Ecological Complexity最新文献

Climate change may modify environmental conditions creating suitable environments for phytopathogen vectors in places that were not suitable before. The present study aimed to contrast current and future spatial distribution of Diaphorina citri in Mexico under two climate change scenarios, Shared Socioeconomic Pathways (SSP) 4.5 and 8.5 for years 2050 and 2070. Non-correlated bioclimatic variables from eight General Circulation Models derived from the Coupled Model Intercomparison Project-6 and presence point data were used to generate distribution models with MaxEnt. Future projections showed that current suitable areas, equivalent to a 38.6% of coverage persist across all scenarios, new suitability areas appear, and no reduction is expected. All the models coincide on a potential increase in relation to the current national distribution of 11.1, 14.8, 13.8 and 25.5% for SSP2 4.5–50 SSP2 4.5–70 SSP5 8.5–50, and SSP5 8.5–70 respectively. Most of the new areas are not currently dedicated to citriculture; however, an increase in the risk of Huanglongbing is expected because most of the new areas are contiguous to the current presence areas, and cover urban zones where there may exist rutaceous hosts, from which the vector may spread the disease to the production zones.

Human-carnivore systems are built on multi-scalar complex processes often resulting in conflicts that force wildlife managers to address what are conceived as problem individuals. In North America, the grizzly bear (Ursus arctos) is often involved in human-bear conflict with management measures such as translocations, in which problem individuals are moved to new areas, being used to reduce conflict risk. While translocations offer a non-lethal alternative to managing conflict animals, they show varying levels of success. Our objective was to perform a novel assessment of grizzly bear translocation success through agent-based simulation by evaluating how familiarity with landscape features coupled with behavioral traits affects the way individuals use resources in a new environment. Our results showed that bears translocated to familiar habitat used high-quality habitat more than bears moved to areas with unfamiliar landscape characteristics. Increased exploration led to greater use of high-quality habitat in the long run but resulted in reduced use of high-quality habitat during the first two years following a translocation. Habitat quality use depended on scale, with bears translocated to less familiar environments accessing higher quality areas at a finer scale than bears translocated to familiar habitats. We emphasize the need to account for wildlife behavioral traits and habitat characteristics at multiple scales when selecting suitable translocation locations. Understanding the role of factors such as these on translocation outcome will help ensure the success of translocations not only as a method for managing problem wildlife, but also for population restoration, species reestablishment, and conservation translocations across the globe.

The negative effect of fragmentation is one of the main concerns in the study of biodiversity loss in landscape ecology. The use of the matrix has been considered an important factor because it can change a population's relationship with the configuration of the landscape. A systematic way to assess the effect of matrix quality in fragmented landscapes could lead to a better understanding of how matrices can suppress the negative effect of fragmentation. We built a computational individual-based model capable of simulating bi-dimensional landscapes with three types of land cover (habitat, suitable matrix and hostile matrix) and individuals that inhabit those landscapes. We explored in which situations suitable matrix proportions and the degree of usability of this suitable matrix mitigate the negative effect of fragmentation per se. We observed that (i) an increase in the general matrix quality (increases in the suitable matrix proportion and/or usability) can suppress the fragmentation effect in 47% of the simulated scenarios; (ii) the less usable the matrix is, the more of it is needed to suppress the fragmentation effect; (iii) there is a level of usability below which increasing the suitable matrix proportion does cause the fragmentation effect to cease. These results point toward landscape management decisions that consider the similarity of the matrix to the native habitat under management. We suggest that an index to measure the usability of elements of the matrix could be an important tool for using computational models in landscape management more efficiently.

Spatio-ecological heterogeneity is strongly linked to many ecological processes and functions such as plant species diversity patterns and change, metapopulation dynamics, and gene flow. Remote sensing is particularly useful for measuring spatial heterogeneity of ecosystems over wide regions with repeated measurements in space and time. Besides, developing free and open source algorithms for ecological modelling from space is vital to allow to prove workflows of analysis reproducible. From this point of view, NASA developed programs like the Surface Biology and Geology (SBG) to support the development of algorithms for exploiting spaceborne remotely sensed data to provide a relatively fast but accurate estimate of ecological properties in vast areas over time. Most of the indices to measure heterogeneity from space are point descriptors : they catch only part of the whole heterogeneity spectrum. Under the SBG umbrella, in this paper we provide a new R function part of the rasterdiv R package which allows to calculate spatio-ecological heterogeneity and its variation over time by considering all its possible facets. The new function was tested on two different case studies, on multi- and hyperspectral images, proving to be an effective tool to measure heterogeneity and detect its changes over time.

Subterranean termites build underground tunnels for foraging. The obtained food is transported to the nest through these tunnels, and consumed to maintain the termite colony. In this process, termites can cause damage to wooden structures. To develop effective control strategies to reduce termite damage, it is important to know the sizes of the termite populations in the tunnels. In this study, we proposed a method for estimating the termite population size using the spatial statistic indices including fractal dimension (FD), local density (LD), and join count statistic (JCS) for the tunnel patterns. However, the method needs further improvement to be applied in field conditions. For the method, we generated 8,000 tunnel pattern images (1,000 images for each N) using an agent-based model based on experimental data. Here, N (= 3, 4, ..., 10) represents the number of termites participating in tunnel construction in the simulation. Subsequently, we calculated the FD, LD and JCS values of the tunnel pattern and trained and verified the k-nearest neighbors (KNN) algorithm, using 5,600 and 2,400 images, respectively. The population size (N) was estimated based on the FD, LD and JCS using the KNN algorithm. The estimated accuracy for all N was 60% to 97% in the range of k = 1 to 300. If the model for tunnel pattern generation includes heterogeneous environmental conditions, the proposed method could be used to effectively estimate the actual number of termite populations. Finally, we briefly discuss the challenges affecting our model, and how these could be overcome.

We investigate a tritrophic system whose cyclic dominance is modelled by the rock–paper–scissors game. We consider that organisms of one or two species are affected by movement limitations, which unbalances the cyclic spatial game. Performing stochastic simulations, we show that mobility unevenness controls the population dynamics. In the case of one slow species, the predominant species depends on the level of mobility restriction, with the slow species being preponderant if the mobility limitations are substantial. If two species face mobility limitations, our outcomes show that being higher dispersive does not constitute an advantage in terms of population growth. On the contrary, if organisms move with higher mobility, they expose themselves to enemies more frequently, being more vulnerable to being eliminated. Finally, our findings show that biodiversity benefits in regions where species are slowed. Biodiversity loss for high mobility organisms, common to cyclic systems, may be avoided with coexistence probability being higher for robust mobility limitations. Our results may help biologists understand the dynamics of unbalanced spatial systems where organisms’ dispersal is fundamental to biodiversity conservation.

We examine the nonlinear reaction–diffusion–advection equations to modeling of the predator–prey system under heterogeneous carrying capacity of the prey, and Holling type II functional response. When advection and diffusion fluxes are absent or small, we detect the discrepancy between the resource (carrying capacity) and species distributions. The large diffusion eliminates this effect. We propose a modification of the functional response coefficients to provide the correlation between species distribution and resource in both cases. The numerical simulation of several models both under small and moderate advection–diffusion fluxes is carried out.

While behavioral responses of individual organisms can be predicted with optimal foraging theory, the theory of how individual behavior feeds back to population and ecosystem dynamics has not been fully explored. Ecological models of trophic interactions incorporating behavior of entire populations commonly assume either that populations act as one when making decisions, that behavior is slowly varying or that non-linear effects are negligible in behavioral choices at the population scale. Here, we scale from individual optimal behavior to ecosystem structure in a classic tri-trophic chain where both prey and predators adapt their behavior in response to food availability and predation risk. Behavior is modeled as playing the field, with both consumers and predators behaving optimally at every instant basing their choices on the average population behavior. We establish uniqueness of the Nash equilibrium, and find it numerically. By modeling the interactions as playing the field, we can perform instantaneous optimization at the individual level while taking the entire population into account. We find that optimal behavior essentially removes the effect of top-down forcing at the population level, while drastically changing the behavior. Bottom-up forcing is found to increase populations at all trophic levels. These phenomena both appear to be driven by an emerging constant consumption rate, corresponding to a partial satiation. In addition, we find that a Type III functional response arises from a Type II response for both predators and consumers when their behavior follows the Nash equilibrium, showing that this is a general phenomenon. Our approach is general and computationally efficient and can be used to account for behavior in population dynamics with fast behavioral responses.