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Register a new userImpact of cyber-invasive species on a large ecological network
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As impacts of introduced species cascade through trophic levels, they can cause indirect and counter-intuitive effects. To investigate the impact of invasive species at the network scale, we use a generalized food web model, capable of propagating changes through networks with a series of ecologically realistic criteria. Using data from a small British offshore island, we quantify the impacts of four virtual invasive species (an insectivore, a herbivore, a carnivore and an omnivore whose diet is based on a rat) and explore which clusters of species react in similar ways. We find that the predictions for the impacts of invasive species are ecologically plausible, even for large networks robust predictions for the impacts of invasive species can be obtained. Species in the same taxonomic group are similarly impacted by a virtual invasive species. However, interesting differences within a given taxonomic group can occur. The results suggest that some native species may be at risk from a wider range of invasives than previously believed. The implications of these results for ecologists and land managers are discussed.
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Mediterranean ecosystems such as those found in California, Central Chile, Southern Europe, and Southwest Australia host numerous, diverse, fire-adapted micro-ecosystems. These micro-ecosystems are as diverse as mountainous conifer to desert-like chaparral communities. Over the last few centuries, human intervention, invasive species, and climate warming have drastically affected the composition and health of Mediterranean ecosystems on almost every continent. Increased fuel load from fire suppression policies and the continued range expansion of non-native insects and plants, some driven by long-term drought, produced the deadliest wildfire season on record in 2018. As a consequence of these fires, a large number of structures are destroyed, releasing household chemicals into the environment as uncontrolled toxins. The mobilization of these materials can lead to health risks and disruption in both human and natural systems. This article identifies drivers that led to a structural weakening of the mosaic of fire-adapted ecosystems in California, and subsequently increased the risk of destructive and explosive wildfires throughout the state. Under a new climate regime, managing the impacts on systems moving out-of-phase with natural processes may protect lives and ensure the stability of ecosystem services.
In this paper we present a discrete dynamical population modeling of invasive species, with reference to the swamp crayfish Procambarus clarkii. Since this species can cause environmental damage of various kinds, it is necessary to evaluate its expected in not yet infested areas. A structured discrete model is built, taking into account all biological information we were able to find, including the environmental variability implemented by means of stochastic parameters (coefficients of fertility, death, etc.). This model is based on a structure with 7 age classes, i.e. a Leslie mathematical population modeling type and it is calibrated with laboratory data provided by the Department of Evolutionary Biology (DEB) of Florence (Italy). The model presents many interesting aspects: the population has a high initial growth, then it stabilizes similarly to the logistic growth, but then it exhibits oscillations (a kind of limit-cycle attractor in the phase plane). The sensitivity analysis shows a good resilience of the model and, for low values of reproductive female fraction, the fluctuations may eventually lead to the extinction of the species: this fact might be exploited as a controlling factor. Moreover, the probability of extinction is valuated with an inverse Gaussian that indicates a high resilience of the species, confirmed by experimental data and field observation: this species has diffused in Italy since 1989 and it has shown a natural tendency to grow. Finally, the spatial mobility is introduced in the model, simulating the movement of the crayfishes in a virtual lake of elliptical form by means of simple cinematic rules encouraging the movement towards the banks of the catchment (as it happens in reality) while a random walk is imposed when the banks are reached.
One of the first beings affected by changes in the climate are trees, one of our most vital resources. In this study tree species interaction and the response to climate in different ecological environments is observed by applying a joint species distribution model to different ecological domains in the United States. Joint species distribution models are useful to learn inter-species relationships and species response to the environment. The climates impact on the tree species is measured through species abundance in an area. We compare the models performance across all ecological domains and study the sensitivity of the climate variables. With the prediction of abundances, tree species populations can be predicted in the future and measure the impact of climate change on tree populations.
Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bees tongue fit a plants flower shape. Empirical estimates of the importance of trait-matching for determining species interactions, however, vary significantly among different types of ecological networks. Here, we show that ambiguity among empirical trait-matching studies may have arisen at least in parts from using overly simple statistical models. Using simulated and real data, we contrast conventional generalized linear models (GLM) with more flexible Machine Learning (ML) models (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Neural Networks, Support Vector Machines, naive Bayes, and k-Nearest-Neighbor), testing their ability to predict species interactions based on traits, and infer trait combinations causally responsible for species interactions. We find that the best ML models can successfully predict species interactions in plant-pollinator networks, outperforming GLMs by a substantial margin. Our results also demonstrate that ML models can better identify the causally responsible trait-matching combinations than GLMs. In two case studies, the best ML models successfully predicted species interactions in a global plant-pollinator database and inferred ecologically plausible trait-matching rules for a plant-hummingbird network, without any prior assumptions. We conclude that flexible ML models offer many advantages over traditional regression models for understanding interaction networks. We anticipate that these results extrapolate to other ecological network types. More generally, our results highlight the potential of machine learning and artificial intelligence for inference in ecology, beyond standard tasks such as image or pattern recognition.
Cyclic dominance is frequently believed to be a mechanism that maintains diversity of competing species. But this delicate balance could also be fragile if some of the members is weakened because an extinction of a species will involve the annihilation of its predator hence leaving only a single species alive. To check this expectation we here introduce a fourth species which chases exclusively a single member of the basic model composed by three cyclically dominant species. Interestingly, the coexistence is not necessarily broken and we have detected three consecutive phase transitions as we vary only the invasion strength of the fourth pestilent species. The resulting phases are analyzed by different techniques including the study of the Hamming distance density profiles. Some of our observations strengthen previous findings about cyclically dominant system, but they also offer new revelations and counter-intuitive phenomenon, like supporting pestilent species may result in its extinction, hence enriching our understanding about these very simple but still surprisingly complex systems.
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