Equivalence and dissimilarity of ecosystem states

Measuring (dis)similarity between ecosystem states is a key theme in ecology. Much of community and ecosystem ecology is devoted to searching for patterns in ecosystem similarity from an external observer's viewpoint, using variables such as species abundances, measures of diversity and complexity. However, from the point of view of organisms in the ecosystem, proportional population growth rates are the only relevant aspect of ecosystem state, because natural selection acts on groups of organisms with different proportional population growth rates. We therefore argue that two ecosystem states are equivalent if and only if, for each species they contain, the proportional population growth rate does not differ between the states. Based on this result, we develop species-level and aggregated summary measures of ecosystem state and discuss their ecological meaning. We illustrate our approach using a long-term dataset on the plankton community from the Central European Lake Constance. We show that the first three principal components of proportional population growth rates describe most of the variation in ecosystem state in Lake Constance. We strongly recommend using proportional population growth rates and the derived equivalence classes for comparative ecosystem studies. This opens up new perspectives on important existing topics such as alternative stable ecosystem states, community assembly, and the processes generating regularities in ecosystems

Fishing-induced life-history changes degrade and destabilize harvested ecosystems

Fishing is widely known to magnify fluctuations in targeted populations. These fluctuations are correlated with population shifts towards young, small, and more quickly maturing individuals. However, the existence and nature of the mechanistic basis for these correlations and their potential ecosystem impacts remain highly uncertain. Here, we elucidate this basis and associated impacts by showing how fishing can increase fluctuations in fishes and their ecosystem, particularly when coupled with decreasing body sizes and advancing maturation characteristic of the life-history changes induced by fishing. More specifically, using an empirically parameterized network model of a well-studied lake ecosystem, we show how fishing may both increase fluctuations in fish abundances and also, when accompanied by decreasing body size of adults, further decrease fish abundance and increase temporal variability of fishes' food resources and their ecosystem. In contrast, advanced maturation has relatively little effect except to increase variability in juvenile populations. Our findings illustrate how different mechanisms underlying life-history changes that may arise as evolutionary responses to intensive, size-selective fishing can rapidly and continuously destabilize and degrade ecosystems even after fishing has ceased. This research helps better predict how life-history changes may reduce fishes' resilience to fishing and ecosystems' resistance to environmental variations.Peer reviewe

The integration of empirical, remote sensing and modelling approaches enhances insight in the role of biodiversity in climate change mitigation by tropical forests

Tropical forests store and sequester high amounts of carbon and are the most diverse terrestrial cosystem. Studies show potentially important effects of biodiversity on carbon storage and equestration, but a complete understanding of this relationship across spatiotemporal scales relevant for climate change mitigation needs three approaches: empirical, remote sensing and ecosystem modelling. Here, we review the contribution of these individual approaches to the understanding of the relationship of biodiversity with carbon storage and sequestration, and find short-term and long term benefits of biodiversity at both broad and fine spatial scales. We argue that enhanced understanding is obtained by combining approaches, i.e., by using output from one approach to improve another approach and thus results in better input, validation and comparison between approaches. This can be further improved by integrating approaches through using ‘boundary objects’(i.e., variables) that can be understood and measured by all approaches, such as the diversity of leaf traits of the upper canopy and forest structure indices. Combining and especially integrating approaches will therefore lead to a better understanding of biodiversity effects on climate change mitigation. This is crucial for making sound policy decisions

Modelling carbon stock and carbon sequestration ecosystem services for policy design: a comprehensive approach using a dynamic vegetation model

Ecosystem service (ES) models can only inform policy design adequately if they incorporate ecological processes. We used the Lund-Potsdam-Jena managed Land (LPJmL) model, to address following questions for Mexico, Bolivia and Brazilian Amazon: (i) How different are C stocks and C sequestration quantifications under standard (when soil and litter C and heterotrophic respiration are not considered) and comprehensive (including all C stock and heterotrophic respiration) approach? and (ii) How does the valuation of C stock and C sequestration differ in national payments for ES and global C funds or markets when comparing both approach? We found that up to 65% of C stocks have not been taken into account by neglecting to include C stored in soil and litter, resulting in gross underpayments (up to 500 times lower). Since emissions from heterotrophic respiration of organic material offset a large proportion of C gained through growth of living matter, we found that markets and decision-makers are inadvertently overestimating up to 100 times C sequestrated. New approaches for modelling C services relevant ecological process-based can help accounting for C in soil, litter and heterotrophic respiration and become important for the operationalization of agreements on climate change mitigation following the COP21 in 2015

More than a meal… integrating non-feeding interactions into food webs

Organisms eating each other are only one of many types of well documented and important interactions among species. Other such types include habitat modification, predator interference and facilitation. However, ecological network research has been typically limited to either pure food webs or to networks of only a few (<3) interaction types. The great diversity of non-trophic interactions observed in nature has been poorly addressed by ecologists and largely excluded from network theory. Herein, we propose a conceptual framework that organises this diversity into three main functional classes defined by how they modify specific parameters in a dynamic food web model. This approach provides a path forward for incorporating non-trophic interactions in traditional food web models and offers a new perspective on tackling ecological complexity that should stimulate both theoretical and empirical approaches to understanding the patterns and dynamics of diverse species interactions in nature.Keywords: Ecosystem engineering, Non-trophic interactions, Ecological network, Food web, Interaction modification, Facilitation, Trophic interaction

System-level indices used to test H1–H3.

<p>System-level indices used to test H1–H3.</p

Biomass (A–B) and production development (C–D) during succession.

<p>(A) Absolute biomass of the 7 major plankton groups in reference to concentrations of Soluble Reactive Phosphorus (SRP, dashed line, data from 1995, see Methods) and cellular levels of polyunsaturated fatty acids (PUFA, dotted line, avg. 2008–2009, see Methods) within the plankton of size fraction <140 μm in µg/l. (B) Relative biomass of all 20 planktonic guilds (cf. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090404#pone-0090404-t001" target="_blank">Table 1</a>). (C) Temporal course of the absolute and (D) relative production.</p

Functional diversity (A), succession rate (B), food quality (C), and system residence times (D).

<p>(A) Functional diversity within four major plankton groups: phytoplankton (Phy), ciliates (Cil), rotifers (Rot), and all crustaceans (HerbCru + CarnCru), and system functional diversity <i>H_bio</i> of all 20 plankton guilds (cf. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090404#pone-0090404-t001" target="_blank">Table 1</a>). Functional diversity of Rot is only shown when Rot biomass exceeded 1% of total biomass. (B) Succession rate <i>σ</i> of the 20 functional plankton guilds peaked twice shortly before and after the CWP. (C) C:P ratios of algal and bacterial biomass and food quality of the food ingested by different consumer groups (average across 1987–1993) in relation to phosphorus concentrations (SRP from 1995, dashed line) and cellular levels of polyunsaturated fatty acids (PUFA average 2008–2009, dotted line) within the sestonic size fraction <140 µm <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090404#pone.0090404-Hartwich1" target="_blank">[92]</a>. Food quality for herbivores decreased with increasing C:P ratios during succession. (D) System residence times for carbon (<i>SRT_C</i>) and phosphorus (<i>SRT_P</i>). <i>SRT_C</i> and <i>SRT_P</i> were maximal during the CWP due to the dominance of larger crustaceans with slower metabolism and in autumn-winter due to decreasing temperature and on average lower metabolic activity (Fig. 5D).</p

Seasonal changes in trophic structure.

<p>(A) The biomass pyramids of the grazing chain and (B) the detritus chain on ascending trophic levels for the 8 major functional groups in units of carbon. Summer and autumn data were pooled to summarize similar distributions. (C) The production pyramids of the grazing chain and d) the detritus chain. Autotrophic biomass and primary production (<i>PP</i>) in (A, C) and bacterial biomass and production (<i>BP</i>) in (B, D) was set to 100% in each phase. Without this standardization, the ratio between <i>PP</i> and <i>BP</i> is approximately 9:1 (cf. Fig. 1D). Seasons and groups in (B–D) same as in (A–B). The detritus chain only shows two trophic levels because consumers partly feeding on bacterivores were assigned to the grazing chain. Arrows indicate that fish biomass and production were underestimated because fish biomass is reduced by commercial fisheries in LC (cf. Methods).</p