Variation in the methods leads to variation in the interpretation of biodiversity–ecosystem multifunctionality relationships

Aims Biodiversity is often positively related to the capacity of an ecosystem to provide multiple functions simultaneously (i.e. multifunctionality). However, there is some controversy over whether biodiversity–multifunctionality relationships depend on the number of functions considered. Particularly, investigators have documented contrasting findings that the effects of biodiversity on ecosystem multifunctionality do not change or increase with the number of ecosystem functions. Here, we provide some clarity on this issue by examining the statistical underpinnings of different multifunctionality metrics. Methods We used simulations and data from a variety of empirical studies conducted across spatial scales (from local to global) and biomes (temperate and alpine grasslands, forests and drylands). We revisited three methods to quantify multifunctionality including the averaging approach, summing approach and threshold-based approach. Important Findings Biodiversity–multifunctionality relationships either did not change or increased as more functions were considered. These results were best explained by the statistical underpinnings of the averaging and summing multifunctionality metrics. Specifically, by averaging the individual ecosystem functions, the biodiversity–multifunctionality relationships equal the population mean of biodiversity-single function relationships, and thus will not change with the number of functions. Likewise, by summing the individual ecosystem functions, the strength of biodiversity–multifunctionality relationships increases as the number of functions increased. We proposed a scaling standardization method by converting the averaging or summing metrics into a scaling metric, which would make comparisons among different biodiversity studies. In addition, we showed that the range-relevant standardization can be applied to the threshold-based approach by solving for the mathematical artefact of the approach (i.e. the effects of biodiversity may artificially increase with the number of functions considered). Our study highlights different approaches yield different results and that it is essential to develop an understanding of the statistical underpinnings of different approaches. The standardization methods provide a prospective way of comparing biodiversity–multifunctionality relationships across studies.This work was supported by the National Natural Science Foundation of China (31600428) to X.J. and a Semper Ardens grant from Carlsberg Foundation to N.J.S. F.T.M. and the global drylands dataset were supported by the European Research Council (ERC Grant Agreements 242658 [BIOCOM] and 647038 [BIODESERT])

A gradient of nutrient enrichment reveals nonlinear impacts of fertilization on Arctic plant diversity and ecosystem function

© The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecology and Evolution 7 (2017): 2449–2460, doi:10.1002/ece3.2863.Rapid environmental change at high latitudes is predicted to greatly alter the diversity, structure, and function of plant communities, resulting in changes in the pools and fluxes of nutrients. In Arctic tundra, increased nitrogen (N) and phosphorus (P) availability accompanying warming is known to impact plant diversity and ecosystem function; however, to date, most studies examining Arctic nutrient enrichment focus on the impact of relatively large (>25x estimated naturally occurring N enrichment) doses of nutrients on plant community composition and net primary productivity. To understand the impacts of Arctic nutrient enrichment, we examined plant community composition and the capacity for ecosystem function (net ecosystem exchange, ecosystem respiration, and gross primary production) across a gradient of experimental N and P addition expected to more closely approximate warming-induced fertilization. In addition, we compared our measured ecosystem CO2 flux data to a widely used Arctic ecosystem exchange model to investigate the ability to predict the capacity for CO2 exchange with nutrient addition. We observed declines in abundance-weighted plant diversity at low levels of nutrient enrichment, but species richness and the capacity for ecosystem carbon uptake did not change until the highest level of fertilization. When we compared our measured data to the model, we found that the model explained roughly 30%–50% of the variance in the observed data, depending on the flux variable, and the relationship weakened at high levels of enrichment. Our results suggest that while a relatively small amount of nutrient enrichment impacts plant diversity, only relatively large levels of fertilization—over an order of magnitude or more than warming-induced rates—significantly alter the capacity for tundra CO2 exchange. Overall, our findings highlight the value of measuring and modeling the impacts of a nutrient enrichment gradient, as warming-related nutrient availability may impact ecosystems differently than single-level fertilization experiments.NASA Terrestrial Ecology Grant Number: NNX12AK83G; National Science Foundation Division of Graduate Education Grant Number: DGE-11-4415

Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment: An example from the WaRM Network

A growing body of work examines the direct and indirect effects of climate change on ecosystems, typically by using manipulative experiments at a single site or performing meta-analyses across many independent experiments. However, results from single-site studies tend to have limited generality. Although meta-analytic approaches can help overcome this by exploring trends across sites, the inherent limitations in combining disparate datasets from independent approaches remain a major challenge. In this paper, we present a globally distributed experimental network that can be used to disentangle the direct and indirect effects of climate change. We discuss how natural gradients, experimental approaches, and statistical techniques can be combined to best inform predictions about responses to climate change, and we present a globally distributed experiment that utilizes natural environmental gradients to better understand long-term community and ecosystem responses to environmental change. The warming and (species) removal in mountains (WaRM) network employs experimental warming and plant species removals at high- and low-elevation sites in a factorial design to examine the combined and relative effects of climatic warming and the loss of dominant species on community structure and ecosystem function, both above- and belowground. The experimental design of the network allows for increasingly common statistical approaches to further elucidate the direct and indirect effects of warming. We argue that combining ecological observations and experiments along gradients is a powerful approach to make stronger predictions of how ecosystems will function in a warming world as species are lost, or gained, in local communities

Integrating natural gradients, experiments, and statistical modeling in a distributed network experiment: An example from the WaRM Network

A growing body of work examines the direct and indirect effects of climate change on ecosystems, typically by using manipulative experiments at a single site or performing meta-analyses across many independent experiments. However, results from single-site studies tend to have limited generality. Although meta-analytic approaches can help overcome this by exploring trends across sites, the inherent limitations in combining disparate datasets from independent approaches remain a major challenge. In this paper, we present a globally distributed experimental network that can be used to disentangle the direct and indirect effects of climate change. We discuss how natural gradients, experimental approaches, and statistical techniques can be combined to best inform predictions about responses to climate change, and we present a globally distributed experiment that utilizes natural environmental gradients to better understand long-term community and ecosystem responses to environmental change. The warming and (species) removal in mountains (WaRM) network employs experimental warming and plant species removals at high- and low-elevation sites in a factorial design to examine the combined and relative effects of climatic warming and the loss of dominant species on community structure and ecosystem function, both above- and belowground. The experimental design of the network allows for increasingly common statistical approaches to further elucidate the direct and indirect effects of warming. We argue that combining ecological observations and experiments along gradients is a powerful approach to make stronger predictions of how ecosystems will function in a warming world as species are lost, or gained, in local communities

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Trophic complexity alters the diversity–multifunctionality relationship in experimental grassland mesocosms

Plant diversity has a positive influence on the number of ecosystem functions maintained simultaneously by a community, or multifunctionality. While the presence of multiple trophic levels beyond plants, or trophic complexity, affects individual functions, the effect of trophic complexity on the diversity–multifunctionality relationship is less well known. To address this issue, we tested whether the independent or simultaneous manipulation of both plant diversity and trophic complexity impacted multifunctionality using a mesocosm experiment from Cedar Creek, Minnesota, USA. Our analyses revealed that neither plant diversity nor trophic complexity had significant effects on single functions, but trophic complexity altered the diversity–multifunctionality relationship in two key ways: It lowered the maximum strength of the diversity–multifunctionality effect, and it shifted the relationship between increasing diversity and multifunctionality from positive to negative at lower function thresholds. Our findings highlight the importance to account for interactions with higher trophic levels, as they can alter the biodiversity effect on multifunctionality.We used a manipulated grassland mesocosm experiment to test the effects of higher trophic levels on ecosystem multifunctionality. We find that the number and identity of trophic levels affect the jack‐of‐all‐trades relationship between biodiversity and ecosystem multifunctionality. Our findings have implications in refining predictions for ecosystem multifunctionality in the face of ongoing biodiversity loss.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/168308/1/ece37498.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/168308/2/ece37498_am.pd

Trophic complexity alters the diversity–multifunctionality relationship in experimental grassland mesocosms

Plant diversity has a positive influence on the number of ecosystem functions maintained simultaneously by a community, or multifunctionality. While the presence of multiple trophic levels beyond plants, or trophic complexity, affects individual functions, the effect of trophic complexity on the diversity–multifunctionality relationship is less well known. To address this issue, we tested whether the independent or simultaneous manipulation of both plant diversity and trophic complexity impacted multifunctionality using a mesocosm experiment from Cedar Creek, Minnesota, USA. Our analyses revealed that neither plant diversity nor trophic complexity had significant effects on single functions, but trophic complexity altered the diversity–multifunctionality relationship in two key ways: It lowered the maximum strength of the diversity–multifunctionality effect, and it shifted the relationship between increasing diversity and multifunctionality from positive to negative at lower function thresholds. Our findings highlight the importance to account for interactions with higher trophic levels, as they can alter the biodiversity effect on multifunctionality

The importance of rare species: a trait-based assessment of rare species contributions to functional diversity and possible ecosystem function in tall-grass prairies

The majority of species in ecosystems are rare, but the ecosystem consequences of losing rare species are poorly known. To understand how rare species may influence ecosystem functioning, this study quantifies the contribution of species based on their relative level of rarity to community functional diversity using a trait-based approach. Given that rarity can be defined in several different ways, we use four different definitions of rarity: abundance (mean and maximum), geographic range, and habitat specificity. We find that rarer species contribute to functional diversity when rarity is defined by maximum abundance, geographic range, and habitat specificity. However, rarer species are functionally redundant when rarity is defined by mean abundance. Furthermore, when using abundance-weighted analyses, we find that rare species typically contribute significantly less to functional diversity than common species due to their low abundances. These results suggest that rare species have the potential to play an important role in ecosystem functioning, either by offering novel contributions to functional diversity or via functional redundancy depending on how rare species are defined. Yet, these contributions are likely to be greatest if the abundance of rare species increases due to environmental change. We argue that given the paucity of data on rare species, understanding the contribution of rare species to community functional diversity is an important first step to understanding the potential role of rare species in ecosystem functioning