Remotely sensed data can help to identify both suitable habitat for individual species, and environmental conditions that foster species richness, which is important when predicting how biodiversity ...will respond to global change. The question is how to summarize remotely sensed data so that they are most relevant for biodiversity analyses, and the Dynamic Habitat Indices are three metrics designed for this. Our goals here were to a) derive, for the first time, the Dynamic Habitat Indices (DHIs) globally, and b) use these to evaluate three hypotheses (available energy, environmental stress, and environmental stability) that attempt to explain global variation in species richness of amphibians, birds, and mammals. The three DHIs summarize three key measures of vegetative productivity: a) annual cumulative productivity, which we used to evaluate the available energy hypothesis that more energy is associate with higher species richness; b) minimum productivity throughout the year, which we used to evaluate the environmental stress hypothesis that higher minima cause higher species richness, and c) seasonality, expressed as the annual coefficient of variation in productivity, which we used to evaluate the environmental stability hypothesis that less intra-annual variability causes higher species richness. We calculated the DHIs globally at 1-km resolution from MODIS vegetation products (NDVI, EVI, LAI, fPAR, and GPP), based on the median of the good observations of all years from the entire MODIS record for each of the 23 or 46 possible dates (8- vs. 16-day composites) during the year, and calculated species richness for three taxa (amphibians, birds, and mammals) at 110-km resolution from species range maps from the IUCN Red List. We found marked global patterns of the DHIs, and strong support for all three hypotheses. The three DHIs for a given vegetation product were well correlated (Spearman rank correlations ranging from −0.6 (cumulative vs. variation DHIs) to −0.93 (variation vs. minimum DHI)). Similarly, DHI components derived from different MODIS vegetation products were well correlated (0.8–0.9), and correlations of the DHIs with temperature and precipitation were moderate and strong respectively. All three DHIs were well correlated with species richness, showing in ranked order positive correlations for cumulative DHI based on GPP (Spearman rank correlations of 0.75, 0.63, and 0.67 for amphibians, resident birds, and mammals respectively) and minimum DHI (0.73, 0.83, and 0.62), and negative for variation DHI (−0.69, −0.83, and −0.59). Multiple linear models of all three DHIs explained 67%, 65%, and 61% of the variability in species richness of amphibians, resident birds, and mammals, respectively. The DHIs, which are closely related to well-established ecological hypotheses of biodiversity, can predict species richness well, and are promising for application in biodiversity science and conservation.
•The Dynamic Habitat Indices (DHIs) capture three aspects of annual productivity.•We derived DHIs from all MODIS vegetation products globally at 1-km resolution.•Cumulative, minimum, and variation DHIs all correlate well with species richness.•Relationships between species richness and the DHIs support ecological theory.•Amphibians and birds are best explained by DHIs, mammals least.
Natural ecological communities are continuously buffeted by a varying environment, often making it difficult to measure the stability of communities using concepts requiring the existence of an ...equilibrium point. Instead of an equilibrium point, the equilibrial state of communities subject to environmental stochasticity is a stationary distribution, which is characterized by means, variances, and other statistical moments. Here, we derive three properties of stochastic multispecies communities that measure different characteristics associated with community stability. These properties can be estimated from multispecies time-series data using first-order multivariate autoregressive (MAR(1)) models. We demonstrate how to estimate the parameters of MAR(1) models and obtain confidence intervals for both parameters and the measures of stability. We also address the problem of estimation when there is observation (measurement) error. To illustrate these methods, we compare the stability of the planktonic communities in three lakes in which nutrient loading and planktivorous fish abundance were experimentally manipulated. MAR(1) models and the statistical methods we present can be used to identify dynamically important interactions between species and to test hypotheses about stability and other dynamical properties of naturally varying ecological communities. Thus, they can be used to integrate theoretical and empirical studies of community dynamics.
In order to identify additional factors required for nuclear export of messenger RNA, a genetic screen was conducted with a yeast mutant deficient in a factor Gle1p, which associates with the nuclear ...pore complex (NPC). The three genes identified encode phospholipase C and two potential inositol polyphosphate kinases. Together, these constitute a signaling pathway from phosphatidylinositol 4,5-bisphosphate to inositol hexakisphosphate (IP$_6$). The common downstream effects of mutations in each component were deficiencies in IP$_6$ synthesis and messenger RNA export, indicating a role for IP$_6$ in GLE1 function and messenger RNA export.
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.
Two phylogenetic comparative methods, independent contrasts and generalized least squares models, can be used to determine the statistical relationship between two or more traits. We show that the ...two approaches are functionally identical and that either can be used to make statistical inferences about values at internal nodes of a phylogenetic tree (hypothetical ancestors), to estimate relationships between characters, and to predict values for unmeasured species. Regression equations derived from independent contrasts can be placed back onto the original data space, including computation of both confidence intervals and prediction intervals for new observations. Predictions for unmeasured species (including extinct forms) can be made increasingly accurate and precise as the specificity of their placement on a phylogenetic tree increases, which can greatly increase statistical power to detect, for example, deviation of a single species from an allometric prediction. We reexamine published data for basal metabolic rates (BMR) of birds and show that conventional and phylogenetic allometric equations differ significantly. In new results, we show that, as compared with nonpasserines, passerines exhibit a lower rate of evolution in both body mass and mass‐corrected BMR; passerines also have significantly smaller body masses than their sister clade. These differences may justify separate, clade‐specific allometric equations for prediction of avian basal metabolic rates.
One of the few generalities in ecology, Taylor's power law, describes the species-specific relationship between the temporal or spatial variance of populations and their mean abundances. For ...populations experiencing constant per capita environmental variability, the regression of log variance versus log mean abundance gives a line with a slope of 2. Despite this expectation, most species have slopes of less than 2 (refs 2, 3-4), indicating that more abundant populations of a species are relatively less variable than expected on the basis of simple statistical grounds. What causes abundant populations to be less variable has received considerable attention, but an explanation for the generality of this pattern is still lacking. Here we suggest a novel explanation for the scaling of temporal variability in population abundances. Using stochastic simulation and analytical models, we demonstrate how negative interactions among species in a community can produce slopes of Taylor's power law of less than 2, like those observed in real data sets. This result provides an example in which the population dynamics of single species can be understood only in the context of interactions within an ecological community.
Ecologists frequently collect data on the patterns of association between adjacent trophic levels in the form of binary or quantitative food webs. Here, we develop statistical methods to estimate the ...roles of consumer and resource phylogenies in explaining patterns of consumer‐resource association. We use these methods to ask whether closely related consumer species are more likely to attack the same resource species and whether closely related resource species are more likely to be attacked by the same consumer species. We then show how to use estimates of phylogenetic signals to predict novel consumer‐resource associations solely from the phylogenetic position of species for which no other (or only partial) data are available. Finally, we show how to combine phylogenetic information with information about species’ ecological characteristics and life‐history traits to estimate the effects of species traits on consumer‐resource associations while accounting for phylogenies. We illustrate these techniques using a food web comprising species of parasitoids, leaf‐mining moths, and their host plants.
Here we give an introduction to the growing number of statistical techniques for analyzing data that are not independent realizations of the same sampling process--in other words, correlated data. We ...focus on regression problems, in which the value of a given variable depends linearly on the value of another variable. To illustrate different types of processes leading to correlated data, we analyze four simulated examples representing diverse problems arising in ecological studies. The first example is a comparison among species to determine the relationship between home-range area and body size; because species are phylogenetically related, they do not represent independent samples. The second example addresses spatial variation in net primary production and how this might be affected by soil nitrogen; because nearby locations are likely to have similar net primary productivity for reasons other than soil nitrogen, spatial correlation is likely. In the third example, we consider a time-series model to ask whether the decrease in density of a butterfly species is the result of decreases in its host-plant density; because the population density of a species in one generation is likely to affect the density in the following generation, time-series data are often correlated. The fourth example combines both spatial and temporal correlation in an experiment in which prey densities are manipulated to determine the response of predators to their food supply. For each of these examples, we use a different statistical approach for analyzing models of correlated data. Our goal is to give an overview of conceptual issues surrounding correlated data, rather than a detailed tutorial in how to apply different statistical techniques. By dispelling some of the mystery behind correlated data, we hope to encourage ecologists to learn about statistics that could be useful in their own work. Although at first encounter these techniques might seem complicated, they have the power to simplify ecological research by making more types of data and experimental designs open to statistical evaluation.
While the area of organic crop production increases at a global scale, the potential interactions between pest management in organic and conventionally managed systems have so far received little ...attention. Here, we evaluate the landscape-level co-dependence of insecticide-based and natural enemy-based pest management using a simulation model for parasitoid-host interactions in landscapes consisting of conventionally and organically managed fields. In our simulations conventional management consists of broad-spectrum or selective insecticide application, while organic management involves no insecticides. Simulations indicate that insecticide use can easily result in lose-lose scenarios whereby both organically and conventionally managed fields suffer from increased pest loads as compared to a scenario where no insecticides are used, but that under some conditions insecticide use can be compatible with biocontrol. Simulations also suggest that the pathway to achieve the insecticide reduction without triggering additional pest pressure is not straightforward, because increasing the proportion of organically managed fields or reducing the spray frequency in conventional fields can potentially give rise to dramatic increases in pest load. The disruptive effect of insecticide use, however, can be mitigated by spatially clustering organic fields and using selective insecticides, although the effectiveness of this mitigation depends on the behavioral traits of the biocontrol agents. Poorly dispersing parasitoids and parasitoids with high attack rates required a lower amount of organically managed fields for effective pest suppression. Our findings show that the transition from a landscape dominated by conventionally managed crops to organic management has potential pitfalls; intermediate levels of organic management may lead to higher pest burdens than either low or high adoption of organic management.
The suppression of agricultural pests has often been proposed as an important service of natural enemy diversity, but few experiments have tested this assertion. In this study we present empirical ...evidence that increasing the richness of a particular guild of natural enemies can reduce the density of a widespread group of herbivorous pests and, in turn, increase the yield of an economically important crop. We performed an experiment in large field enclosures where we manipulated the presence/absence of three of the most important natural enemies (the coccinellid beetle Harmonia axyridis, the damsel bug Nabis sp., and the parasitic wasp Aphidius ervi) of pea aphids (Acyrthosiphon pisum) that feed on alfalfa (Medicago sativa). When all three enemy species were together, the population density of the pea aphid was suppressed more than could be predicted from the summed impact of each enemy species alone. As crop yield was negatively related to pea aphid density, there was a concomitant non‐additive increase in the production of alfalfa in enclosures containing the more diverse enemy guild. This trophic cascade appeared to be influenced by an indirect interaction involving a second herbivore inhabiting the system – the cowpea aphid, Aphis craccivora. Data suggest that high relative densities of cowpea aphids inhibited parasitism of pea aphids by the specialist parasitoid, A. ervi. Therefore, when natural enemies were together and densities of cowpea aphids were reduced by generalist predators, parasitism of pea aphids increased. This interaction modification is similar to other types of indirect interactions among enemy species (e.g. predator–predator facilitation) that can enhance the suppression of agricultural pests. Results of our study, and those of others performed in agroecosystems, complement the broader debate over how biodiversity influences ecosystem functioning by specifically focusing on systems that produce goods of immediate relevance to human society.
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