Models Of Species Interactions Research Articles

Ecological integration of Vespa velutina Lepeletier, 1836 in South Korea: a comprehensive study of co-occurrence and interaction patterns with native social wasps.

The spread of Vespa velutina Lepeletier, 1836 (Asian hornet) in South Korea poses a threat to biodiversity and agriculture. Traditional species interaction models often fail to capture the complexity of invasive species dynamics. This study used association rule learning (ARL) and clustering analyses to explore the interactions of V. velutina with native Vespidae species in South Korea, with the aim of uncovering patterns of coexistence and competition. Over 4 years (2020-2023), 304 traps were strategically placed across South Korea to collect data on Vespidae species. Our findings showed that Vespula flaviceps, Vespa crabro, and Vespula koreensis were the most frequently encountered species. V. velutina was also widespread, suggesting its successful integration into local ecosystems. The ARL analysis, using the 'apriori' algorithm, identified significant co-occurrence patterns and potential interactions. The rules generated indicated both competitive and coexistent relationships with varying levels of association strength across different regions. Clustering analyses, including hierarchical and k-means clustering, grouped species based on their occurrence similarities. The distinct clusters formed in the analysis highlighted the unique ecological roles and interactions of V. velutina and other Vespidae species in South Korean ecosystems. This study confirms the successful establishment of V. velutina in diverse South Korean ecosystems and highlights its complex interactions with native species. These findings support the need for nuanced management strategies that consider the intricate ecological relationships of V. velutina. This approach is crucial for the effective management of invasive species and ecosystem conservation. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

American marten occupancy and activity patterns at the southern extent of their range in the eastern United States.

The relatively recent rediscovery of an American marten (Martes americana) population that was reintroduced over 30 years ago in southern Vermont provides an opportunity to investigate the relative importance of other mesocarnivores, and forest stand (e.g., DBH, downed logs, vertical structure) and habitat variables to their presence on the Green Mountain National Forest. Marten are state-listed as an endangered species in Vermont and occur there at the southern extent of their range in the eastern United States. We collected detection data from camera surveys in 5 km2 units between 2019 and 2021 (December-April; n = 40 units, 238 cameras). We examined activity patterns and applied an occupancy modeling framework to the detection data to assess the relative importance of covariates at unit and camera levels and assess interactions of marten with other mesocarnivores. We did not find any unit-level occupancy models with significant covariates that were better supported than the base model in the single-season unit-level analysis. Distance to the nearest release site was the covariate most supported for detectability at both spatial scales, and marten occupancy at the camera level was positively influenced by the amount of canopy cover. Two species interaction models did not indicate any positive or negative association beyond random with other mesocarnivores and activity patterns among mesocarnivores had substantial overlap. Marten recovery since the time of the reintroduction appears slow, and even 30 years later, the marten distribution is limited and suggests that dispersal is restricted at some level. We recommend a further investigation of the possible impact of other mesocarnivores to juvenile survival or other vital demographic rate (e.g., recruitment) in marten that were not explicitly measured in this study.

Forced sympatry? Spatiotemporal interactions of ursids, the Himalayan brown bear and the Asiatic black bear, along a gradient of anthropic disturbances in Western Himalaya

AbstractThe coexistence of species with similar ecological niches implies that species must segregate along one or more niche axes in order to avoid competition. Partitioning of time, space, or resources is an essential niche dimension along which competitor species tend to coexist. The intensity of interspecific competition may vary under varying anthropogenic pressures, but such knowledge is scarce. We investigated the co‐occurrence patterns of two bear species, Asiatic Black Bear Ursus thibetanus and Himalayan Brown Bear Ursus arctos isabellinus, at two sites in the Western Himalayan region of India with similar ecological conditions and varying anthropogenic pressures. Camera trap datasets from the Great Himalayan National Park (GHNP) in Himachal Pradesh and the Bhagirathi basin in Uttarakhand were used to assess the species interactions. We used two species interaction models and kernel density estimates to understand the co‐occurrence pattern of the bear species and their interactions with humans. Our results provide insights into the spatiotemporal behavior of the two bear species and reveal their sympatric and allopatric relationships in two different anthropogenic environments. We found (1) a prominent association between the two bear species (98%) indicating spatial overlap in GHNP, but found spatial separation among the two species (93%) in Bhagirathi basin (2). In GHNP, the spatially co‐occurring bear species were found to be temporarily segregated, whereas in Bhagirathi, they showed spatial avoidance and similar activity patterns (3). In GHNP, human activities in the alpine habitat had a significant negative impact on brown bear presence. The comparative study revealed that the brown bear uses the forested habitats of GHNP due to high anthropogenic disturbances in the alpine areas, whereas no spatial association was observed in the Bhagirathi basin with comparatively low anthropogenic disturbances. The results highlight that, in addition to biological factors, anthropogenic pressures can also influence interspecific competition.

Monitoring the fabric of nature: using allometric trophic network models and observations to assess policy effects on biodiversity.

Species diversity underpins all ecosystem services that support life. Despite this recognition and the great advances in detecting biodiversity, exactly how many and which species co-occur and interact, directly or indirectly in any ecosystem is unknown. Biodiversity accounts are incomplete; taxonomically, size, habitat, mobility or rarity biased. In the ocean, the provisioning of fish, invertebrates and algae is a fundamental ecosystem service. This extracted biomass depends on a myriad of microscopic and macroscopic organisms that make up the fabric of nature and which are affected by management actions. Monitoring them all and attributing changes to management policies is daunting. Here we propose that dynamic quantitative models of species interactions can be used to link management policy and compliance with complex ecological networks. This allows managers to qualitatively identify 'interaction-indicator' species, which are highly impacted by management policies through propagation of complex ecological interactions. We ground the approach in intertidal kelp harvesting in Chile and fishers' compliance with policies. Results allow us to identify sets of species that respond to management policy and/or compliance, but which are often not included in standardized monitoring. The proposed approach aids in the design of biodiversity programmes that attempt to connect management with biodiversity change. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

Non-constant steady states and Hopf bifurcation of a species interaction model

In this paper, we are concerned with the species interaction model with the ratio-dependent Holling III functional response and strong Allee effect. To explore nonhomogeneous solutions of the model, we consider the existence and non-existence of non-constant steady states and temporal bifurcation. Then we show the boundedness of the global positive solutions for the parabolic system and present the upper and lower bounds of positive solutions for the associated elliptic system. The non-existence and existence of the non-constant steady states of the elliptic system with the homogeneous Neumann boundary conditions are obtained by using the priori estimates, maximum principle and index theory. Furthermore, the existence and the direction of Hopf bifurcation are investigated via the stability analysis, center manifold theory and normal form reduction. Numerical simulations are carried out to verify our theoretical analysis and to illustrate that the ratio-dependent Holling III functional response and strong Allee effect have strong impact on dynamical behaviors of the species interaction systems.

Discretization and chaos control in a fractional order predator-prey harvesting model

The study of interaction between predator and prey species is one of the important subjects in mathematical biology. Optimal strategy control plays a vital role in preserving animals from extinction. Harvesting of species is a vital issue for the conservation biologists. In this work, we investigate the bifurcation and chaos control of the two species interaction model of fractional order in discrete time with harvesting of both prey and predator species. Existence results and the stability conditions of the system are analyzed using the fixed points and jacobian matrix. The chaotic behavior of the system is discussed with bifurcation diagrams. Linear control and hybrid control methods are used in controlling the chaos of the system. Numerical experiments with different phase portraits are simulated for the better understanding of the qualitative behavior of the considered model.

Coupled Thermal-Hydraulic Analysis and Species Mass Transport in a Versatile Experimental Salt Irradiation Loop (VESIL) Using CTF

With the resurgence of interest in molten salt reactors, there is a need for new experiments and modeling capabilities to characterize the unique phenomena present in this fluid fuel system. A Versatile Experimental Salt Irradiation Loop (VESIL) is currently under investigation at Idaho National Laboratory to be placed in the Advanced Test Reactor (ATR). One of the key phenomena this proposed experiment plans to elucidate is fission product speciation in the fuel-salt and the subsequent effects this has on the fuel-salt properties, source term generation, and corrosion control. Specifically, noble gases (Xe & Kr) will bubble out to a plenum or off-gas system, and noble metals (Mo, Tc, Te, etc.) will precipitate and deposit in specific zones in the loop. This work extends the mass transfer and species interaction models in CTF (Coolant-Boiling in Rod Arrays—Two Fluids) and applies these models to give a preliminary estimation of fission product behavior in the proposed VESIL design. A noble metal–helium bubble mass transfer model is coupled with the thermal-hydraulic results from CTF to determine the effectiveness of this insoluble fission product (IFP) extraction method for VESIL. Amounts of IFP species extracted to the off-gas system and species distributions in VESIL after a 60-day ATR cycle are reported.

Species patch size at seeding affects the productivity of mixed legume-grass communities

The impact of inter- and intraspecific neighboring plants on mixed legume-grass communities has rarely been explored in relation to seeded species patch size. In this study, two native perennial species, the legume alfalfa (Medicago sativa L.) and the grass tall fescue (Festuca arundinacea L.), were investigated as monocultures and in mixture. A three-year growth experiment was conducted to investigate the effects of plant-plant competitive interactions on fine-scale seeding patterns: monoculture, three different conspecific patch sizes (1.0, 0.5, and 0.25 m side length of squares) and a control in which the seeds were mixed and scattered (i.e., patches were not formed) as in conventional seeding. The results demonstrated significant differences in the mutual effect intensity in all conspecific patch sizes, indicating the presence of grass-legume interactions on mixed plant communities. Smaller patch sizes resulted in better facilitation by higher neighbor effect intensity when compared with a larger patch size and the conventional mixture. Seedings in the smallest patch size of 0.25 m × 0.25 m showed intra- and interspecific competition and significantly improved aboveground productivity compared with the other patch sizes. We directly quantified the variation of species neighbor effect intensity between grass and legume mixtures among different species patch sizes at seeding. Integrating this knowledge into species interaction models in plant community ecology could greatly enhance our understanding of species coexistence in grasslands as well as provide opportunities for manipulating competition to achieve specific agronomic aims.

Drought and Nitrogen Application Modulate the Morphological and Physiological Responses of Dalbergia odorifera to Different Niche Neighbors.

Mixed stands can be more productive if growth facilitation via niche segregation occurs. Dalbergia odorifera T. Chen, a tropical tree species endemic to Hainan Island with great economic values, belongs to the family Leguminosae. However, selecting mixed species with suitable ecological niches to efficiently construct mixed forests of D. odorifera in the context of abiotic stress [drought, nitrogen (N) deposition] remained obscure. In the present study, the target plant D. odorifera was planted with the same species D. odorifera, heterogeneous but the same family Delonix regia and non-Leguminous Family Swietenia mahagoni in the root interaction and isolated models under two watering regimes [100% and 30% field capacity (FC)] and two N applications (application, non-application), respectively. Principle component analysis based on the performances of growth, phenotype, and physiology was performed to identify the main factors affected by the treatments and the most discriminatory effects of water, N level, and species interaction models. Both comprehensive evaluation values and comprehensive index values were calculated to evaluate the influences of different niche neighbors on D. odorifera. Results showed that D. odorifera was benefited from S. mahagoni but inhibited from D. odorifera in all treatments under root system interaction. Drought stress aggravated the inhibitory effects on D. odorifera from D. odorifera. N application stimulated the promoted effects on D. odorifera from S. mahagoni but enhanced competition intensity of D. odorifera from D. regia under the 100% FC condition. N application alleviated the inhibitory effect of drought stress on D. odorifera from D. odorifera and S. mahagoni. Furthermore, the responses of D. odorifera to different niche neighbors were dominated by belowground interaction rather than the negligible aboveground one. Therefore, the feasibility of niche segregation as the criterion for selecting neighbors to construct D. odorifera mixed stands was confirmed. In addition, water level and N application could alter responses of D. odorifera to different niche neighbors under the root system interaction. Appropriate N application could alleviate the inhibitory effect of drought stress on D. odorifera in its mixed forests. A mixture with S. mahagoni under appropriate N application could be the optimal planting model.

The need for alternative plant species interaction models

Abstract Aims The limitations of classical Lotka–Volterra models for analyzing and interpreting competitive interactions among plant species have become increasingly clear in recent years. Three of the problems that have been identified are (i) the absence of frequency-dependence, which is important for long-term coexistence of species, (ii) the need to take unmeasured (often unmeasurable) variables influencing individual performance into account (e.g. spatial variation in soil nutrients or pathogens) and (iii) the need to separate measurement error from biological variation. Methods We modified the classical Lotka–Volterra competition models to address these limitations. We fitted eight alternative models to pin-point cover data on Festuca ovina and Agrostis capillaris over 3 years in an herbaceous plant community in Denmark. A Bayesian modeling framework was used to ascertain whether the model amendments improve the performance of the models and increase their ability to predict community dynamics and to test hypotheses. Important Findings Inclusion of frequency-dependence and measurement error, but not unmeasured variables, improved model performance greatly. Our results emphasize the importance of comparing alternative models in quantitative studies of plant community dynamics. Only by considering possible alternative models can we identify the forces driving community assembly and change, and improve our ability to predict the behavior of plant communities.

Climate Change-Driven Regime Shifts in a Planktonic Food Web.

AbstractPredicting how food webs will respond to global environmental change is difficult because of the complex interplay between the abiotic forcing and biotic interactions. Mechanistic models of species interactions in seasonal environments can help understand the effects of global change in different ecosystems. Seasonally ice-covered lakes are warming faster than many other ecosystems and undergoing pronounced food web changes, making the need to forecast those changes especially urgent. Using a seasonally forced food web model with a generalist zooplankton grazer and competing cold-adapted winter and warm-adapted summer phytoplankton, we show that with declining ice cover, the food web moves through different dynamic regimes, from annual to biennial cycles, with decreasing and then disappearing winter phytoplankton blooms and a shift of maximum biomass to summer season. Interestingly, when predator-prey interactions were not included, a declining ice cover did not cause regime shifts, suggesting that both are needed for regime transitions. A cluster analysis of long-term data from Lake Baikal, Siberia, supports the model results, revealing a change from regularly occurring winter blooms of endemic diatoms to less frequent winter bloom years with decreasing ice cover. Together, the results show that even gradual environmental change, such as declining ice cover duration, may cause discontinuous or abrupt transitions between dynamic regimes in food webs.

The cichlid–Cichlidogyrus network: a blueprint for a model system of parasite evolution

Species interactions are a key aspect of evolutionary biology. Parasites, specifically, are drivers of the evolution of species communities and impact biosecurity and public health. However, when using interaction networks for evolutionary studies, interdependencies between distantly related species in these networks are shaped by ancient and complex processes. We propose using recent interacting host–parasite radiations, e.g. African cichlid fishes and cichlid gill parasites belonging to Cichlidogyrus (Dactylogyridae, Monogenea), as macroevolutionary model of species interactions. The cichlid–Cichlidogyrus network encompasses 138 parasite species and 416 interactions identified through morphological characteristics and genetic markers in 160 publications. We discuss the steps required to develop this model system based on data resolution, sampling bias, and reporting quality. In addition, we propose the following steps to guide efforts for a macroevolutionary model system for species interactions: first, evaluating and expanding model system outcome measures to increase data resolution; second, closing knowledge gaps to address underreporting and sampling bias arising from limited human and financial resources. Identifying phylogenetic and geographic targets, creating systematic overviews, enhancing scientific collaborations, and avoiding data loss through awareness of predatory journal publications can accelerate this process; and third, standardising data reporting to increase reporting quality and to facilitate data accessibility.

Modeling the Growth and Interaction Between Brochothrix thermosphacta, Pseudomonas spp., and Leuconostoc gelidum in Minced Pork Samples.

The aim of this study was to obtain the growth parameters of specific spoilage micro-organisms previously isolated in minced pork (MP) samples and to develop a three-spoilage species interaction model under different storage conditions. Naturally contaminated samples were used to validate this approach by considering the effect of the food microbiota. Three groups of bacteria were inoculated on irradiated samples, in mono- and in co-culture experiments (n = 1152): Brochothrix thermosphacta, Leuconostoc gelidum, and Pseudomonas spp. (Pseudomonas fluorescens and Pseudomonas fragi). Samples were stored in two food packaging [food wrap and modified atmosphere packaging (CO2 30%/O2 70%)] at three isothermal conditions (4, 8, and 12°C). Analysis was carried out by using both 16S rRNA gene amplicon sequencing and classical microbiology in order to estimate bacterial counts during the storage period. Growth parameters were obtained by fitting primary (Baranyi) and secondary (square root) models. The food packaging shows the highest impact on bacterial growth rates, which in turn have the strongest influence on the shelf life of food products. Based on these results, a three-spoilage species interaction model was developed by using the modified Jameson-effect model and the Lotka Volterra (prey–predator) model. The modified Jameson-effect model showed slightly better performances, with 40–86% out of the observed counts falling into the Acceptable Simulation Zone (ASZ). It only concerns 14–48% for the prey–predator approach. These results can be explained by the fact that the dynamics of experimental and validation datasets seems to follow a Jameson behavior. On the other hand, the Lotka Volterra model is based on complex interaction factors, which are included in highly variable intervals. More datasets are probably needed to obtained reliable factors, and so better model fittings, especially for three- or more-spoilage species interaction models. Further studies are also needed to better understand the interaction of spoilage bacteria between them and in the presence of natural microbiota.

Not all disturbances are created equal: disturbance magnitude affects predator–prey populations more than disturbance frequency

Disturbance plays a key role in ecological structure and function. Two important and often studied components of disturbance are frequency and magnitude. Despite the potential for non‐linear interactions between frequency and magnitude, their effects are often assumed to combine in a linear manner. Additionally, studies of disturbance have mainly examined effects on species diversity and competitive interactions within a single trophic level, with less focus on exploitative interactions across trophic levels. Furthermore, while the effects of disturbance are often viewed in terms of reducing population abundance, disturbances can also alter demographic processes, ‘indirectly’ changing abundances. We analyzed several classic dynamic models of species interactions to examine the effects of varying disturbance frequency and magnitude on population persistence in predator–prey and competition systems. Our analysis revealed the potential for non‐linear interactions between frequency and magnitude and their effect on population persistence. Effects differed depending on the form of population dynamics and whether disturbance affected abundance or demographic rates. It is critical to management efforts aiming to improve chances of population persistence to further understand the effects of varying disturbances on interacting populations.

Dynamic Analysis of a Fractional Order Three Species Interaction Model with Holling Type II Response

Dynamic Analysis of a Fractional Order Three Species Interaction Model with Holling Type II Response

Non-trophic impacts from white sharks complicate population recovery for sea otters.

Complex interactions between protected populations may challenge the recovery of whole ecosystems. In California, white sharks (Carcharodon carcharias) mistargeting southern sea otters (Enhydra lutris nereis) are an emergent impact to sea otter recovery, inhibiting the broader ecosystem restoration sea otters might provide. Here, we integrate and analyze tracking and stranding data to compare the phenology of interactions between white sharks and their targeted prey (elephant seals, Mirounga angustirostris) with those of mistargeted prey (sea otters, humans). Pronounced seasonal peaks in shark bites to otters and humans overlap in the late boreal summer, immediately before the annual adult white shark migration to elephant seal rookeries. From 1997 to 2017, the seasonal period when sharks bite otters expanded from 2 to 8 months of the year and occurred primarily in regions where kelp cover declined. Immature and male otters, demographics most associated with range expansion, were disproportionately impacted. While sea otters are understood to play a keystone role in kelp forests, recent ecosystem shifts are revealing unprecedented bottom‐up and top‐down interactions. Such shifts challenge ecosystem management programs that rely on static models of species interactions.

Exploiting network topology for large-scale inference of nonlinear reaction models.

The development of chemical reaction models aids understanding and prediction in areas ranging from biology to electrochemistry and combustion. A systematic approach to building reaction network models uses observational data not only to estimate unknown parameters but also to learn model structure. Bayesian inference provides a natural approach to this data-driven construction of models. Yet traditional Bayesian model inference methodologies that numerically evaluate the evidence for each model are often infeasible for nonlinear reaction network inference, as the number of plausible models can be combinatorially large. Alternative approaches based on model-space sampling can enable large-scale network inference, but their realization presents many challenges. In this paper, we present new computational methods that make large-scale nonlinear network inference tractable. First, we exploit the topology of networks describing potential interactions among chemical species to design improved 'between-model' proposals for reversible-jump Markov chain Monte Carlo. Second, we introduce a sensitivity-based determination of move types which, when combined with network-aware proposals, yields significant additional gains in sampling performance. These algorithms are demonstrated on inference problems drawn from systems biology, with nonlinear differential equation models of species interactions.

Derivation of species interactions strength in a plant community with game theory

Derivation of species interactions function in a plant community and the quantified analysis of the effects of multi-species interactions poses a challenge because of the large number of interactions in a community. However, through the interaction with the local environment and resources as well as the adjustment of species richness and distribution frequency, the community will eventually reach the highest resources utilization efficiency to attain the status of an ecologically stable community. We developed the species-interaction function based on above characteristic of stable community and the evolutionarily stable strategies theory of game theory, the conditions of coexistence, and the maximization of the utilization efficiency of minimal restriction resources. We discovered that once the ability of different species to occupy an unoccupied or vacant place in a plant community (the competitive ability of species) has been realized to the maximum extent, the product of the community richness and the harmonic mean of the fitness of all species is equal to the product of the total amount of minimal restriction resources and the resources utilization efficiency of the community. The species-interaction strength further proves that the interspecific and intraspecific interactions strengths are unified in the process of competing for resources. Through validation with published species diversity data covering fifteen years of a grassland community, it is proved that our species interaction model was able to explain the changes of plant diversity in the communities with the minimum limitation resource changes with the impact of global climate changes.

Handling times and saturating transmission functions in a snail-worm symbiosis.

All dynamic species interaction models contain an assumption that describes how contact rates scale with population density. Choosing an appropriate contact-density function is important, because different functions have different implications for population dynamics and stability. However, this choice can be challenging, because there are many possible functions, and most are phenomenological and thus difficult to relate to underlying ecological processes. Using one such phenomenological function, we described a nonlinear relationship between field transmission rates and host density in a common snail-oligochaete symbiosis. We then used a well-known contact function from predator-prey models, the Holling Type II functional response, to describe and predict host snail contact rates in the laboratory. The Holling Type II functional response accurately described both the nonlinear contact-density relationship and the average contact duration that we observed. Therefore, we suggest that contact rates saturate with host density in this system because each snail contact requires a non-instantaneous handling time, and additional possible contacts do not occur during that handling time. Handling times and nonlinear contact rates might also explain the nonlinear relationship between symbiont transmission and snail density that we observed in the field, which could be confirmed by future work that controls for other potential sources of seasonal variation in transmission rates. Because most animal contacts are not instantaneous, the Holling Type II functional response might be broadly relevant to diverse host-symbiont systems.

An a posteriori species clustering for quantifying the effects of species interactions on ecosystem functioning

Abstract Quantifying the effects of species interactions is key to understanding the relationships between biodiversity and ecosystem functioning but remains elusive due to combinatorics issues. Functional groups have been commonly used to capture the diversity of forms and functions and thus simplify the reality. However, the explicit incorporation of species interactions is still lacking in functional group‐based approaches. Here, we propose a new approach based on an a posteriori clustering of species to quantify the effects of species interactions on ecosystem functioning. We first decompose the observed ecosystem function using null models, in which species diversity does not affect ecosystem function, to separate the effects of species interactions and species composition. This allows the identification of a posteriori functional groups that have contrasting diversity effects on ecosystem functioning. We then develop a formal combinatorial model of species interactions in which an ecosystem is described as a combination of co‐occurring functional groups, which we call an assembly motif. Each assembly motif corresponds to a particular biotic environment. We demonstrate the relevance of our approach using datasets from a microbial experiment and the long‐term Cedar Creek Biodiversity II experiment. We show that our a posteriori approach is more accurate, more efficient and more parsimonious than a priori approaches. The discrepancy between a priori and a posteriori approaches results from the way each clustering is set up: a priori approaches are based on ecosystem or species properties, such as ecosystem size (number of species or functional groups) or species’ functional traits, whereas our a posteriori approach is based only on the observed interaction and composition effects on ecosystem functioning. Our findings demonstrate that an a posteriori approach is highly explanatory: it identifies who interacts with whom, and quantifies the effects of species interactions on ecosystem functioning. They also highlight that a combinatorial modelling of ecosystem functioning can predict the functioning of an ecosystem without any hypothesis about the biotic or environmental determinants or any information on species functional traits. It only requires the species composition of the ecosystem and the observed functioning of others that share the same assembly motif.