Evolution of Cooperation in Spatio-Temporal Evolutionary Games with Public Goods Feedback.

In biology, evolutionary game-theoretical models often arise in which players' strategies impact the state of the environment, driving feedback between strategy and the surroundings. In this case, cooperative interactions can be applied to studying ecological systems, animal or microorganism populations, and cells producing or actively extracting a growth resource from their environment. We consider the framework of eco-evolutionary game theory with replicator dynamics and growth-limiting public goods extracted by population members from some external source. It is known that the two sub-populations of cooperators and defectors can develop spatio-temporal patterns that enable long-term coexistence in the shared environment. To investigate this phenomenon and unveil the mechanisms that sustain cooperation, we analyze two eco-evolutionary models: a well-mixed environment and a heterogeneous model with spatial diffusion. In the latter, we integrate spatial diffusion into replicator dynamics. Our findings reveal rich strategy dynamics, including bistability and bifurcations, in the temporal system and spatial stability, as well as Turing instability, Turing-Hopf bifurcations, and chaos in the diffusion system. The results indicate that effective mechanisms to promote cooperation include increasing the player density, decreasing the relative timescale, controlling the density of initial cooperators, improving the diffusion rate of the public goods, lowering the diffusion rate of the cooperators, and enhancing the payoffs to the cooperators. We provide the conditions for the existence, stability, and occurrence of bifurcations in both systems. Our analysis can be applied to dynamic phenomena in fields as diverse as human decision-making, microorganism growth factors secretion, and group hunting.

A novel micro-CT approach for in situ visualization of the spatial dynamics of mesovoids in aerobic composting piles.

The spatial configuration of mesovoids profoundly affects the aerobic composting microenvironment, which governs vital processes such as greenhouse gas production and emission, thermal conduction, and overall composting efficiency. Nondestructive in-situ characterization of the composting spatial structure is crucial to better understand its interaction mechanism with the microenvironment. In this study, a valuable contribution to the field of composting research was made by introducing micro-computed tomography (micro-CT) tool for in situ three-dimensional (3D) visual characterizing the void structure dynamics of straw and manure compost pile units at the mesoscale. Representative samples at different composting stages derived from wheat straw and cow manure were procured by pre-embedding samplers in laboratory-based aerobic composting reactor systems. Based on an advanced Skyscan 1275 micro-CT system, scanning conditions and image processing algorithms were determined, and the void structure and their dynamic changes in the pile unit during composting were in-situ 3D visualized for the first time. The micro-CT images effectively reveal well-developed void structures exhibiting spatiotemporal dynamics during composting, and they exhibit excellent consistency with conventional macrophysical effects and wet chemical analyses. Micro-CT quantification results of the void structure parameters changes in pile unit during composting were as follows: percentage of the total voidage and the connected voidage in pile unit were in the range of 52.34%-58.56%, indicating a very suitable composting spatial structural microenvironment. This new micro-CT method provides a valuable perspective for analyzing and understanding the complex aerobic composting process.

Past and future land use change dynamics: assessing the impact of urban development on agricultural land in the Pantura Jabar region, Indonesia.

The conversion of large-scale agricultural land into urban areas poses a significant challenge to achieving national and global food security targets, as outlined in Sustainable Development Goal number 2, which aims to eradicate hunger. Indonesia has experienced a significant decline in rice field areas, with a reduction of approximately 650 thousand hectares within a year (2017-2018), the largest being in Java. Hence, this study aims to examine the impact of urban expansion on agricultural land in the north coast region of West Java Province from 2013 to 2020 and develop a predictive model for 2030 to support sustainable land use planning. The primary methods employed were random forest (RF) analysis using Google Earth Engine, intensity analysis, multilayer perceptron-neural network (MLP-NN), Markov chains-cellular automata (Markov-CA), and stakeholder interviews. The model also evaluated the influence of "distance to tollgates" as a previously unexplored driving factor in existing land use modeling studies. Landsat image classification results using the RF algorithm showed 87-88% accuracy. Cropland has historically been and is projected to remain the primary target for the expansion of built-up areas. Spatial planning irregularities were found in the growth of these areas that adversely affected farmers' socioeconomic and environmental conditions. Evaluation of land use models using MLP-NN and Markov-CA demonstrated an accuracy rate of 86.29-86.23%. The distance to tollgates factor significantly impacts the models, albeit less than population density. The 2030 intervention scenario, which implements a firm policy for sustainable agricultural land use, offers the potential to maintain the predicted cropland loss compared to business as usual.

Introducing desirable patches to initiate ecosystem transitions and accelerate ecosystem restoration.

Meeting restoration targets may require active strategies to accelerate natural regeneration rates or overcome the resilience associated with degraded ecosystem states. Introducing desired ecosystem patches in degraded landscapes constitutes a promising active restoration strategy, with various mechanisms potentially causing these patches to become foci from which desired species can re-establish throughout the landscape. This study considers three mechanisms previously identified as potential drivers of introduced patch dynamics: autocatalytic nucleation, directed dispersal, and resource concentration. These mechanisms reflect qualitatively different positive feedbacks. We developed an ecological model framework that compared how the occurrence of each mechanism was reflected in spatio-temporal patch dynamics. We then analyzed the implications of these relationships for optimal restoration design. We found that patch expansion accelerated over time when driven by the autocatalytic nucleation mechanism, while patch expansion driven by the directed dispersal or resource concentration mechanisms decelerated over time. Additionally, when driven by autocatalytic nucleation, patch expansion was independent of patch position in the landscape. However, the proximity of other patches affected patch expansion either positively or negatively when driven by directed dispersal or resource concentration. For autocatalytic nucleation, introducing many small patches was a favorable strategy, provided that each individual patch exceeded a critical patch size. Introducing a single patch or a few large patches was the most effective restoration strategy to initiate the directed dispersal mechanism. Introducing many small patches was the most effective strategy for reaching restored ecosystem states driven by a resource concentration mechanism. Our model results suggest that introducing desirable patches can substantially accelerate ecosystem restoration, or even induce a critical transition from an otherwise stable degraded state toward a desired ecosystem state. However, the potential of this type of restoration strategy for a particular ecosystem may strongly depend on the mechanism driving patch dynamics. In turn, which mechanism drives patch dynamics may affect the optimal spatial design of an active restoration strategy. Each of the three mechanisms considered reflects distinct spatio-temporal patch dynamics, providing novel opportunities for empirically identifying key mechanisms, and restoration designs that introduce desired patches in degraded landscapes according to these patch dynamics.

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.

Occurrence and distribution of plastic particles (10-25,000 µm) and microfibers in the surface water of an urban river network in Japan.

Urban rivers remain the key conduits conveying land-sourced plastics into the ocean. However, detailed information is limited on the concurrent evaluation over a wide array of particle size-specific abundances, characteristics, and distribution patterns of plastics in riverine environments. Therefore, this study provides a comprehensive assessment of plastic pollution in an urban river network in Japan by analyzing mesoplastics (5000-25,000 µm), large microplastics (300-5000 µm), small microplastics (SMPs, 10-300 µm), and microplastic-fibers (MPFs, 10-5000 µm) concurrently, for the first time. Sampling was conducted at seven stations in the Kamo and Katsura Rivers flowing across metropolitan Kyoto City. The analytical procedures involved infrared spectroscopy and fluorescence-staining microscopy. The concentrations of plastics were moderate compared to the global reports and gradually increased along the river flow (3550-15,840 items/m3; 180-13,180 µg/m3), mostly due to urban discharges via non-point sources. The number concentrations increased with decreasing particle size, marking 99.94% of SMPs, including 50% smaller than 40 µm. Conversely, mass concentrations decreased, exhibiting 96% larger than 1000 µm (64% mesoplastics including 20% around 5000 µm), along with 2% SMPs. Polyethylene (PE) and polyvinyl alcohol were distinct among SMPs, with PE indicating higher susceptibility to fragmentation compared to polypropylene and other polymer types. MPF concentrations were homogeneous throughout the watershed (1470-3600 items/m3; 520-1060 µg/m3), with a higher proportion of fibers smaller than 1000 µm (86%), apparently originating from polyethylene terephthalate/nylon/acrylic-like textile fibers. The proportion of MPFs surpassed particles within 100-3000 µm and was considerably high around 300 µm (> 98%). The river network of Kyoto conveys billions of tiny microplastics to the Yodo River, the primary water resource downstream, within a dry day.

J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments.

BACKGROUND: The combined effects of biological variability and measurement-related errors on cancer sequencing data remain largely unexplored. However, the spatio-temporal simulation of multi-cellular systems provides a powerful instrument to address this issue. In particular, efficient algorithmic frameworks are needed to overcome the harsh trade-off between scalability and expressivity, so to allow one to simulate both realistic cancer evolution scenarios and the related sequencing experiments, which can then be used to benchmark downstream bioinformatics methods. RESULT: We introduce a Julia package for SPAtial Cancer Evolution (J-SPACE), which allows one to model and simulate a broad set of experimental scenarios, phenomenological rules and sequencing settings.Specifically, J-SPACE simulates the spatial dynamics of cells as a continuous-time multi-type birth-death stochastic process on a arbitrary graph, employing different rules of interaction and an optimised Gillespie algorithm. The evolutionary dynamics of genomic alterations (single-nucleotide variants and indels) is simulated either under the Infinite Sites Assumption or several different substitution models, including one based on mutational signatures. After mimicking the spatial sampling of tumour cells, J-SPACE returns the related phylogenetic model, and allows one to generate synthetic reads from several Next-Generation Sequencing (NGS) platforms, via the ART read simulator. The results are finally returned in standard FASTA, FASTQ, SAM, ALN and Newick file formats. CONCLUSION: J-SPACE is designed to efficiently simulate the heterogeneous behaviour of a large number of cancer cells and produces a rich set of outputs. Our framework is useful to investigate the emergent spatial dynamics of cancer subpopulations, as well as to assess the impact of incomplete sampling and of experiment-specific errors. Importantly, the output of J-SPACE is designed to allow the performance assessment of downstream bioinformatics pipelines processing NGS data. J-SPACE is freely available at: https://github.com/BIMIB-DISCo/J-Space.jl .

Looking across Scales in Disease Ecology and Evolution.

AbstractOver the past few decades, it has become clear that ecological and evolutionary dynamics are influenced by processes operating across spatial and temporal scales. Processes that operate on small spatial scales have the potential to influence dynamics at much larger scales; for example, a change in the physiology of a primary producer can alter primary productivity in an ecosystem. Similarly, evolution-a process that historically was thought of as occurring at longer timescales-can influence ecological dynamics and vice versa. The importance of considering multiple scales is broadly true in ecology and evolution, and it is especially important for studies of disease ecology and evolution. Yet characterizing the scales at which individual studies operate is surprisingly challenging, as we (re)discovered while trying to characterize articles published in this journal over the past three decades. However, while it is difficult to determine where one scale ends and another begins, it is also clear that work that spans across a spectrum can yield insights that could not be gleaned from a narrower focus. To demonstrate this, we highlight studies previously published in this journal that show the value of working across scales. We then introduce the six articles that comprise this Focused Topic section. Together, these articles present systems, theory, and methods that provide important insights that could not have been obtained from studying a single scale in isolation.

Exploring human mixing patterns based on time use and social contact data and their implications for infectious disease transmission models.

BACKGROUND: The increasing availability of data on social contact patterns and time use provides invaluable information for studying transmission dynamics of infectious diseases. Social contact data provide information on the interaction of people in a population whereas the value of time use data lies in the quantification of exposure patterns. Both have been used as proxies for transmission risks within in a population and the combination of both sources has led to investigate which contacts are more suitable to describe these transmission risks. METHODS: We used social contact and time use data from 1707 participants from a survey conducted in Flanders, Belgium in 2010-2011. We calculated weighted exposure time and social contact matrices to analyze age- and gender-specific mixing patterns and to quantify behavioral changes by distance from home. We compared the value of both separate and combined data sources for explaining seroprevalence and incidence data on parvovirus-B19, Varicella-Zoster virus (VZV) and influenza like illnesses (ILI), respectively. RESULTS: Assortative mixing and inter-generational interaction is more pronounced in the exposure matrix due to the high proportion of time spent at home. This pattern is less pronounced in the social contact matrix, which is more impacted by the reported contacts at school and work. The average number of contacts declined with distance. On the individual-level, we observed an increase in the number of contacts and the transmission potential by distance when travelling. We found that both social contact data and time use data provide a good match with the seroprevalence and incidence data at hand. When comparing the use of different combinations of both data sources, we found that the social contact matrix based on close contacts of at least 4 h appeared to be the best proxy for parvovirus-B19 transmission. Social contacts and exposure time were both on their own able to explain VZV seroprevalence data though combining both scored best. Compared with the contact approach, the time use approach provided the better fit to the ILI incidence data. CONCLUSIONS: Our work emphasises the common and complementary value of time use and social contact data for analysing mixing behavior and analysing infectious disease transmission. We derived spatial, temporal, age-, gender- and distance-specific mixing patterns, which are informative for future modelling studies.

Wolf spatial behavior promotes encounters and kills of abundant prey.

Predators use different spatial tactics to track the prey on the landscape. Three hypotheses describe spatial tactics: prey abundance for prey that are aggregated in space; prey habitat for uniformly distributed prey; and prey catchability for prey that are difficult to catch and kill. The gray wolf (Canis lupus) is a generalist predator that likely employs more than one spatial hunting tactic to match their diverse prey with distinct distributions and behavior that are available. We conducted a study on 17 GPS collared wolves in 6 packs in Riding Mountain National Park, Manitoba, Canada where wolves prey on moose (Alces alces) and elk (Cervus canadensis). We evaluated wolf selection for prey density, habitat selection and catchability on the landscape through within-territory habitat selection analysis. We reveal support for both the prey habitat and prey catchability hypotheses. For moose, their primary prey, wolves employed a mixed habitat and catchability tactic. Wolves used spaces described by the intersection of moose habitat and moose catchability. Wolves selected for the catchability of elk, their secondary prey, but not elk habitat. Counter to our predictions, wolves avoided areas of moose and elk density, likely highlighting the ongoing space race between predator and prey. We illustrate that of the three hypotheses the primary driver was prey catchability, where the interplay of both prey habitat with catchability culminate in predator spatial behaviour in a multiprey system.

Diversity and dynamics in larval digenean assemblages parasitizing Heleobia parchappii in a freshwater shallow lake from the Southeastern Pampa plain, Argentina.

Digeneans have important roles within ecosystems; however, it is estimated that only 14% of the species have been described. Therefore, before being able to detail their role, digenean species' identification and the diversity present in the ecosystems must be known. In this study, the diversity and the temporal-spatial dynamics of larval digeneans in the freshwater snail Heleobia parchappii were analysed in a shallow lake. Specimens of H. parchappii were collected seasonally at three points during one year and a total of 2871 molluscs were analysed. A total of 23 species of digenea were registered and both the overall prevalence and the composition of the assemblages presented temporal and spatial variations, responding to the differential environmental conditions characteristics (anthropic effect, presence of native forests, and differential use of the habitat by the definitive hosts) of three sampled sites. The assemblages of larval digenean in their first intermediate host support the idea that this area is of great importance in biodiversity, and could be endemic areas of some species of digenean that use reptiles, amphibians and bats as hosts, groups that are at risk of conservation. Protection of these environments is a fundamental pillar in the policies for the conservation of wild flora and fauna.

Causal inference in coupled human and natural systems.

Coupled human and natural systems (CHANS) are complex, dynamic, interconnected systems with feedback across social and environmental dimensions. This feedback leads to formidable challenges for causal inference. Two significant challenges involve assumptions about excludability and the absence of interference. These two assumptions have been largely unexplored in the CHANS literature, but when either is violated, causal inferences from observable data are difficult to interpret. To explore their plausibility, structural knowledge of the system is requisite, as is an explicit recognition that most causal variables in CHANS affect a coupled pairing of environmental and human elements. In a large CHANS literature that evaluates marine protected areas, nearly 200 studies attempt to make causal claims, but few address the excludability assumption. To examine the relevance of interference in CHANS, we develop a stylized simulation of a marine CHANS with shocks that can represent policy interventions, ecological disturbances, and technological disasters. Human and capital mobility in CHANS is both a cause of interference, which biases inferences about causal effects, and a moderator of the causal effects themselves. No perfect solutions exist for satisfying excludability and interference assumptions in CHANS. To elucidate causal relationships in CHANS, multiple approaches will be needed for a given causal question, with the aim of identifying sources of bias in each approach and then triangulating on credible inferences. Within CHANS research, and sustainability science more generally, the path to accumulating an evidence base on causal relationships requires skills and knowledge from many disciplines and effective academic-practitioner collaborations.

Tracking spatial dynamics of functional connectivity during a task.

Functional connectivity (FC) measured from functional magnetic resonance imaging (fMRI) provides a powerful tool to explore brain organization. Studies of the temporal dynamics of brain organization have shown a large temporal variability of the functional connectome, which may be associated with mental status transitions and/or adaptive process. Most dynamic studies, e.g. functional connectome and functional network connectivity (FNC), have focused on the macroscopic FC changes, i.e. the changes of temporal coherence across various brain network sources, nodes and/or regions of interest, where it is assumed within the network or node that the FC is static. In this paper, we develop a novel method to examine the spatial dynamics of FC, without the assumption of its intra-network stationarity. We applied our approach to fMRI data during an auditory oddball task (AOD) from twenty-two subjects, in an attempt to capture/validate the approach by evaluating whether spatial connectivity varies with task condition. The results showed that connectivity networks exhibit spatial variability over time, in addition to participating in conventional temporal dynamics, i.e. cross-network variability or dynamic functional network connectivity (dFNC). Furthermore, we studied the relationship of spatial dynamic in FC to cognitive processes, by performing a cluster analysis to evaluate an individual's functional correspondence towards the 'target' (oddball) detection from AOD task, and extracting cognitive task correspondence states as well as their dynamic FC spatial maps segregated by such states. We found a clear trend in different task-guided states, particularly, a prominent reduction of task stimulus synchrony state along with strong anticorrelation between default mode network (DMN) and cognitive attentional networks. We also observed an increasing occurrence of the task desynchrony state which showed an absence of DMN anticorrelation. The results highlight the impact of a well-studied cognitive task on the observed spatial dynamic structure. We also showed that the FC spatial dynamic pattern from our method largely corresponds to macroscopic dFNC patterns, but with more details and specifications over space, meanwhile the connectivity within the source itself provides novel information and varies over time. Overall, we demonstrate clear evidence of the presence of the (usually ignored) spatial dynamics of connectivity, its links to the task and implications of cognition/mental status.

Gene Drive Dynamics in Natural Populations: The Importance of Density Dependence, Space, and Sex.

The spread of synthetic gene drives is often discussed in the context of panmictic populations connected by gene flow and described with simple deterministic models. Under such assumptions, an entire species could be altered by releasing a single individual carrying an invasive gene drive, such as a standard homing drive. While this remains a theoretical possibility, gene drive spread in natural populations is more complex and merits a more realistic assessment. The fate of any gene drive released in a population would be inextricably linked to the population's ecology. Given the uncertainty often involved in ecological assessment of natural populations, understanding the sensitivity of gene drive spread to important ecological factors is critical. Here we review how different forms of density dependence, spatial heterogeneity, and mating behaviors can impact the spread of self-sustaining gene drives. We highlight specific aspects of gene drive dynamics and the target populations that need further research.

Parasitism and host dispersal plasticity in an aquatic model system.

Dispersal is a central determinant of spatial dynamics in communities and ecosystems, and various ecological factors can shape the evolution of constitutive and plastic dispersal behaviours. One important driver of dispersal plasticity is the biotic environment. Parasites, for example, influence the internal condition of infected hosts and define external patch quality. Thus, state-dependent dispersal may be determined by infection status and context-dependent dispersal by the abundance of infected hosts in the population. A prerequisite for such dispersal plasticity to evolve is a genetic basis on which natural selection can act. Using interconnected microcosms, we investigated dispersal in experimental populations of the freshwater protist Paramecium caudatum in response to the bacterial parasite Holospora undulata. For a collection of 20 natural host strains, we found substantial variation in constitutive dispersal and to a lesser degree in dispersal plasticity. First, infection tended to increase or decrease dispersal relative to uninfected controls, depending on strain identity, indicative of state-dependent dispersal plasticity. Infection additionally decreased host swimming speed compared to the uninfected counterparts. Second, for certain strains, there was a weak negative association between dispersal and infection prevalence, such that uninfected hosts dispersed less when infection was more frequent in the population, indicating context-dependent dispersal plasticity. Future experiments may test whether the observed differences in dispersal plasticity are sufficiently strong to be picked up by natural selection. The evolution of dispersal plasticity as a strategy to mitigate parasite effects spatially may have important implications for epidemiological dynamics.

Coordinated Metacommunity Assembly and Spatial Distribution of Multiple Microbial Kingdoms within a Lake.

Freshwater planktonic communities comprise a tremendous diversity of microorganisms. This study investigated the distribution patterns of microbial kingdoms (bacteria, fungi, protists, and microbial metazoans) within a lake ecosystem. Water samples were collected from 50 sites along the shoreline in a lake during an early eutrophication period, and MiSeq sequencing was performed with different marker genes. Metacommunity analyses revealed a bimodal occupancy-frequency distribution and a Clementsian gradient persisting throughout all microbial kingdoms, suggesting similar regional processes in all kingdoms. Variation partitioning revealed that environmental characteristics, macrophyte/macroinvertebrate composition, space coordinates, and distance-based Moran's eigenvector maps (dbMEM) together could explain up to 29% of the community variances in microbial kingdoms. Kingdom synchrony results showed strong couplings between kingdoms (R2 ≥ 0.31), except between Fungi and Metazoa (R2 = 0.09). Another variation partitioning revealed that microbial kingdoms could well explain their community variances up to 73%. Interestingly, the kingdom Protista was best synchronized with the other kingdoms. A correlation network showed that positive associations between kingdoms outnumbered the negative ones and that the kingdom Protista acted as a hub among kingdoms. Module analysis showed that network modules included multi-kingdom associations that were prevalent. Our findings suggest that protists coordinate community assembly and distribution of other kingdoms, and inter-kingdom interactions are a key determinant in shaping their community structures in a freshwater lake.

Spatial Dynamics and Spread of Ecosystem Engineers: Two Patch Analysis.

Ecosystem engineers are organisms characterized by interacting with other organisms thorough physical modifications or modifying their habitat. Examples of ecosystem engineers include Spartina alterniflora cordgrass or the zebra mussel Dreissena polymorpha. For both of these, the effect of modifying the environment can be nonlocal, affecting other regions farther away from the region populated by the ecosystem engineer. This shows the importance of understanding the population dynamics of ecosystem engineers in a spatial context. To do this, we have developed an extension of the ecosystem engineer population model of Cuddington et al. (Am Natur, 2009. https://doi.org/10.1086/597216 ) to the simplest spatial model, incorporating two local populations. We use this model to understand the relationship between dispersal and engineering effects, both at local and regional scales. Our main result is that the delayed Allee effect induced in the nonspatial model is extended to the spatial model, so the spread dynamics of an ecosystem engineer can be similar to the Allee case. However, there are more complex possibilities due to the two components of the dynamics. We also find quantitative guidelines that explain the interaction between spread of the environment modification and organism spread.

When Does Spatial Diversification Usefully Maximize the Durability of Crop Disease Resistance?

Maximizing the durability of crop disease resistance genes in the face of pathogen evolution is a major challenge in modern agricultural epidemiology. Spatial diversification in the deployment of resistance genes, where susceptible and resistant fields are more closely intermixed, is predicted to drive lower epidemic intensities over evolutionary timescales. This is due to an increase in the strength of dilution effects, caused by pathogen inoculum challenging host tissue to which it is not well-specialized. The factors that interact with and determine the magnitude of this spatial suppressive effect are not currently well understood, however, leading to uncertainty over the pathosystems where such a strategy is most likely to be cost-effective. We model the effect on landscape scale disease dynamics of spatial heterogeneity in the arrangement of fields planted with either susceptible or resistant cultivars, and the way in which this effect depends on the parameters governing the pathosystem of interest. Our multiseason semidiscrete epidemiological model tracks spatial spread of wild-type and resistance-breaking pathogen strains, and incorporates a localized reservoir of inoculum, as well as the effects of within and between field transmission. The pathogen dispersal characteristics, any fitness cost(s) of the resistance-breaking trait, the efficacy of host resistance, and the length of the timeframe of interest all influence the strength of the spatial diversification effect. A key result is that spatial diversification has the strongest beneficial effect at intermediate fitness costs of the resistance-breaking trait, an effect driven by a complex set of nonlinear interactions. On the other hand, however, if the resistance-breaking strain is not fit enough to invade the landscape, then a partially effective resistance gene can result in spatial diversification actually worsening the epidemic. These results allow us to make general predictions of the types of system for which spatial diversification is most likely to be cost-effective, paving the way for potential economic modeling and pathosystem specific evaluation. These results highlight the importance of studying the effect of genetics on landscape scale spatial dynamics within host-pathogen disease systems.[Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Seafood prices reveal impacts of a major ecological disturbance.

Coastal hypoxia (dissolved oxygen ≤ 2 mg/L) is a growing problem worldwide that threatens marine ecosystem services, but little is known about economic effects on fisheries. Here, we provide evidence that hypoxia causes economic impacts on a major fishery. Ecological studies of hypoxia and marine fauna suggest multiple mechanisms through which hypoxia can skew a population's size distribution toward smaller individuals. These mechanisms produce sharp predictions about changes in seafood markets. Hypoxia is hypothesized to decrease the quantity of large shrimp relative to small shrimp and increase the price of large shrimp relative to small shrimp. We test these hypotheses using time series of size-based prices. Naive quantity-based models using treatment/control comparisons in hypoxic and nonhypoxic areas produce null results, but we find strong evidence of the hypothesized effects in the relative prices: Hypoxia increases the relative price of large shrimp compared with small shrimp. The effects of fuel prices provide supporting evidence. Empirical models of fishing effort and bioeconomic simulations explain why quantifying effects of hypoxia on fisheries using quantity data has been inconclusive. Specifically, spatial-dynamic feedbacks across the natural system (the fish stock) and human system (the mobile fishing fleet) confound "treated" and "control" areas. Consequently, analyses of price data, which rely on a market counterfactual, are able to reveal effects of the ecological disturbance that are obscured in quantity data. Our results are an important step toward quantifying the economic value of reduced upstream nutrient loading in the Mississippi Basin and are broadly applicable to other coupled human-natural systems.

Monitoring Soil Moisture Dynamics Using Electrical Resistivity Tomography under Homogeneous Field Conditions.

There is a gap between lab experiments where resistivity-soil moisture relations are generally very good and field studies in complex environmental settings where relations are always less good and complicated by many factors. An experiment was designed where environmental settings are more controlled, the best outside laboratory, to assess the transferability from lab to outdoor. A field experiment was carried out to evaluate the use of electric resistivity tomography (ERT) for monitoring soil moisture dynamics over a period of 67 days. A homogeneous site in the central part of The Netherlands was selected consisting of grass pasture on an aeolian sand soil profile. ERT values were correlated to gravimetric soil moisture samples for five depths at three different dates. Correlations ranged from 0.43 to 0.73 and were best for a soil depth of 90 cm. Resistivity patterns over time (time-lapse ERT) were analyzed and related to rainfall events where rainfall infiltration patterns could be identified. Duplicate ERT measurements showed that the noise level of the instrument and measurements is low and generally below 3% for the soil profile below the mixed layer but above the groundwater. Although the majority of the measured resistivity patterns could be well explained, some artefacts and dynamics were more difficult to clarify, even so in this homogeneous field situation. The presence of an oak tree with its root structure and a ditch with surface water with higher conductivity may have an impact on the resistivity pattern in the soil profile and over time. We conclude that ERT allows for detailed spatial measurement of local soil moisture dynamics resulting from precipitation although field experiments do not yield accuracies similar to laboratory experiments. ERT approaches are suitable for detailed spatial analyses where probe or sample-based methods are limited in reach or repeatability.