The last 15 years has seen parallel surges of interest in two research areas that have rarely intersected: biodiversity and ecosystem functioning (BEF), and multispecies predator–prey interactions ...(PPI). Research addressing role of biodiversity in ecosystem functioning has focused primarily on single trophic‐level systems, emphasizing additive effects of diversity that manifest through resource partitioning and the sampling effect. Conversely, research addressing predator–prey interactions has focused on two trophic‐level systems, emphasizing indirect and non‐additive interactions among species. Here, we use a suite of consumer‐resource models to organize and synthesize the ways in which consumer species diversity affects the densities of both resources and consumer species. Specifically, we consider sampling effects, resource partitioning, indirect effects caused by intraguild interactions and non‐additive effects. We show that the relationship between consumer diversity and the density of resources and consumer species are broadly similar for systems with one vs. two trophic levels, and that indirect and non‐additive interactions generally do little more than modify the impacts of diversity established by the sampling effect and resource partitioning. The broad similarities between systems with one vs. two trophic levels argue for greater communication between researchers studying BEF, and researchers studying multispecies PPI.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Plant functional traits often show strong latitudinal trends. To explain these trends, studies have often focused on environmental variables, correlations with other traits that themselves show ...latitudinal trends, and phylogenetic conservatism. However, few studies have systematically disentangled the relative contributions of these factors. Using a dataset consisting of 9,370 plant species from Southwest China, we investigated factors affecting fruit type (fleshy vs. dry): plant growth form, environmental constraints (summarized by climate region), and phylogenetic conservatism. Growth form and climate region are often cited in the literature as important explanations for the higher proportion of fleshy fruited species in the tropics. Nonetheless, in our analyses using partial R2, growth form and climate region explained only 1.7% and 0.3%, respectively, of the variance in fruit type in a model including phylogeny, while phylogenetic conservatism explained 79.5%. Furthermore, phylogenetic conservatism was evenly distributed along the phylogeny, implying that fruit type reflects both ancient and recent phylogenetic relationships. Our findings illustrate the value of parsing out the contributions of explanatory variables and phylogeny to the variance in species' traits. Methods using phylogenies that calculate partial R2 give a more informative tool than traditional methods to explore the phylogenetic patterns of functional traits.
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Of the several approaches that are used to analyse functional trait–environment relationships, the most popular is community‐weighted mean regressions (CWMr) in which species trait values are ...averaged at the site level and then regressed against environmental variables. Other approaches include model‐based methods and weighted correlations of different metrics of trait–environment associations, the best known of which is the fourth‐corner correlation method.
We investigated these three general statistical approaches for trait–environment associations: CWMr, five weighted correlation metrics (Peres‐Neto, Dray, & ter Braak, Ecography, 40, 806–816, 2017), and two multilevel models (MLM) using four different methods for computing p‐values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study.
CWMr gave highly significant associations for both traits, whereas the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios, implying that the significant results for the data could be spurious. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil, Ozinga, Kleyer, and ter Braak (Journal of Vegetation Science, 24, 988–1000, 2013) had both good type I error control and high power when an appropriate method was used to obtain p‐values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but had lower power.
There is no overall best method for identifying trait–environment associations. For the simple task of testing associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al., 2013). However, care must be taken to ensure against inflated type I errors for both weighted correlations and MLM2. Because CWMr exhibited highly inflated type I error rates, it should always be avoided.
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Ecological and evolutionary predictions are being increasingly employed to inform decision-makers confronted with intensifying pressures on biodiversity. For these efforts to effectively guide ...conservation actions, knowing the limit of predictability is pivotal. In this study, we provide realistic expectations for the enterprise of predicting changes in ecological and evolutionary observations through time. We begin with an intuitive explanation of predictability (the extent to which predictions are possible) employing an easy-to-use metric, predictive power PP ( t ). To illustrate the challenge of forecasting, we then show that among insects, birds, fishes and mammals, (i) 50% of the populations are predictable at most 1 year in advance and (ii) the median 1-year-ahead predictive power corresponds to a prediction R 2 of only 20%. Predictability is not an immutable property of ecological systems. For example, different harvesting strategies can impact the predictability of exploited populations to varying degrees. Moreover, incorporating explanatory variables, accounting for time trends and considering multivariate time series can enhance predictability. To effectively address the challenge of biodiversity loss, researchers and practitioners must be aware of the information within the available data that can be used for prediction and explore efficient ways to leverage this knowledge for environmental stewardship.
Interactions between multiple anthropogenic environmental changes can drive non-additive effects in ecological systems, and the non-additive effects can in turn be amplified or dampened by spatial ...covariation among environmental changes. We investigated the combined effects of night-time warming and light pollution on pea aphids and two predatory ladybeetle species. As expected, neither night-time warming nor light pollution changed the suppression of aphids by the ladybeetle species that forages effectively in darkness. However, for the more-visual predator, warming and light had non-additive effects in which together they caused much lower aphid abundances. These results are particularly relevant for agriculture near urban areas that experience both light pollution and warming from urban heat islands. Because warming and light pollution can have non-additive effects, predicting their possible combined consequences over broad spatial scales requires knowing how they co-occur. We found that night-time temperature change since 1949 covaried positively with light pollution, which has the potential to increase their non-additive effects on pea aphid control by 70% in US alfalfa. Our results highlight the importance of non-additive effects of multiple environmental factors on species and food webs, especially when these factors co-occur.
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The primary rationale for the use of phylogenetically based statistical methods is that phylogenetic signal, the tendency for related species to resemble each other, is ubiquitous. Whether this ...assertion is true for a given trait in a given lineage is an empirical question, but general tools for detecting and quantifying phylogenetic signal are inadequately developed. We present new methods for continuous‐valued characters that can be implemented with either phylogenetically independent contrasts or generalized least‐squares models. First, a simple randomization procedure allows one to test the null hypothesis of no pattern of similarity among relatives. The test demonstrates correct Type I error rate at a nominal α= 0.05 and good power (0.8) for simulated datasets with 20 or more species. Second, we derive a descriptive statistic, K, which allows valid comparisons of the amount of phylogenetic signal across traits and trees. Third, we provide two biologically motivated branch‐length transformations, one based on the Ornstein‐Uhlenbeck (OU) model of stabilizing selection, the other based on a new model in which character evolution can accelerate or decelerate (ACDC) in rate (e.g., as may occur during or after an adaptive radiation). Maximum likelihood estimation of the OU (d) and ACDC (g) parameters can serve as tests for phylogenetic signal because an estimate of d or g near zero implies that a phylogeny with little hierarchical structure (a star) offers a good fit to the data. Transformations that improve the fit of a tree to comparative data will increase power to detect phylogenetic signal and may also be preferable for further comparative analyses, such as of correlated character evolution. Application of the methods to data from the literature revealed that, for trees with 20 or more species, 92% of traits exhibited significant phylogenetic signal (randomization test), including behavioral and ecological ones that are thought to be relatively evolutionarily malleable (e.g., highly adaptive) and/or subject to relatively strong environmental (nongenetic) effects or high levels of measurement error. Irrespective of sample size, most traits (but not body size, on average) showed less signal than expected given the topology, branch lengths, and a Brownian motion model of evolution (i.e., K was less than one), which may be attributed to adaptation and/or measurement error in the broad sense (including errors in estimates of phenotypes, branch lengths, and topology). Analysis of variance of log K for all 121 traits (from 35 trees) indicated that behavioral traits exhibit lower signal than body size, morphological, life‐history, or physiological traits. In addition, physiological traits (corrected for body size) showed less signal than did body size itself. For trees with 20 or more species, the estimated OU (25% of traits) and/or ACDC (40%) transformation parameter differed significantly from both zero and unity, indicating that a hierarchical tree with less (or occasionally more) structure than the original better fit the data and so could be preferred for comparative analyses.
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Transgenic crops that express insecticide genes from Bacillus thuringiensis (Bt) are used worldwide against moth and beetle pests. Because these engineered plants can kill over 95% of susceptible ...larvae, they can rapidly select for resistance. Here, we use a model for a pyramid two-toxin Bt crop to explore the consequences of spatio-temporal variation in the area of Bt crop and non-Bt refuge habitat. We show that variability over time in the proportion of suitable non-Bt breeding habitat, Q, or in the total area of Bt and suitable non-Bt habitat, K, can increase the overall rate of resistance evolution by causing short-term surges of intense selection. These surges can be exacerbated when temporal variation in Q and/or K cause high larval densities in refuges that increase density-dependent mortality; this will give resistant larvae in Bt fields a relative advantage over susceptible larvae that largely depend on refuges. We address the effects of spatio-temporal variation in a management setting for two bollworm pests of cotton, Helicoverpa armigera and H. punctigera, and field data on landscape crop distributions from Australia. Even a small proportion of Bt fields available to egg-laying females when refuges are sparse may result in high exposure to Bt for just a single generation per year and cause a surge in selection. Therefore, rapid resistance evolution can occur when Bt crops are rare rather than common in the landscape. These results highlight the need to understand spatio-temporal fluctuations in the landscape composition of Bt crops and non-Bt habitats in order to design effective resistance management strategies.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Many consumers depend on the contemporaneous growth of their food resources. For example, Tanytarsus gracilentus midges feed on algae, and because midge generation time is much longer than that of ...algae, individual midges benefit not just from the standing stock but also from the growth of algae during their lifespans. This implies that an intermediate consumption rate maximizes midge somatic growth: low consumption rates constrain midge growth because they do not fully utilize the available food, whereas high consumption rates suppress algal biomass growth and consequently limit future food availability. An experiment manipulating midge presence and initial algal abundance showed that midges can suppress algal growth, as measured by changes in algal gross primary production (GPP). We also found a positive relationship between GPP and midge growth. A consumer–resource model fit to the experimental data showed a hump‐shaped relationship between midge consumption rates and their somatic growth. In the model, predicted midge somatic growth rates were only positively associated with GPP when their consumption rate was below the value that optimized midge growth. Therefore, midges did not overexploit algae in the experiment. This work highlights the balance that consumers which depend on contemporaneous resource growth might have to strike between short‐term growth and future food availability, and the benefits for consumers when they ‘manage' their resources well.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Understanding how nutrient limitation affects algal biomass and production is a long‐standing interest in aquatic ecology. Nutrients can influence these whole‐community characteristics through ...several mechanisms, including shifting community composition. Therefore, incorporating the joint responses of biomass, taxonomic composition, and production of algal communities, and relationships among them, is important for understanding effects of nutrient enrichment.
In shallow subarctic Lake Mývatn, Iceland, benthic algae compose a majority of whole‐lake primary production, support high secondary production, and influence nutrient cycling. Given the importance of these ecosystem processes, the factors that limit benthic algae have a large effect on the function and dynamics of the Mývatn system.
In a 33‐day nutrient enrichment experiment conducted in Lake Mývatn, we measured the joint responses of benthic algal biomass, primary production, and composition to nitrogen (N) and phosphorus (P) supplementation. We enriched N and P using nutrient‐diffusing agar overlain by sediment, with three levels of N and P that were crossed in a factorial design.
We found little evidence of community‐wide nutrient limitation, as chlorophyll‐a concentrations showed a negligible response to nutrients. Gross primary production (GPP) was unaffected by P and inhibited by N enrichment after 10 days, although the inhibitory effect of N diminished by day 33.
In contrast to biomass and primary production, community composition was strongly affected by N and marginally affected by P, with some algal groups increasing and others decreasing with enrichment. The taxa with the most negative and positive responses to N enrichment were Fragilariaceae and Scenedesmus, respectively.
The abundances of particular algal groups, based on standardised cell counts, were related to GPP measured at the end of the experiment. Oocystis was negatively associated with GPP but was unaffected by N or P, while Fragilariaceae and Scenedesmus were positively associated with GPP but had opposite responses to N. As a result, nutrient‐induced compositional shifts did not alter GPP.
Overall, our results show that nutrient enrichment can have large effects on algal community composition while having little effect on total biomass and primary production. Our study suggests that nutrient‐driven compositional shifts may not alter the overall ecological function of algal communities if (1) taxa have contrasting responses to nutrient enrichment but have similar effects on ecological processes, and/or (2) taxa that have strong influences on ecological function are not strongly affected by nutrients.
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Most biological control systems involve a diverse community of natural enemies. We investigated how specialist and generalist natural enemies differ as biological control agents of pea aphids ...(Acyrthosiphon pisum), and how interactions among natural enemies affect successful control. In alfalfa, pea aphids are attacked by a specialist parasitoid wasp, Aphidius ervi, and a guild of generalist predators primarily made up of Nabis and Orius bugs, coccinellid and carabid beetles, and web-building spiders. In three field experiments, we manipulated the parasitoid, then the generalist predator guild, and finally both classes of natural enemy, and recorded resulting impacts on pea aphid population control. The parasitoid caused little immediate reduction in aphid population growth but caused a large decline after a delay corresponding to the generation time of the parasitoid. In contrast, the generalist guild caused an immediate decline in the aphid population growth rate. However, the generalists did not exert density-dependent control, so aphid densities continued to increase throughout the experiment. The third field experiment in which we simultaneously manipulated parasitoids and predators investigated the possibility of "nonadditive effects" on aphid control. Densities of parasitoid pupae were 50% lower in the presence of generalist predators, indicating intraguild predation. Nonetheless, the ratio of parasitoids to aphids was not changed, and the impact of the two types of natural enemies was additive. We constructed a stage-structured model of aphid, parasitoid, and predator dynamics and fit the model to data from our field experiments. The model supports the additivity of parasitoid and predator effects on aphid suppression but suggests that longer-term experiments (32 d rather than 20 d) would likely reveal nonadditive effects as predation removes parasitoids whose response to aphid densities occurs with a delay. The model allowed us to explore additional factors that could influence the additivity of parasitoid and predator effects. Aphid density-dependent population growth and predator immigration in response to aphid density would likely have little influence on the additivity between parasitism and predation. However, if a parasitoid were to show a strong Type II functional response, in contrast to A. ervi whose functional response is nearly Type I, interactions with predators would likely be synergistic. These analyses reveal factors that should be investigated in other systems to address whether parasitism and predation act additively on host densities.
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BFBNIB, FZAB, GIS, IJS, INZLJ, KILJ, NLZOH, NMLJ, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZRSKP
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