Species exist within communities of other interacting species, so an exogenous force that directly affects one species can indirectly affect all other members of the community. In the case of climate change, many species may be affected directly and subsequently initiate numerous indirect effects that propagate throughout the community. Therefore, the net effect of climate change on any one species is a function of the direct and indirect effects. We investigated the direct and indirect effects of climate warming on corn leaf aphids, a pest of corn and other grasses, by performing an experimental manipulation of temperature, predators, and two common aphid-tending ants. Although warming had a positive direct effect on aphid population growth rate, warming reduced aphid abundance when ants and predators were present. This occurred because winter ants, which aggressively defend aphids from predators under control temperatures, were less aggressive toward predators and less abundant when temperatures were increased. In contrast, warming increased the abundance of cornfield ants, but they did not protect aphids from predators with the same vigor as winter ants. Thus, warming broke down the ant-aphid mutualism and counterintuitively reduced the abundance of this agricultural pest.
1. Classical studies of succession, largely dominated by plant community studies, focus on intrinsic drivers of change in community composition, such as interspecific competition and changes to the abiotic environment. They often do not consider extrinsic drivers of colonization, such as seasonal phenology, that can affect community change. 2. We investigated both intrinsic and extrinsic drivers of succession for dipteran communities that occupy ephemeral pools, such as those in artificial containers. By initiating communities at different times in the season and following them over time, we compared the relative importance of intrinsic (i.e., habitat age) vs. extrinsic (i.e., seasonal phenology) drivers of succession. 3. We placed water-filled artificial containers in a deciduous forest with 20 containers initiated in each of three months. Containers were sampled weekly to assess community composition. Repeated-measures mixed-effects analysis of community correspondence analysis (CA) scores enabled us to partition intrinsic and extrinsic effects on succession. Covariates of temperature and precipitation were also tested. 4. Community trajectories (as defined by CA) differed significantly with habitat age and season, indicating that both intrinsic and extrinsic effects influence succession patterns. Comparisons of AICcs showed that habitat age was more important than season for species composition. Temperature and precipitation did not explain composition changes beyond those explained by habitat age and season. 5. Quantification of relative strengths of intrinsic and extrinsic effects on succession in dipteran and other ephemeral communities enables us to disentangle processes that must be understood for predicting changes in community composition.
Climate change can affect species directly and indirectly by altering interactions between species within communities. These indirect effects can ramify through a community and affect many species, including some that may not have been directly affected by the perturbation. Identifying these chains of indirect effects is difficult, and most studies only follow indirect effects across two or three species. Here, we use a factorial field experiment to demonstrate that precipitation affects spotted aphids through a complex chain of indirect interactions that are mediated by other herbivores and a generalist predator. We experimentally simulated drought, which reduced water content in alfalfa plants. While water stress in alfalfa had no direct effect on spotted aphids, it lowered the population growth rate of pea aphids, another common alfalfa pest. Because ladybeetle predators were attracted to high pea aphid densities, predator densities were lower in drought treatments. Consequently, spotted aphid densities were released from top-down control (apparent competition) in drought treatments and reached densities three times higher than spotted aphids in ambient treatments with high pea aphid densities. Thus, drought affected spotted aphids in the interaction chain: drought --> alfalfa --> pea aphids --> predators --> spotted aphids. This result illustrates the lengthy path that indirect effects of climate change may take through a community, as well as the importance of community-level experiments in determining the net effect of climate change.
Plant species vary greatly in defenses against herbivores, but existing theory has struggled to explain this variation. Here, we test how phylogenetic relatedness, tradeoffs, trait syndromes, and sexual reproduction affect the macroevolution of defense. To examine the macroevolution of defenses, we studied 26 Oenothera (Onagraceae) species, combining chemistry, comparative phylogenetics and experimental assays of resistance against generalist and specialist herbivores. We detected dozens of phenolic metabolites within leaves, including ellagitannins (ETs), flavonoids, and caffeic acid derivatives (CAs). The concentration and composition of phenolics exhibited low to moderate phylogenetic signal. There were clear negative correlations between multiple traits, supporting the prediction of allocation tradeoffs. There were also positively covarying suites of traits, but these suites did not strongly predict resistance to herbivores and thus did not act as defensive syndromes. By contrast, specific metabolites did correlate with the performance of generalist and specialist herbivores. Finally, that repeated losses of sex in Oenothera was associated with the evolution of increased flavonoid diversity and altered phenolic composition. These results show that secondary chemistry has evolved rapidly during the diversification of Oenothera. This evolution has been marked by allocation tradeoffs between traits, some of which are related to herbivore performance. The repeated loss of sex appears also to have constrained the evolution of plant secondary chemistry, which may help to explain variation in defense among plants.
Ecological networks of two interacting guilds of species, such as flowering plants and pollinators, are common in nature, and studying their structure can yield insights into their resilience to environmental disturbances. Here we develop analytical methods for exploring the strengths of interactions within bipartite networks consisting of two guilds of phylogenetically related species. We then apply these methods to investigate the resilience of a plant-pollinator community to anticipated climate change. The methods allow the statistical assessment of, for example, whether closely related pollinators are more likely to visit plants with similar relative frequencies, and whether closely related pollinators tend to visit closely related plants. The methods can also incorporate trait information, allowing us to identify which plant traits are likely responsible for attracting different pollinators. These questions are important for our study of 14 prairie plants and their 22 insect pollinators. Over the last 70 years, six of the plants have advanced their flowering, while eight have not. When we experimentally forced earlier flowering times, five of the six advanced-flowering species experienced higher pollinator visitation rates, whereas only one of the eight other species had more visits; this network thus appears resilient to climate change, because those species with advanced flowering have ample pollinators earlier in the season. Using the methods developed here, we show that advanced-flowering plants did not have a distinct pollinator community from the other eight species. Furthermore, pollinator phylogeny did not explain pollinator community composition; closely related pollinators were not more likely to visit the same plant species. However, differences among pollinator communities visiting different plants were explained by plant height, floral color, and symmetry. As a result, closely related plants attracted similar numbers of pollinators. By parsing out characteristics that explain why plants share pollinators, we can identify plant species that likely share a common fate in a changing climate.
Rapid transitions in ecosystem structure, or regime shifts, are a hallmark of alternative stable states (ASS). However, regime shifts can occur even when feedbacks are not strong enough to cause ASS. We investigated the potential for ASS to explain transitions between dominance of an invasive species, rusty crayfish (Orconectes rusticus), and native sunfishes (Lepomis spp.) in northern Wisconsin (USA) lakes. A rapid transition from Lepomis to rusty crayfish dominance occurred as rusty crayfish invaded Trout Lake, and the reverse transition resulted from an eight-year experimental removal of rusty crayfish from Sparkling Lake. We fit a stage-structured population model of species interactions to 31 years of time-series data from each lake. The model identified water level as an important driver, with drought conditions reducing rusty crayfish recruitment and allowing Lepomis dominance. The maximum-likelihood parameter estimates of the negative interaction between rusty crayfish and Lepomis led to ASS in the model, where each species was capable of excluding the other within a narrow range of environmental conditions. However, uncertainty in parameter estimates made it impossible to exclude the potential that rapid transitions were caused by a simpler threshold response lacking alternative equilibria. Simulated forward and backward transitions between species dominance occurred at different environmental conditions (i.e., hysteresis), even when the parameters used for simulation did not predict ASS as a result of slow species responses to environmental drivers. Thus, ASS are possible, but by no means certain, explanations for rapid transitions in this system, and our results highlight the difficulties associated with distinguishing ASS from other types of threshold responses. However, whether regime shifts are caused by ASS may be relatively unimportant in this system, as the range of conditions over which transitions occur is narrow, and under most conditions, the system is predicted to exist in only a single state.
Recent studies suggest that environmental changes may tip the balance between interacting species, leading to the extinction of one or more species. While it is recognized that evolution will play a role in determining how environmental changes directly affect species, the interactions among species force us to consider the coevolutionary responses of species to environmental changes.
Data sets from ecological experiments can be difficult to analyze, due to lack of independence of experimental units and complex variance structures. In addition, information of interest may lie in complicated contrasts among treatments, rather than direct output from statistical tests. Here, we present a statistical framework for analyzing data sets containing non-independent experimental units and differences in variance among treatments (heteroscedasticity) and apply this framework to experimental data on interspecific competition among three tadpole species. Our framework involves three steps: (1) use a multilevel regression model to calculate coefficients of treatment effects on response variables; (2) combine coefficients to quantify the strength of competition (the target information of our experiment); and (3) use parametric bootstrapping to calculate significance of competition strengths. We repeated this framework using three multilevel regression models to analyze data at the level of individual tadpoles, at the replicate level, and at the replicate level accounting for heteroscedasticity. Comparing results shows the need to correctly specify the statistical model, with the model that accurately accounts for heteroscedasticity leading to different conclusions from the other two models. This approach gives a single, comprehensive analysis of experimental data that can be used to extract informative biological parameters in a statistically rigorous way.
Cannibalism, where one species feeds on individuals of its own species, and intraguild predation (IGP), where a predator feeds on other predatory species, can both pose significant threats to natural enemies and interfere with their biological control of pests. Behavioral mechanisms to avoid these threats, however, could help maintain superior pest control. Here, we ask whether larvae of Coccinella septempunctata (Coleoptera: Coccinellidae) and Harmonia axyridis (Coleoptera: Coccinellidae) respond to larval tracks deposited by the other and whether this behavioral response reduces the threat of cannibalism and IGP. In petri dish experiments, we show that both H. axyridis and C. septempunctata avoid foraging in areas with conspecific larval tracks. Using a method of preventing larvae from depositing tracks, we then demonstrate that the frequency of cannibalism is greater for both species when larvae are prevented from depositing tracks compared with when the tracks are deposited. For multi-species interactions we show in petri dish experiments that C. septempunctata avoids H. axyridis larval tracks but H. axyridis does not avoid C. septempunctata larval tracks, demonstrating an asymmetry in response to larval tracks that parallels the asymmetry in aggressiveness between these species as intraguild predators. On single plants, we show that the presence of H. axyridis larval tracks reduces the risk of IGP by H. axyridis on C. septempunctata. Our study suggests that larval tracks can be used in more ways than previously described, in this case by changing coccinellid larval behavior in a way that reduces cannibalism and IGP.
Pyramid transgenic crops that express two Bacillus thuringiensis (Bt) toxins hold great potential for reducing insect damage and slowing the evolution of resistance to the toxins. Here, we analyzed a suite of models for pyramid Bt crops to illustrate factors that should be considered when implementing the high dose-refuge strategy for resistance management; this strategy involves the high expression of toxins in Bt plants and use of non-Bt plants as refuges. Although resistance evolution to pyramid Bt varieties should in general be slower, resistance to pyramid Bt varieties is nonetheless driven by the same evolutionary processes as single Bt-toxin varieties. The main advantage of pyramid varieties is the low survival of insects heterozygous for resistance alleles. We show that there are two modes of resistance evolution. When populations of purely susceptible insects persist, leading to density dependence, the speed of resistance evolution changes slowly with the proportion of refuges. However, once the proportion of non-Bt plants crosses the threshold below which a susceptible population cannot persist, the speed of resistance evolution increases rapidly. This suggests that adaptive management be used to guarantee persistence of susceptible populations. We compared the use of seed mixtures in which Bt and non-Bt plants are sown in the same fields to the use of spatial refuges. As found for single Bt varieties, seed mixtures can speed resistance evolution if larvae move among plants. Devising optimal management plans for deploying spatial refuges is difficult because they depend on crop rotation patterns, whether males or females have limited dispersal, and other characteristics. Nonetheless, the effects of spatial refuges on resistance evolution can be understood by considering the three mechanisms determining the rate of resistance evolution: the force of selection (the proportion of insects killed by Bt), assortative mating (deviations of the proportion of heterozygotes from Hardy-Weinberg equilibrium at the total population level), and male mating success (when males carrying resistance alleles find fewer mates). Of these three, assortative mating is often the least important, even though this mechanism is the most frequently cited explanation for the efficacy of the high dose-refuge strategy.
"The jack-of-all-trades is a master of none" describes the widely held belief that engaging in many tasks comes at the cost of being unable to do those tasks well. However, empirical evidence for generalist fitness costs remains scarce. We used published data from a long-term field survey of aphid parasitoids to determine whether relative specialists are more abundant than generalists on their shared hosts, a pattern that would be expected if generalists suffer a trade-off between host-range breadth and host-use efficiency. Relative specialists were more abundant than generalists on their shared hosts, but only when we used a measure of specialization that accounts for the taxonomic differences among parasitoids hosts. These results suggest that a generalist-specialist trade-off exists within this group of parasitoids and that the generalist fitness cost depends on the taxonomic breadth, rather than the number, of host species that are used.
Suppression of the invasive plant Salvinia molesta by the salvinia weevil is an iconic example of successful biological control. However, in the billabongs (oxbow lakes) of Kakadu National Park, Australia, control is fitful and incomplete. By fitting a process-based nonlinear model to thirteen-year data sets from four billabongs, here we show that incomplete control can be explained by alternative stable states--one state in which salvinia is suppressed and the other in which salvinia escapes weevil control. The shifts between states are associated with annual flooding events. In some years, high water flow reduces weevil populations, allowing the shift from a controlled to an uncontrolled state; in other years, benign conditions for weevils promote the return shift to the controlled state. In most described ecological examples, transitions between alternative stable states are relatively rare, facilitated by slow-moving environmental changes, such as accumulated nutrient loading or climate change. The billabongs of Kakadu give a different manifestation of alternative stable states that generate complex and seemingly unpredictable dynamics. Because shifts between alternative stable states are stochastic, they present a potential management strategy to maximize effective biological control: when the domain of attraction to the state of salvinia control is approached, augmentation of the weevil population or reduction of the salvinia biomass may allow the lower state to trap the system.
In nature, multiple parasite species infect multiple host species and are influenced by processes operating across different spatial and temporal scales. Data sets incorporating these complexities offer exciting opportunities to examine factors that shape epidemics. We present a method using generalized linear mixed models in a multilevel modeling framework to analyze patterns of variances and correlations in binomially distributed prevalence data. We then apply it to a multi-lake, multiyear data set involving two Daphnia host species and nine microparasite species. We found that the largest source of variation in parasite prevalence was the species identities of host-parasite pairs, indicating strong host-parasite specificity. Within host-parasite combinations, spatial variation (among lakes) exceeded interannual variation. This suggests that factors promoting differences among lakes (e.g., habitat characteristics and species interactions) better explain variation in peak infection prevalence in our data set than factors driving differences among years (e.g., climate). Prevalences of parasites in D. dentifera were more positively correlated than those for D. pulicaria, suggesting that similar factors influenced epidemic size among parasites in D. dentifera. Overall, this study demonstrates a method for parsing patterns of variation and covariation in infection prevalence data, providing greater insight into the relative importance of different underlying drivers of parasitism.
Recent climate change has caused the distributions of many species to shift poleward, yet few empirical studies have addressed which species will be vulnerable to longer-term climate changes. To investigate past consequences of climate change, we calculated the population extinction rates of 35 reptile species from 87 Greek land-bridge islands in the Mediterranean that occurred over the past 16,000 years. Population extinction rates were higher for those species that today have more northern distributions. We further found that northern species requiring cool, mesic habitats had less available suitable habitat among islands, implicating loss of suitable habitat in their elevated extinction rates. These extinctions occurred in the context of increasing habitat fragmentation, with islands shrinking and separating as sea levels rose. Thus, the circumstances faced by reptiles on the islands are similar to challenges for numerous species today that must cope with a changing climate while living in an increasingly human-fragmented landscape. Our island-biogeographical approach to investigating historical population extinctions gives insight into the long-term patterns of species responses to climate change.
Climate change has led to phenological shifts in flowering plants and insect pollinators, causing concern that these shifts will disrupt plant-pollinator mutualisms. We experimentally investigated how shifts in flowering onset affect pollinator visitation for 14 native perennial plant species, six of which have exhibited shifts to earlier flowering over the last 70 years and eight of which have not. We manipulated flowering onset in greenhouses and then observed pollinator visitation in the field. Five of six species with historically advanced flowering received more visits when flowering was experimentally advanced, whereas seven of eight species with historically unchanged flowering received fewer visits when flowering earlier. This pattern suggests that species unconstrained by pollinators have advanced their flowering, whereas species constrained by pollinators have not. In contrast to current concern about phenological mismatches disrupting plant-pollinator mutualisms, mismatches at the onset of flowering are not occurring for most of our study species.
Ecology Letters (2010) 13: 1459-1474 ABSTRACT: There is growing concern that rapid environmental degradation threatens mutualistic interactions. Because mutualisms can bind species to a common fate, mutualism breakdown has the potential to expand and accelerate effects of global change on biodiversity loss and ecosystem disruption. The current focus on the ecological dynamics of mutualism under global change has skirted fundamental evolutionary issues. Here, we develop an evolutionary perspective on mutualism breakdown to complement the ecological perspective, by focusing on three processes: (1) shifts from mutualism to antagonism, (2) switches to novel partners and (3) mutualism abandonment. We then identify the evolutionary factors that may make particular classes of mutualisms especially susceptible or resistant to breakdown and discuss how communities harbouring mutualisms may be affected by these evolutionary responses. We propose a template for evolutionary research on mutualism resilience and identify conservation approaches that may help conserve targeted mutualisms in the face of environmental change.
We derive a new metric of community similarity that takes into account the phylogenetic relatedness among species. This metric, phylogenetic community dissimilarity (PCD), can be partitioned into two components, a nonphylogenetic component that reflects shared species between communities (analogous to Sørensen s similarity metric) and a phylogenetic component that reflects the evolutionary relationships among nonshared species. Therefore, even if a species is not shared between two communities, it will increase the similarity of the two communities if it is phylogenetically related to species in the other community. We illustrate PCD with data on fish and aquatic macrophyte communities from 59 temperate lakes. Dissimilarity between fish communities associated with environmental differences between lakes often has a phylogenetic component, whereas this is not the case for macrophyte communities. With simulations, we then compare PCD with two other metrics of phylogenetic community similarity, II(ST) and UniFrac. Of the three metrics, PCD was best at identifying environmental drivers of community dissimilarity, showing lower variability and greater statistical power. Thus, PCD is a statistically powerful metric that separates the effects of environmental drivers on compositional versus phylogenetic components of community structure.
More diverse communities of consumers typically use more resources, which often is attributed to resource partitioning. However, experimentally demonstrating this role of resource partitioning in diverse communities has been difficult. We used an experimental response-surface design, varying intra- and interspecific consumer densities, to compare patterns of resource exploitation between simple and diverse communities of aphid predators. With increasing density, each single consumer species rapidly plateaued in its ability to extract more resources. This suggests intraspecific competition for a subset of the resource pool, a hallmark of resource partitioning. In contrast, more diverse-predator communities achieved greater overall resource depletion. By statistically fitting mechanistic models to the data, we demonstrated that resource partitioning rather than facilitation provides the better explanation for the observed differences in resource use between simple and diverse communities. This model-fitting approach also allowed us to quantify overlap in resource use by different consumer species.
Autoregressive moving average (ARMA) models are useful statistical tools to examine the dynamical characteristics of ecological time-series data. Here, we illustrate the utility and challenges of applying ARMA (p,q) models, where p is the dimension of the autoregressive component of the model, and q is the dimension of the moving average component. We focus on parameter estimation and model selection, comparing both maximum likelihood (ML) and restricted maximum likelihood (REML) parameter estimation. While REML estimation performs better (has less bias) than ML estimation for ARMA (p,q) models with p = 1 (as has been found previously), for models with p > 1 the performance of the estimators is complicated by multimodal likelihood functions. The resulting difficulties in estimation lead to our recommendation that likelihood functions be routinely investigated when applying ARMA (p,q) models. To aid this investigation, we provide MATLAB and R code for the ML and REML likelihood functions. We further explore the consequences of measurement error, showing how it can be explicitly and implicitly incorporated into estimation. In addition to parameter estimation, we also examine model selection for identifying the correct model dimensions (p and q). Finally, we estimate the characteristic return rate of the stochastic process to its stationary distribution, a quantity that describes a key property of population dynamics, and investigate bias that results from both estimation and model selection. While fitting ARMA models to ecological time series with complex dynamics has challenges, these challenges can be surmounted, making ARMA a useful and broadly applicable approach.
We develop statistical methods for phylogenetic logistic regression in which the dependent variable is binary (0 or 1) and values are nonindependent among species, with phylogenetically related species tending to have the same value of the dependent variable. The methods are based on an evolutionary model of binary traits in which trait values switch between 0 and 1 as species evolve up a phylogenetic tree. The more frequently the trait values switch (i.e., the higher the rate of evolution), the more rapidly correlations between trait values for phylogenetically related species break down. Therefore, the statistical methods also give a way to estimate the phylogenetic signal of binary traits. More generally, the methods can be applied with continuous- and/or discrete-valued independent variables. Using simulations, we assess the statistical properties of the methods, including bias in the estimates of the logistic regression coefficients and the parameter that estimates the strength of phylogenetic signal in the dependent variable. These analyses show that, as with the case for continuous-valued dependent variables, phylogenetic logistic regression should be used rather than standard logistic regression when there is the possibility of phylogenetic correlations among species. Standard logistic regression does not properly account for the loss of information caused by resemblance of relatives and as a result is likely to give inflated type I error rates, incorrectly identifying regression parameters as statistically significantly different from zero when they are not.
How strongly natural populations are regulated has a long history of debate in ecology. Here, we discuss concepts of population regulation appropriate for stochastic population dynamics. We then analyse two large collections of data sets with autoregressive-moving average (ARMA) models, using model selection techniques to find best-fitting models. We estimated two metrics of population regulation: the characteristic return rate of populations to stationarity and the variability of the stationary distribution (the long-term distribution of population abundance). Empirically, longer time series were more likely to show weakly regulated population dynamics. For data sets of length > or = 20, more than 35% had characteristic return times > 6 years, and more than 29% had stationary distributions whose coefficients of variation were more than two times greater than would be the case if they were maximally regulated. These results suggest that many natural populations are weakly regulated.
Why do epidemics end? This simple question has puzzled ecologists and epidemiologists for decades. Early explanations focused on drops in host density arising from highly virulent parasites and, later, on the effects of acquired immunity. More recently, however, two additional epidemic-ending mechanisms have surfaced: environmental change (including seasonality) and rapid evolution of increased resistance of hosts to infection. Both mechanisms, via either decreasing seasonal temperatures or evolution of resistance, act by altering transmission rates. To explore these possibilities, we tracked five epidemics of a virulent yeast parasite in lake populations of Daphnia dentifera from late summer through autumn. We then fit and compared performance of time-series models that included temperature-dependent and/or evolutionary changes in transmission rates. The analyses show evolution to be the better explanation of epidemic dynamics. Thus, by integrating data and models, this study highlights the potential role of evolution in driving the termination of epidemics in natural populations.
Data are often collected for a single species within an ecological community, so quantitative tools for drawing inferences about the unobserved portions of the community from single-species data are valuable. In this paper, we present and examine a method for estimating community dimension (the number of strongly interacting species or groups) from time series data on a single species. The dynamics of one species can be strongly affected by environmental stochasticity acting not only on itself, but also on other species with which it interacts. By fully accounting for the effects of stochasticity on populations embedded in a community, our approach gives better estimates of community dimension than commonly used methods. Using a combination of time series data and simulations, we show that failing to properly account for stochasticity when attempting to relate population dynamics to attributes of the community can give misleading information about community dimension.
Understanding the drivers and consequences of disease epidemics is an important frontier in ecology. However, long-term data on hosts, their parasites, and the corresponding environmental conditions necessary to explore these interactions are often unavailable. We examined the dynamics of Daphnia pulicaria, a keystone zooplankter in lake ecosystems, to explore the long-term causes and consequences of infection by a chytridiomycete parasitoid (Polycaryum laeve). After quantifying host-pathogen dynamics from vouchered samples collected over 15 years, we used autoregressive models to evaluate (1) hypothesized drivers of infection, including host density, water temperature, dissolved oxygen, host-food availability, and lake mixing; and (2) the effects of epidemics on host populations. Infection was present in most years but varied widely in prevalence, from < 1% to 34%, with seasonal peaks in early spring and late fall. Within years, lake stratification strongly inhibited P. laeve transmission, such that epidemics occurred primarily during periods of water mixing. Development of the thermocline likely reduced transmission by spatially separating susceptible hosts from infectious zoospores. Among years, ice duration and cumulative snowfall correlated negatively with infection prevalence, likely because of reductions in spring phytoplankton and D. pulicaria density in years with extended winters. Epidemics also influenced dynamics of the host population. Infected D. pulicaria rarely (< 1%) contained eggs, and P. laeve prevalence was positively correlated with sexual reproduction in D. pulicaria. Analyses of D. pulicaria density-dependent population dynamics predicted that, in the absence of P. laeve infection, host abundance would be 11-50% higher than what was observed. By underscoring the importance of complex physical processes in controlling host-parasite interactions and of epidemic disease in influencing host populations, our results highlight the value of long-term data for understanding wildlife disease dynamics.
How environmental change affects species abundances depends on both the food web within which species interact and their potential to evolve. Using field experiments, we investigated both ecological and evolutionary responses of pea aphids (Acyrthosiphon pisum), a common agricultural pest, to increased frequency of episodic heat shocks. One predator species ameliorated the decrease in aphid population growth with increasing heat shocks, whereas a second predator did not, with this contrast caused by behavioral differences between predators. We also compared aphid strains with stably inherited differences in heat tolerance caused by bacterial endosymbionts and showed the potential for rapid evolution for heat-shock tolerance. Our results illustrate how ecological and evolutionary complexities should be incorporated into predictions of the consequences of environmental change for species populations.
The storage effect is a mechanism that can facilitate the coexistence of competing species through temporal fluctuations in reproductive output. Numerous natural systems have the prerequisites for the storage effect, yet it has rarely been quantitatively assessed. Here, we investigate the possible importance of the storage effect in explaining the coexistence of tree species in the diverse tropical forest on Barro Colorado Island, Panama. This tropical forest has been monitored for more than 20 years, and annual seed production is asynchronous among species, a primary requirement for the storage effect. We constructed a model of forest regeneration that includes species-specific recruitment through seed, sapling, and adult stages, and we parameterized the model using data for 28 species for which information is known about seedling germination and survival. Simulations of the model demonstrated that the storage effect alone can be a strong mechanism allowing long-term persistence of species. We also developed a metric to quantify the strength of the storage effect in a way comparable to classical resource partitioning. Applying this metric to seed production data from 108 species, the storage effect reduces the strength of pairwise interspecific competition to 11-43% of the strength of intraspecific competition, thereby demonstrating strong potential to facilitate coexistence. Finally, for a subset of 51 species whose phylogenetic relationships are known, we compared the strength of the storage effect between pairs of species to their phylogenetic similarity. The strength of the storage effect between closely related species was on average no different from distantly related species, implying that the storage effect can be important in promoting the coexistence of even closely related species.
Assessments of the status of endangered species have focused on population sizes, often without knowledge of demographic and behavioral processes underlying population recovery. We analyzed demographic data from a 28-year study of a critically endangered primate, the northern muriqui, to investigate possible changes in demographic rates as this population recovered from near extirpation. As the population increased from 60 to nearly 300 individuals, its growth rate declined due to increased mortality and male-biased birth sex ratios; the increased mortality was not uniform across ages and sexes, and there has been a recent increase in mortality of prime-aged males. If not for a concurrent increase in fertility rates, the population would have stabilized at 200 individuals instead of continuing to grow. The unexpected increase in fertility rates and in adult male mortality can be attributed to the muriquis expansion of their habitat by spending more time on the ground. The demographic consequences of this behavioral shift must be incorporated into management tactics for this population and emphasize the importance of understanding demographic rates in the recovery of endangered species.
Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called early warning signals, and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.
The earlier flowering times exhibited by many plant species are a conspicuous sign of climate change. Altered phenologies have caused concern that species could suffer population declines if they flower at times when effective pollinators are unavailable. For two perennial wildflowers, Tradescantia ohiensis and Asclepias incarnata, we used an experimental approach to explore how changing phenology affects the taxonomic composition of the pollinator assemblage and the effectiveness of individual pollinator taxa. After finding in the previous year that fruit set varied with flowering time, we manipulated flowering onset in greenhouses, placed plants in the field over the span of five weeks, and measured pollinator effectiveness as the number of seeds produced after a single visit to a flower. The average effectiveness of pollinators and the expected rates of pollination success were lower for plants of both species flowering earlier than for plants flowering at historical times, suggesting there could be reproductive costs to earlier flowering. Whereas for A. incarnata, differences in average seed set among weeks were due primarily to changes in the composition of the pollinator assemblage, the differences for T. ohiensis were driven by the combined effects of compositional changes and increases over time in the effectiveness of some pollinator taxa. Both species face the possibility of temporal mismatch between the availability of the most effective pollinators and the onset of flowering, and changes in the effectiveness of individual pollinator taxa through time may add an unexpected element to the reproductive consequences of such mismatches.
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