Global patterns of allochthony in stream-riparian meta-ecosystems.

Ecosystems that are coupled by reciprocal flows of energy and nutrient subsidies can be viewed as a single "meta-ecosystem." Despite these connections, the reciprocal flow of subsidies is greatly asymmetrical and seasonally pulsed. Here, we synthesize existing literature on stream-riparian meta-ecosystems to quantify global patterns of the amount of subsidy consumption by organisms, known as "allochthony." These resource flows are important since they can comprise a large portion of consumer diets, but can be disrupted by human modification of streams and riparian zones. Despite asymmetrical subsidy flows, we found stream and riparian consumer allochthony to be equivalent. Although both fish and stream invertebrates rely on seasonally pulsed allochthonous resources, we find allochthony varies seasonally only for fish, being nearly three times greater during the summer and fall than during the winter and spring. We also find that consumer allochthony varies with feeding traits for aquatic invertebrates, fish, and terrestrial arthropods, but not for terrestrial vertebrates. Finally, we find that allochthony varies by climate for aquatic invertebrates, being nearly twice as great in arid climates than in tropical climates, but not for fish. These findings are critical to understanding the consequences of global change, as ecosystem connections are being increasingly disrupted.

Long transients and dendritic network structure affect spatial predator-prey dynamics in experimental microcosms.

Spatial dynamics can promote persistence of strongly interacting predators and prey. Theory predicts that spatial predator-prey systems are prone to long transients, meaning that the dynamics leading to persistence or extinction manifest over hundreds of generations. Furthermore, the form and duration of transients may be altered by spatial network structure. Few empirical studies have examined the importance of transients in spatial food webs, especially in a network context, due to the difficulty in collecting the large scale and long-term data required. We examined predator-prey dynamics in protist microcosms using three experimental spatial structures: isolated, river-like dendritic networks and regular lattice networks. Densities and patterns of occupancy were followed for both predators and prey over a time scale that equates to >100 predator and >500 prey generations. We found that predators persisted in dendritic and lattice networks whereas they went extinct in the isolated treatment. The dynamics leading to predator persistence played out over long transients with three distinct phases. The transient phases showed differences between dendritic and lattice structures, as did underlying patterns of occupancy. Spatial dynamics differed among organisms in different trophic positions. Predators showed higher local persistence in more connected bottles while prey showed this in more spatially isolated ones. Predictions based on spatial patterns of connectivity derived from metapopulation theory explained predator occupancy, while prey occupancy was better explained by predator occupancy. Our results strongly support the hypothesized role of spatial dynamics in promoting persistence in food webs, but that the dynamics ultimately leading to persistence may occur with long transients which in turn may be influenced by spatial network structure and trophic interactions.

Body size dependent dispersal influences stability in heterogeneous metacommunities.

Body size affects key biological processes across the tree of life, with particular importance for food web dynamics and stability. Traits influencing movement capabilities depend strongly on body size, yet the effects of allometrically-structured dispersal on food web stability are less well understood than other demographic processes. Here we study the stability properties of spatially-arranged model food webs in which larger bodied species occupy higher trophic positions, while species' body sizes also determine the rates at which they traverse spatial networks of heterogeneous habitat patches. Our analysis shows an apparent stabilizing effect of positive dispersal rate scaling with body size compared to negative scaling relationships or uniform dispersal. However, as the global coupling strength among patches increases, the benefits of positive body size-dispersal scaling disappear. A permutational analysis shows that breaking allometric dispersal hierarchies while preserving dispersal rate distributions rarely alters qualitative aspects of metacommunity stability. Taken together, these results suggest that the oft-predicted stabilizing effects of large mobile predators may, for some dimensions of ecological stability, be attributed to increased patch coupling per se, and not necessarily coupling by top trophic levels in particular.

The application of metacommunity theory to the management of riverine ecosystems.

River managers strive to use the best available science to sustain biodiversity and ecosystem function. To achieve this goal requires consideration of processes at different scales. Metacommunity theory describes how multiple species from different communities potentially interact with local-scale environmental drivers to influence population dynamics and community structure. However, this body of knowledge has only rarely been used to inform management practices for river ecosystems. In this paper, we present a conceptual model outlining how the metacommunity processes of local niche sorting and dispersal can influence the outcomes of management interventions and provide a series of specific recommendations for applying these ideas as well as research needs. In all cases, we identify situations where traditional approaches to riverine management could be enhanced by incorporating an understanding of metacommunity dynamics. A common theme is developing guidelines for assessing the metacommunity context of a site or region, evaluating how that context may affect the desired outcome, and incorporating that understanding into the planning process and methods used. To maximize the effectiveness of management activities, scientists and resource managers should update the toolbox of approaches to riverine management to reflect theoretical advances in metacommunity ecology.

Predicting Pattern Formation in Multilayer Networks.

We investigate how the structure of interactions between coupled oscillators influences the formation of asynchronous patterns in a multilayer network by formulating a simple, general multilayer oscillator model. We demonstrate the analysis of this model in three-oscillator systems, illustrating the role of interactions among oscillators in sustaining differences in both the phase and amplitude of oscillations leading to the formation of asynchronous patterns. Finally, we demonstrate the generalizability of our model's predictions through comparison with a more realistic multilayer model. Overall, our model provides a useful approach for predicting the types of asynchronous patterns that multilayer networks of coupled oscillators which cannot be achieved by the existing methods which focus on characterizing the synchronous state.

Effects of uncertainty and variability on population declines and IUCN Red List classifications.

The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age-structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates, particularly when available evidence indicates a potential transition to higher risk categories.

Pharmaceuticals and other anthropogenic chemicals in atmospheric particulates and precipitation.

Air and precipitation samples were analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS) and gas chromatography mass spectrometry (GC-MS) for pharmaceuticals, personal care products, and other commercial chemicals within the St. Paul/Minneapolis metropolitan area of Minnesota, U.S. Of the 126 chemicals analyzed, 17 were detected at least once. Bisphenol A, N,N-diethyl-meta-toluamide (DEET), and cocaine were the most frequently detected; their maximum concentrations in snow were 3.80, 9.49, and 0.171ng/L and in air were 0.137, 0.370, and 0.033ng/m3, respectively. DEET and cocaine were present in samples of rain up to 14.5 and 0.806ng/L, respectively. Four antibiotics - ofloxacin, ciprofloxacin, enrofloxacin, and sulfamethoxazole - were detected at concentrations up to 10.3ng/L in precipitation, while ofloxacin was the sole antibiotic detected in air at 0.013ng/m3. The X-ray contrast agent iopamidol and the non-steroidal anti-inflammatory drug naproxen were detected in snow up to 228ng/L and 3.74ng/L, respectively, while caffeine was detected only in air at 0.069 and 0.111ng/m3. Benzothiazole was present in rain up to 70ng/L, while derivatives of benzotriazole - 4-methylbenzotriazole, 5-methylbenzotriazole, and 5-chlorobenzotriazole - were detected at concentrations up to 1.5ng/L in rain and 3.4ng/L in snow. Nonylphenol and nonylphenol monoethoxylate were detected once in air at 0.165 and 0.032ng/m3, respectively. Although the sources of these chemicals to atmosphere are not known, fugacity analysis suggests that wastewater may be a source of nonylphenol, nonylphenol monoethoxylate, DEET, and caffeine to atmosphere. The land-spreading of biosolids is known to generate PM10 that could also account for the presence of these contaminants in air. Micro-pollutant detections in air and precipitation are similar to the profile of contaminants reported previously for surface water. This proof of concept study suggests that atmospheric transport of these chemicals may partially explain the ubiquity of these contaminants in the aquatic environment.

Geometric indicators of population persistence in branching continuous-space networks.

We study population persistence in branching tree networks emulating systems such as river basins, cave systems, organisms on vegetation surfaces, and vascular networks. Population dynamics are modeled using a reaction-diffusion-advection equation on a metric graph which provides a continuous, spatially explicit model of network habitat. A metric graph, in contrast to a standard graph, allows for population dynamics to occur within edges rather than just at graph vertices, subsequently adding a significant level of realism. Within this framework, we stochastically generate branching tree networks with a variety of geometric features and explore the effects of network geometry on the persistence of a population which advects toward a lethal outflow boundary. We identify a metric (CM), the distance from the lethal outflow point at which half of the habitable volume of the network lies upstream of, as a promising indicator of population persistence. This metric outperforms other metrics such as the maximum and minimum distances from the lethal outflow to an upstream boundary and the total habitable volume of the network. The strength of CM as a predictor of persistence suggests that it is a proper "system length" for the branching networks we examine here that generalizes the concept of habitat length in the classical linear space models.

Implications of different population model structures for management of threatened plants.

Population viability analysis (PVA) is a reliable tool for ranking management options for a range of species despite parameter uncertainty. No one has yet investigated whether this holds true for model uncertainty for species with complex life histories and for responses to multiple threats. We tested whether a range of model structures yielded similar rankings of management and threat scenarios for 2 plant species with complex postfire responses. We examined 2 contrasting species from different plant functional types: an obligate seeding shrub and a facultative resprouting shrub. We exposed each to altered fire regimes and an additional, species-specific threat. Long-term demographic data sets were used to construct an individual-based model (IBM), a complex stage-based model, and a simple matrix model that subsumes all life stages into 2 or 3 stages. Agreement across models was good under some scenarios and poor under others. Results from the simple and complex matrix models were more similar to each other than to the IBM. Results were robust across models when dominant threats are considered but were less so for smaller effects. Robustness also broke down as the scenarios deviated from baseline conditions, likely the result of a number of factors related to the complexity of the species' life history and how it was represented in a model. Although PVA can be an invaluable tool for integrating data and understanding species' responses to threats and management strategies, this is best achieved in the context of decision support for adaptive management alongside multiple lines of evidence and expert critique of model construction and output.

Can inducible resistance in plants cause herbivore aggregations? Spatial patterns in an inducible plant/herbivore model.

Many theories regarding the evolution of inducible resistance in plants have an implicit spatial component, but most relevant population dynamic studies ignore spatial dynamics. We examined a spatially explicit model of plant inducible resistance and herbivore population dynamics to explore how realistic features of resistance and herbivore responses influence spatial patterning. Both transient and persistent spatial patterns developed in all models examined, where patterns manifested as wave-like aggregations of herbivores and variation in induction levels. Patterns arose when herbivores moved away from highly induced plants, there was a lag between damage and deployment of induced resistance, and the relationship between herbivore density and strength of the induction response had a sigmoid shape. These mechanisms influenced pattern formation regardless of the assumed functional relationship between resistance and herbivore recruitment and mortality. However, in models where induction affected herbivore mortality, large-scale herbivore population cycles driven by the mortality response often co-occurred with smaller scale spatial patterns driven by herbivore movement. When the mortality effect dominated, however, spatial pattern formation was completely replaced by spatially synchronized herbivore population cycles. Our results present a new type of ecological pattern formation driven by induced trait variation, consumer behavior, and time delays that has broad implications for the community and evolutionary ecology of plant defenses.

Combined Influences of Model Choice, Data Quality, and Data Quantity When Estimating Population Trends.

Estimating and projecting population trends using population viability analysis (PVA) are central to identifying species at risk of extinction and for informing conservation management strategies. Models for PVA generally fall within two categories, scalar (count-based) or matrix (demographic). Model structure, process error, measurement error, and time series length all have known impacts in population risk assessments, but their combined impact has not been thoroughly investigated. We tested the ability of scalar and matrix PVA models to predict percent decline over a ten-year interval, selected to coincide with the IUCN Red List criterion A.3, using data simulated for a hypothetical, short-lived organism with a simple life-history and for a threatened snail, Tasmaphena lamproides. PVA performance was assessed across different time series lengths, population growth rates, and levels of process and measurement error. We found that the magnitude of effects of measurement error, process error, and time series length, and interactions between these, depended on context. We found that high process and measurement error reduced the reliability of both models in predicted percent decline. Both sources of error contributed strongly to biased predictions, with process error tending to contribute to the spread of predictions more than measurement error. Increasing time series length improved precision and reduced bias of predicted population trends, but gains substantially diminished for time series lengths greater than 10-15 years. The simple parameterization scheme we employed contributed strongly to bias in matrix model predictions when both process and measurement error were high, causing scalar models to exhibit similar or greater precision and lower bias than matrix models. Our study provides evidence that, for short-lived species with structured but simple life histories, short time series and simple models can be sufficient for reasonably reliable conservation decision-making, and may be preferable for population projections when unbiased estimates of vital rates cannot be obtained.

Colonisation rate and adaptive foraging control the emergence of trophic cascades.

Ecological communities are assembled and sustained by colonisation. At the same time, predators make foraging decisions based on the local availabilities of potential resources, which reflects colonisation. We combined field and laboratory experiments with mathematical models to demonstrate that a feedback between these two processes determines emergent patterns in community structure. Namely, our results show that prey colonisation rate determines the strength of trophic cascades - a feature of virtually all ecosystems - by prompting behavioural shifts in adaptively foraging omnivorous fish predators. Communities experiencing higher colonisation rates were characterised by higher invertebrate prey and lower producer biomasses. Consequently, fish functioned as predators when colonisation rate was high, but as herbivores when colonisation rate was low. Human land use is changing habitat connectivity worldwide. A deeper quantitative understanding of how spatial processes modify individual behaviour, and how this scales to the community level, will be required to predict ecosystem responses to these changes.

Fire management, managed relocation, and land conservation options for long-lived obligate seeding plants under global changes in climate, urbanization, and fire regime.

Most species face multiple anthropogenic disruptions. Few studies have quantified the cumulative influence of multiple threats on species of conservation concern, and far fewer have quantified the potential relative value of multiple conservation interventions in light of these threats. We linked spatial distribution and population viability models to explore conservation interventions under projected climate change, urbanization, and changes in fire regime on a long-lived obligate seeding plant species sensitive to high fire frequencies, a dominant plant functional type in many fire-prone ecosystems, including the biodiversity hotspots of Mediterranean-type ecosystems. First, we investigated the relative risk of population decline for plant populations in landscapes with and without land protection under an existing habitat conservation plan. Second, we modeled the effectiveness of relocating both seedlings and seeds from a large patch with predicted declines in habitat area to 2 unoccupied recipient patches with increasing habitat area under 2 projected climate change scenarios. Finally, we modeled 8 fire return intervals (FRIs) approximating the outcomes of different management strategies that effectively control fire frequency. Invariably, long-lived obligate seeding populations remained viable only when FRIs were maintained at or above a minimum level. Land conservation and seedling relocation efforts lessened the impact of climate change and land-use change on obligate seeding populations to differing degrees depending on the climate change scenario, but neither of these efforts was as generally effective as frequent translocation of seeds. While none of the modeled strategies fully compensated for the effects of land-use and climate change, an integrative approach managing multiple threats may diminish population declines for species in complex landscapes. Conservation plans designed to mitigate the impacts of a single threat are likely to fail if additional threats are ignored.

Population persistence in river networks.

Organisms inhabiting river systems contend with downstream biased flow in a complex tree-like network. Differential equation models are often used to study population persistence, thus suggesting resolutions of the 'drift paradox', by considering the dependence of persistence on such variables as advection rate, dispersal characteristics, and domain size. Most previous models that explicitly considered network geometry artificially discretized river habitat into distinct patches. With the recent exception of Ramirez (J Math Biol 65:919-942, 2012), partial differential equation models have largely ignored the global geometry of river systems and the effects of tributary junctions by using intervals to describe the spatial domain. Taking advantage of recent developments in the analysis of eigenvalue problems on quantum graphs, we use a reaction-diffusion-advection equation on a metric tree graph to analyze persistence of a single population in terms of dispersal parameters and network geometry. The metric graph represents a continuous network where edges represent actual domain rather than connections among patches. Here, network geometry usually has a significant impact on persistence, and occasionally leads to dramatically altered predictions. This work ranges over such themes as model definition, reduction to a diffusion equation with the associated model features, numerical and analytical studies in radially symmetric geometries, and theoretical results for general domains. Notable in the model assumptions is that the zero-flux interior junction conditions are not restricted to conservation of hydrological discharge.

Directional biases and resource-dependence in dispersal generate spatial patterning in a consumer-producer model.

Directional dispersal plays a large role in shaping ecological processes in diverse systems such as rivers, coastlines and vegetation communities. We describe an instability driven by directional dispersal in a spatially explicit consumer-producer model where spatial patterns emerge in the absence of external environmental variation. Dispersal of the consumer has both undirected and directed components that are functions of producer biomass. We demonstrate that directional dispersal is required for the instability, while undirected diffusive dispersal sets a lower bound to the spatial scale of emerging patterns. Furthermore, instability requires indirect feedbacks affecting consumer per capita dispersal rates, and not activator-inhibitor dynamics affecting production and mortality as is described in previous theory. This novel and less-restrictive mechanism for generating spatial patterns can arise over realistic parameter values, which we explore using an empirically inspired model and data on stream macroinvertebrates.

Transient responses to spatial perturbations in advective systems.

We study the transient dynamics, following a spatially-extended perturbation of models describing populations residing in advective media such as streams and rivers. Our analyses emphasize metrics that are independent of initial perturbations-resilience, reactivity, and the amplification envelope-and relate them to component spatial wavelengths of the perturbation using spatial Fourier transforms of the state variables. This approach offers a powerful way of understanding the influence of spatial scale on the initial dynamics of a population following a spatially variable environmental perturbation, an important property in determining the ecological implications of transient dynamics in advective systems. We find that asymptotically stable systems may exhibit transient amplification of perturbations (i.e., have positive reactivity) for some spatial wavelengths and not others. Furthermore, the degree and duration of amplification varies strongly with spatial wavelength. For two single-population models, there is a relationship between transient dynamics and the response length that characterizes the steady state response to spatial perturbations: a long response length implies that peak amplification of perturbations is small and occurs fast. This relationship holds less generally in a specialist consumer-resource model, likely due to the model's tendency for flow-induced instabilities at an alternative characteristic spatial scale.

Response of equilibrium states to spatial environmental heterogeneity in advective systems.

Much ecological research involves identifying connections between abiotic forcing and population densities or distributions. We present theory that describes this relationship for populations in media with strong unidirectional flow (e.g., aquatic organisms in streams and rivers). Typically, equi librium populations change in very different ways in response to changes in demographic versus dispersal rates and to changes over local versus larger spatial scales. For populations in a mildly heterogeneous environment, there is a population ''response length'' that characterizes the distance downstream over which the impact of a point source perturbation is felt. The response length is also an important parameter for characterizing the response to non point source disturbances at different spatial scales. In the absence of density dependence, the response length is close to the mean distance traveled by an organism in its lifetime. Density-dependent demographic rates are likely to increase the response length from this default value, and density-dependent dispersal will reduce it. Indirect density dependence, mediated by predation, may also change the response length, the direction of change depending on the strength of the prey's tendency to flee the predator.

Spatial scaling of consumer-resource interactions in advection-dominated systems.

Ecologists studying consumer-resource interactions in advection-dominated systems such as streams and rivers frequently seek to link the results of small-scale experiments with larger-scale patterns of distribution and abundance. Accomplishing this goal requires determining the characteristic scale, termed the response length, at which there is a shift from local dynamics dominated by advective dispersal to larger-scale dynamics dominated by births and deaths. Here, we model the dynamics of consumer-resource systems in a spatially variable, advective environment and show how consumer-resource interactions alter the response length relative to its single-species value. For one case involving a grazer that emigrates in response to high predator density, we quantify the changes using published data from small-scale experiments on aquatic invertebrates. Using Fourier analysis, we describe the responses of advection-dominated consumer-resource systems to spatially extended environmental variability in a way that involves explicit consideration of the response length. The patterns we derive for different consumer-resource systems exhibit important similarities in how component populations respond to spatial environmental variability affecting dispersal as opposed to demographic parameters.

Scaling population responses to spatial environmental variability in advection-dominated systems.

We model the spatial dynamics of an open population of organisms that disperse solely through advection in order to understand responses to multiscale environmental variability. We show that the distance over which a population responds to a localized perturbation, called the response length, can be characterized as an organisms average lifetime dispersal distance, unless there is strong density-dependence in demographic or dispersal rates. Continuous spatial fluctuations in demographic rates at scales smaller than the response length will be largely averaged in the population distribution, whereas those in per capita emigration rates will be strongly tracked. We illustrate these results using a parameterized example to show how responses to environmental variability may differ in streams with different average current velocities. Our model suggests an approach to linking local dynamics dominated by dispersal processes to larger-scale dynamics dominated by births and deaths.