the use of data subsets inadvertently biased the conclusions'. We have two concerns with Cadotte's re-analysis. First, Cadotte's approach largely ignores the concerns we raised about multicollinearity. When two or more predictors exhibit a high degree of correlation, each predictor contains little unique information. As a result, it is difficult (if not impossible) to estimate their independent effects using statistical methods like multivariate or partial regression (Dormann et al. 2013). The consequences of multicollinearity include inflated error estimates that can alter conclusions about what predictors are significant or not, as well as unstable parameter estimates that can change in sign and magnitude with minor alterations to analyses (Graham 2003; Zuur, Ieno & Elphick 2010). Multicollinearity is a concern for the data set of Venail et al. (2015) because PD and SR are correlated with r = 0·90. We were concerned about drawing inferences from predictors that have little unique information, which is why we performed analyses that all attempted to hold one of the two predictors constant while examining the impact of the other. In contrast, Cadotte performed model selection using the full data set where the SR–PD correlation was r = 0·90. We remain sceptical of this approach because of the difficulties generating reliable estimates for strongly correlated predictors. A second issue with Cadotte's analyses, which we are guilty of for some analyses in our study, is the assumption that the relationship between biodiversity (PD or SR) and community biomass is linear. Most studies included in the Venail et al. data set have shown that the effect of biodiversity on community biomass is positive, but nonlinear and decelerating. For example, Cardinale et al. (2011) summarized the form of diversity–biomass relationships for 433 experimental manipulations of primary producer richness and concluded 'Of the studies that have shown a positive effect of producer diversity on producer biomass, 79% were best fit by some form of a positive but decelerating curve (log, power, or M-M functions, Fig. 5A)'. In contrast, only 13% of studies to date are best fit by linear relationships. We reran Cadotte's analyses after accounting for nonlinear relationships and found that most of his conclusions did not hold. Our modified analyses (provided in accompanying R-code) rerun the same analyses of Cadotte, which account for variation in biomass among studies, but using ln-transformed predictors to also account for positive, decelerating relationships. Cadotte's first set of analyses modelled biomass in experimental plots as linear functions of SR and/or PD with study included as a random effect to account for differences in biomass among sites. These produced an AIC of 10 216 and 10 194 for SR and PD, respectively, and an AIC of 10 196 for a model including both SR and PD as predictors. In contrast, the best model in our modified analyses included both ln-transformed SR and PD with an AIC of 10 184. This represents an improved fit to data compared to Cadotte's analyses, and confirms that failure to account for nonlinear relationships led to inferior models. After confirming that relationships between PD, SR and community biomass are better described by nonlinear models, we reran Cadotte's partial regression analyses which found that PD explains a significant fraction of the residual variation in biomass after accounting for effects of SR (F = 4·09, P = 0·04), but SR did not explain residual variation after accounting for effects of PD (F = 0·09, P = 0·77). Using ln-transformed predictors where the SR–PD correlation was lower (r = 0·70), we found that ln(PD) explained 0·05% of the variation unaccounted for by ln(SR) (F = 3·79, P = 0·052, R2 = 0·005). Yet, ln(SR) explained 1·4% of the residual variation in community biomass unaccounted for by ln(PD) (F = 12, P < 0·01, R2 = 0·014). Cadotte also reran our structural equation model (SEM), but used the full data set where the PD–SR correlation was r = 0·90. He accounted for variation among studies by scaling biomass to have a mean = 0 and SD = 1. Cadotte's SEM (reproduced in Fig. 1a) shows that PD explains a significant fraction of variation in scaled biomass and SD through time. In contrast, SR did not explain variation in either. We reran the same SEM on the full data set, but using ln-transformed predictors to account for nonlinear relationships. The modified SEM was a significantly improved fit over the linear version (compare χ2, P-values and AIC for Fig. 1a,b) and led to conclusions that were consistent with those from our original paper (Venail et al. 2015) where we found SR impacts community biomass, but PD does not. In contrast, PD affects the SD of biomass through time, but SR does not. In his final analysis, Cadotte tried to assess whether the five experiments included in our SEM were a 'biased' representation of the full set of 16 experiments. He chose 1000 random subsets of five experiments and, for each subset, ran two mixed effects models – one modelling biomass as a function of PD and one modelling biomass as a function of SR. He then calculated the difference in AIC for the two models. If ΔAIC was <0 (>0), this indicated PD (SR) was a better predictor of biomass for that random subset. The frequency distribution of ΔAIC values (Fig. 3 of his comment) is reproduced in Fig. 1c. The mean of this distribution was significantly <0, suggesting PD is a better predictor of biomass than SR in most random subsets of five experiments. In addition, the subset of five experiments used for our SEM was different than the overall distribution, suggesting biased selection. But Cadotte's conclusions about the 'representativeness' of the five experiments are overturned when we repeat the same analyses using ln-transformed predictor variables. Indeed, the balance of evidence favoured ln(SR) as the better model (Fig. 1d) with the distribution of ΔAIC values being significantly >0 (mean = +5·64, t = 12·06, P < 0·01). The value of ΔAIC for the subset of five experiments used in our SEM is near the centre of the distribution, indicating it was not a biased subset. So where do we stand in this exchange? Cadotte, Cardinale & Oakley (2008) found that PD was not only a significant predictor of community biomass in grassland biodiversity experiments, it explained ~2% more variation than SR. We (Venail et al. 2015) suggested that synthesis did not control for multicollinearity among predictors. When we (Venail et al. 2015) controlled for multicollinearity (but failed to account for biomass differences among studies), we found PD was not a significant predictor of community biomass or stability, whereas SR was. Cadotte argued in his comment that our new analyses were incorrect because we did not account for variation in biomass among studies, and were biased by our use of data subsets to control for multicollinearity. Cadotte's re-analyses led him to conclude that PD is not only significant, but is again a better predictor of community biomass than SR. We responded by pointing out that multicollinearity continues to be a concern about Cadotte's analyses, and his conclusions do not hold after accounting for nonlinear relationships between biodiversity and ecosystem functioning. Whether using the statistical approaches from our original paper (Venail et al. 2015) or model selection favoured by Cadotte, we are led to two conclusions: (i) either SR or PD can explain most of the variation in community biomass and stability on their own because they share so much information. However, (ii) when we examine their effects after statistically controlling for the other, there is little evidence that PD is a better predictor of ecological function than SR. SR is usually a significant predictor of community biomass and stability after controlling for variation in PD, whereas PD is often (though not always) non-significant after controlling for variation in SR. We would caution against interpreting these results as evidence that PD does not matter for ecosystem functioning. Cadotte is correct that experiments analysed to date have not been explicitly designed to test hypotheses about PD, and therefore, we will need studies that orthogonally manipulate PD and SR to fully resolve their relative importance. On the other hand, given the existing data and analyses, we think it is important that researchers refrain from claiming that phylogenetic diversity is a 'strong' predictor of ecosystem functioning, or a 'better' predictor than plant richness in grasslands. 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