Spatially-explicit Simulation Model Research Articles

Grazing impact on structure and dynamics of bare soil areas in a Patagonian grass-shrub steppe

Bare soil is an idiosyncratic and important feature of arid ecosystems that can be affected by human land use such as domestic grazing intensification. Here we focused on the study of individual bare soil areas (BSA) and their long-term dynamics using DINVEG (spatially-explicit simulation model). In Patagonian rangelands we photographed BSA in grazed and ungrazed stands (n = 50). Compared with exclosure, grazing increased the size of the BSAs (17%, p < 0.05) and produced less irregular forms (13%, p < 0.05). The number of plants that delimit BSAs were not different (~9), but under grazing the sum of spaces among plants (BSA opening) was 15% higher than in exclosure (p < 0.05). The difference in wind speed (center-edge of the BSA) increased 70% under grazing. Wind difference was poorly correlated with the BSAs structure variables. Simulations (200 years) with DINVEG indicated that grazing decreased the likelihood of BSAs being colonized by grasses and increased the proportion of bare soil cells. Our study suggests that in these Patagonian steppes domestic grazing promotes structural changes in individual BSA and in the transition to grass covered patches. Changes in wind speed may result from grazing effect at the whole vegetation mosaic since BSA features are not enough to explain differences.

Evaluation of cultural control and resistance-breeding strategies for suppression of whitefly infestation of cassava at the landscape scale: a simulation modeling approach.

BACKGROUNDThe whitefly Bemisia tabaci is an important vector of virus diseases, impacting cassava production in East Africa. To date, breeding efforts in this region have focused on disease resistance. Here we use a spatially‐explicit simulation model to explore how breeding strategies for whitefly resistance will influence the population dynamics of whitefly in the context of regional variation in cassava crop management practices.RESULTSSimulations indicated that regions with a short cropping cycle and two cropping seasons per year were associated with high whitefly abundance. Nymph mortality and antixenosis resistance mechanisms were more effective than mechanisms that lead to longer whitefly development times. When spatial variation was introduced in heterogeneous landscapes, however, negative consequences of the antixenosis effect were observed in fields containing whitefly susceptible varieties, unless the proportion of whitefly resistant variety in the landscape was low (~10%) or the amount of matrix in the landscape was high (~75%).CONCLUSIONWe show the importance of considering cropping regime and landscape management context when developing and deploying whitefly‐resistant cassava varieties. Recommendations differ significantly between regions. There may also be unintended negative consequences of higher whitefly densities for whitefly susceptible varieties if uptake of the new variety in a landscape is high, depending on the mechanism of resistance and the landscape context. Furthermore, we show that in some cases, such as where there is substantial fallow combined with a short single‐season crop, the management characteristics of the existing cropping regime alone may be effective at controlling whitefly populations. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

EFForTS-LGraf: A landscape generator for creating smallholder-driven land-use mosaics.

Spatially-explicit simulation models are commonly used to study complex ecological and socio-economic research questions. Often these models depend on detailed input data, such as initial land-cover maps to set up model simulations. Here we present the landscape generator EFFortS-LGraf that provides artificially-generated land-use maps of agricultural landscapes shaped by small-scale farms. EFForTS-LGraf is a process-based landscape generator that explicitly incorporates the human dimension of land-use change. The model generates roads and villages that consist of smallholder farming households. These smallholders use different establishment strategies to create fields in their close vicinity. Crop types are distributed to these fields based on crop fractions and specialization levels. EFForTS-LGraf model parameters such as household area or field size frequency distributions can be derived from household surveys or geospatial data. This can be an advantage over the abstract parameters of neutral landscape generators. We tested the model using oil palm and rubber farming in Indonesia as a case study and validated the artificially-generated maps against classified satellite images. Our results show that EFForTS-LGraf is able to generate realistic land-cover maps with properties that lie within the boundaries of landscapes from classified satellite images. An applied simulation experiment on landscape-level effects of increasing household area and crop specialization revealed that larger households with higher specialization levels led to spatially more homogeneous and less scattered crop type distributions and reduced edge area proportion. Thus, EFForTS-LGraf can be applied both to generate maps as inputs for simulation modelling and as a stand-alone tool for specific landscape-scale analyses in the context of ecological-economic studies of smallholder farming systems.

Potential Benefits of Vessel Slowdowns on Endangered Southern Resident Killer Whales

A voluntary commercial vessel Slowdown trial was conducted through 16 nm of shipping lanes overlapping critical habitat of at-risk southern resident killer whales (SRKW) in the Salish Sea. From August 7 to October 6, 2017, the trial requested piloted vessels to slow to 11 knots speed-through-water. Analysis of AIS vessel tracking data showed that 421 of 951 (44%) piloted transits achieved speeds within one knot of the target (i.e., ≤12 knots) and 55% achieved speeds ≤13 knots. Slowdown results were compared to ‘Baseline’ noise of the same region, matched across lunar months. A local hydrophone listening station in Lime Kiln State Park, 2.3 km from the shipping lane, recorded 1.2 dB reductions in median broadband noise (10-100,000 Hz, rms) compared to the Baseline period, despite longer transit durations during the trial. Filtering for commercial vessels within 6 km radius of Lime Kiln listening station (aiming to remove confounding effects of variability in vessel numbers), and excluding small boat noise, high current and wind speed periods, median noise was reduced by 2.5 dB. The reductions were highest in the 1st decade band (-3.1 dB, 10-100 Hz) and lowest in the 4th decade band (-0.3 dB reduction, 10-100 kHz). A regional vessel noise model predicted noise for a range of traffic volume and vessel speed scenarios for a 1133 km2 ‘Slowdown region’ containing the 16 nm of the trial’s shipping lanes. A temporally and spatially-explicit simulation model was used to evaluate the changes in traffic volume and speed on SRKW in their foraging habitat within this Slowdown region. The simulation model tracked the number and magnitude of noise-exposure events that impacted each of 78 SRKW across different traffic volume-speed scenarios. These metrics were simplified to a cumulative effect termed ‘potential lost foraging time’. The model predicted that the voluntary Slowdown trial achieved 22% reduction in ‘potential lost foraging time’ for SRKW, with 40% reductions under 100% 11 knot participation. Slower vessel speeds reduced underwater noise in the Slowdown area despite longer passage times and therefore suggest this is an effective way to benefit SRKW habitat function in the vicinity of shipping lanes.

Coupling general physical environmental process models with specific question-driven ecological simulation models

The mechanistic basis for problem-specific, question-driven ecological simulation models often includes general physical phenomena. Although linking general physical environmental process models with custom-written spatially-explicit ecological models appears conceptually straightforward, practical implementation of such structures is often problematic for ecological modellers and may require specific software engineering knowledge. We describe a general coupling framework that introduces an intermediate layer between the two types of models. We used a popular programming language (NetLogo) to implement a spatially-explicit simulation model for insect population growth and migration, and a commonly-used complex atmospheric dispersion model (HYSPLIT) to project air particle (here insect pest) transport, dispersion and deposition. We developed an algorithm that facilitates an ongoing dialogue between these two models. The dialogue takes place along a temporal scale with frequency and duration constrained only by the study objective. Our approach should be readily adaptable to analogous problems.

Modelling the spread and control of Xylella fastidiosa in the early stages of invasion in Apulia, Italy

Xylella fastidiosa is an important plant pathogen that attacks several plants of economic importance. Once restricted to the Americas, the bacterium, which causes olive quick decline syndrome, was discovered near Lecce, Italy in 2013. Since the initial outbreak, it has invaded 23,000 ha of olives in the Apulian Region, southern Italy, and is of great concern throughout Mediterranean basin. Therefore, predicting its spread and estimating the efficacy of control are of utmost importance. As data on this invasive infectious disease are poor, we have developed a spatially-explicit simulation model for X. fastidiosa to provide guidance for predicting spread in the early stages of invasion and inform management strategies. The model qualitatively and quantitatively predicts the patterns of spread. We model control zones currently employed in Apulia, showing that increasing buffer widths decrease infection risk beyond the control zone, but this may not halt the spread completely due to stochastic long-distance jumps caused by vector dispersal. Therefore, management practices should aim to reduce vector long-distance dispersal. We find optimal control scenarios that minimise control effort while reducing X. fastidiosa spread maximally—suggesting that increasing buffer zone widths should be favoured over surveillance efforts as control budgets increase. Our model highlights the importance of non-olive hosts which increase the spread rate of the disease and may lead to an order of magnitude increase in risk. Many aspects of X. fastidiosa disease invasion remain uncertain and hinder forecasting; we recommend future studies investigating quantification of the infection growth rate, and short and long distance dispersal.

Stochastic spread models: A comparison between an individual-based and a lattice-based model for assessing the expansion of invasive termites over a landscape

Spatially-explicit simulation models can help state and local regulatory agencies to predict both the rate and direction of the spread of an invasive species from a set of surveyed locations. Such models can be used to develop successful early detection, quarantine, or eradication plans based on the predicted areas of infestation. Individual-based models (IBMs) are often used to replicate the dynamics of complex systems and are both able to incorporate individual differences and local interactions among organisms, as well as spatial details. In this work, we introduce a new stochastic lattice-based model for simulating the spread of invasive termites over a landscape and compare it to a recently published stochastic individual-based approach, based on the same ecological parameters, with the goal of improving its computational efficiency. The two modeling frameworks were tested over a homogeneous landscape with randomly located sources of infestation. Further, the setting of a case-study of an invasive termite, Nasutitermes corniger (Motschulsky), was used to simulate the spread of the species in Dania Beach, Florida, U.S.A., and the results of the proposed model were compared with an earlier application of the IBM over the same area. The results show that the extent of the infested areas predicted by the new lattice-based model is similar, thus comparable, to the individual-based model while improving the computation time significantly. The simulation presented in this work could be used by the regulatory authorities to draw one or more areas of intervention instead of wasting resources by randomly surveying unknown perimeters.

A spatial simulation model for the dispersal of the bluetongue vector Culicoides brevitarsis in Australia.

BackgroundThe spread of Bluetongue virus (BTV) among ruminants is caused by movement of infected host animals or by movement of infected Culicoides midges, the vector of BTV. Biologically plausible models of Culicoides dispersal are necessary for predicting the spread of BTV and are important for planning control and eradication strategies.MethodsA spatially-explicit simulation model which captures the two underlying population mechanisms, population dynamics and movement, was developed using extensive data from a trapping program for C. brevitarsis on the east coast of Australia. A realistic midge flight sub-model was developed and the annual incursion and population establishment of C. brevitarsis was simulated. Data from the literature was used to parameterise the model.ResultsThe model was shown to reproduce the spread of C. brevitarsis southwards along the east Australian coastline in spring, from an endemic population to the north. Such incursions were shown to be reliant on wind-dispersal; Culicoides midge active flight on its own was not capable of achieving known rates of southern spread, nor was re-emergence of southern populations due to overwintering larvae. Data from midge trapping programmes were used to qualitatively validate the resulting simulation model.ConclusionsThe model described in this paper is intended to form the vector component of an extended model that will also include BTV transmission. A model of midge movement and population dynamics has been developed in sufficient detail such that the extended model may be used to evaluate the timing and extent of BTV outbreaks. This extended model could then be used as a platform for addressing the effectiveness of spatially targeted vaccination strategies or animal movement bans as BTV spread mitigation measures, or the impact of climate change on the risk and extent of outbreaks. These questions involving incursive Culicoides spread cannot be simply addressed with non-spatial models.

Cereal aphid movement: general principles and simulation modelling

Cereal aphids continue to be an important agricultural pest, with complex lifecycle and dispersal behaviours. Spatially-explicit models that are able to simulate flight initiation, movement direction, distance and timing of arrival of key aphid species can be highly valuable to area-wide pest management programmes. Here I present an overview of how knowledge about cereal aphid flight and migration can be utilized by mechanistic simulation models. This article identifies specific gaps in knowledge for researchers who may wish to further scientific understanding of aphid flight behaviour, whilst at the same time provides a synopsis of the knowledge requirements for a mechanistic approach applicable to the simulation of a wide range of insect species.Although they are one of the most comprehensively studied insect groups in entomology, it is only recently that our understanding of cereal aphid flight and migration has been translated effectively into spatially-explicit simulation models. There are now a multitude of examples available in the literature for modelling methods that address each of the four phases of the aerial transportation process (uplift, transport in the atmosphere, initial distribution, and subsequent movement). I believe it should now be possible to draw together this knowledgebase and the range of modelling methods available to simulate the entire process: integrating mechanistic simulations that estimate the initiation of migration events, with the large scale migration modelling of cereal aphids and their subsequent local movement.Electronic supplementary materialThe online version of this article (doi:10.1186/2051-3933-1-14) contains supplementary material, which is available to authorized users.

Predicted Impact of Barriers to Migration on the Serengeti Wildebeest Population

The Serengeti wildebeest migration is a rare and spectacular example of a once-common biological phenomenon. A proposed road project threatens to bisect the Serengeti ecosystem and its integrity. The precautionary principle dictates that we consider the possible consequences of a road completely disrupting the migration. We used an existing spatially-explicit simulation model of wildebeest movement and population dynamics to explore how placing a barrier to migration across the proposed route (thus creating two disjoint but mobile subpopulations) might affect the long-term size of the wildebeest population. Our simulation results suggest that a barrier to migration—even without causing habitat loss—could cause the wildebeest population to decline by about a third. The driver of this decline is the effect of habitat fragmentation (even without habitat loss) on the ability of wildebeest to effectively track temporal shifts in high-quality forage resources across the landscape. Given the important role of the wildebeest migration for a number of key ecological processes, these findings have potentially important ramifications for ecosystem biodiversity, structure, and function in the Serengeti.

Continuous versus binary representations of landscape heterogeneity in spatially-explicit models of mobile populations

How a landscape is represented is an important structural assumption in spatially-explicit simulation models. Simple models tend to specify just habitat and non-habitat (binary), while more complex models may use multiple levels or a continuum of habitat quality (continuous). How these different representations influence model projections is unclear. To assess the influence of landscape representation on population models, I developed a general, individual-based model with local dispersal and examined population persistence across binary and continuous landscapes varying in the amount and fragmentation of habitat. In binary and continuous landscapes habitat and non-habitat were assigned a unique mean suitability. In continuous landscapes, suitability of each individual site was then drawn from a normal distribution with fixed variance. Populations went extinct less often and abundances were higher in continuous landscapes. Production in habitat and non-habitat was higher in continuous landscapes, because the range of habitat suitability sampled by randomly dispersing individuals was higher than the overall mean habitat suitability. Increasing mortality, dispersal distance, and spatial heterogeneity all increased the discrepancy between continuous and binary landscapes. The effect of spatial structure on the probability of extinction was greater in binary landscapes. These results show that, under certain circumstances, model projections are influenced by how variation in suitability within a landscape is represented. Care should be taken to assess how a given species actually perceives the landscape when conducting population viability analyses or empirical validation of theory.

The positive effects of negative interactions: Can avoidance of competitors or predators increase resource sampling by prey?

Spatial overlap between predators and prey is key to predicting their interaction strength and population dynamics. We constructed a spatially-explicit simulation model to explore how predator and prey behavioral traits and patterns of resource distribution influence spatial overlap between predators, prey, and prey resources. Predator and prey spatial association primarily followed the ideal free distribution. Departures from this model were intriguing, especially from the interactions of predator and prey behavior. When prey weakly avoided conspecifics, they associated more highly with resources when predators were present. Predators increased the rate of prey movement between patches, which increased their ability to sample their environment and aggregate in patches with high resources. When prey strongly avoided each other, predators decreased prey association with resources. That is, an increased rate of prey movement increased the probability that prey would interact and avoid each other without regard to the distribution of resources. More generally, a more highly clumped distribution of resources acted as a spatial anchor that generally increased prey, predator, and resource association. Prey tended to congregate with resources and predators generally congregated with prey.

Sexual harassment in heterogeneous landscapes can mediate population regulation in a grasshopper

Population regulation has been related to differences in the quality among habitats, which mediate differences in vital rates such that in poor habitats reproductive rates are lower than those in high-quality habitats. The spatial distribution of animals in such habitats depends on their preferences and the degree to which individuals have a free choice of a particular habitat. The identified mechanisms that lead to a particular spatial distribution and eventually to regulation mainly include foraging-related interference, for example, ideal free distribution, or simple selection of available high-quality habitats, that is, site-dependent habitat selection. However, in insect species these mechanisms might not be applicable, but density-dependent habitat selection still occurs. We therefore suggest a mechanism that refers to the nearly universal observation that matings also bear fitness costs. Although these costs have been investigated on the individual level in many insect species, their consequences for population dynamics have not yet been addressed. In the grasshopper species Stenobothrus lineatus, females in a nonreceptive mating status escape sexually approaching males by undirected jumps. By including such avoidance behavior in a spatially-explicit simulation model, we investigated its potential to result in progressive use of low-quality habitats at increasing population densities. In particular, we show that (1) such behavior changes habitat selection, (2) altered habitat selection results in population regulation, and (3) the degree of habitat heterogeneity influences regulation such that (4) heterogeneous habitats show finetuned regulation and homogeneous habitats tend to support large fluctuations. Key words: habitat quality, habitat use, individual interactions, mating behavior, spatial simulation model, Stenobothrus lineatus. [Behav Ecol]

On the mechanisms of coexistence among annual-plant species, using neighbourhood techniques and simulation models

Many studies have investigated the density-dependent regulation ofannual-plant populations on coastal sand dunes, but few have explored theconsequences of competition for the coexistence of plants in these simplecommunities. We used neighbourhood techniques to parameterize competition anddispersal functions from field data collected for two species of dune annual(Aira praecox and Erodium cicutarium)over three successive years, and then combined these functions into spatiallyexplicit simulation models. The population size of Airavaried enormously between years, while Erodium remainedsteady. Competition with neighbours reduced the spike length ofAira plants only in one of the three years (when itspopulation density was highest), while competition with neighbouringErodium plants appeared to result in the local death ofAira plants. However, these density-dependent effects werefar too weak to generate the observed changes in the population size ofAira among years, or to maintain populations below theupper limits observed. The large-seeded Erodium wasaffected by intraspecific competition but was unaffected by small-seededAira plants. Therefore, the larger-seeded species wascompetitively superior to the smaller-seeded species, an affect that couldpromote coexistence (albeit weakly) by a competition-colonisation trade-off.Modal dispersion distances of Aira andErodium were 45 and 60 mm respectively,greater than the radius within which competitive interactions occurred (40mm). Theoretical studies suggest that under these conditions thespatial arrangement of plants should be nearly random. In factAira was spatially aggregated, especially when rare,suggesting that patchy mortality across the dunes was important in generatingspatial structure. The study suggests that density dependence only weaklyregulates dune annual communities, while year-to-year environmental variationexert major influences on population sizes and spatial structures.

How much habitat is enough?

If conservation efforts are to be successful, it is critical that we understand the relationship between habitat loss and the probability of population extinction. Available evidence suggests a threshold amount of habitat loss at which the probability of population extinction increases from near-zero to near-one following a small additional loss of habitat. The main factors thought to determine this extinction threshold are reproductive rate of the organism, rate of emigration of the organism from habitat, habitat pattern in the landscape (fragmentation), and matrix quality (survival rate of the organism in non-habitat areas). Effective conservation measures require knowledge not only of what factors determine the threshold, but also the relative magnitudes of their effects. Therefore, the purpose of this study was to determine the relative effects of these four factors on the extinction threshold, using a spatially-explicit simulation model. Reproductive rate had the largest potential effect on the extinction threshold. The rate of emigration from habitat also had a very strong predicted effect on the extinction threshold; the higher the rate of emigration, the more habitat was needed for persistence. Matrix quality had a moderate effect, and habitat pattern had a very small predicted effect. The simulations predicted that under certain circumstances up to 58% less habitat is required for population persistence, if a matrix of very low quality is converted to a matrix of very high quality. Matrix quality can be improved by maintaining heterogeneous landscapes with a diversity of vegetated features, and by reducing factors causing direct mortality of dispersers such as road traffic and pesticides. In summary, the simulations suggest that (i) the first priority for conservation should be habitat preservation and restoration, (ii) information on movement rates of organisms is critical for predicting extinction thresholds and (iii) conservation strategies should consider the quality of the whole landscape, including the matrix.

Linking a spatially-explicit model of acacias to GIS and remotely-sensed data

Spatially-explicit and landscape-related simulation models are increasingly used in ecology, but are often criticized because their parameterization has high data requirements. A frequently suggested approach to overcome this difficulty is the linkage of spatially-explicit or landscape-related models with GIS (geographic information system) and remote-sensing technology. GIS can provide data on relevant landscape features, such as topography, and satellite images can be used to identify spatial vegetation distribution. In this paper, we use these techniques for simple, cost-inexpensive (in both time and money) parameterization based on readily-available GIS and remotely-sensed data.

An individual-based, spatially-explicit simulation model of the population dynamics of the endangered red-cockaded woodpecker, Picoides borealis

Spatially-explicit population models allow a link between demography and the landscape. We developed a spatially-explicit simulation model for the red-cockaded woodpecker, Picoides borealis, an endangered and territorial cooperative breeder endemic to the southeastern United States. This kind of model is especially appropriate for this species because it can incorporate the spatial constraints on dispersal of helpers, and because territory locations are predictable. The model combines demographic data from a long-term study with a description of the spatial location of territories. Sensitivity analysis of demographic parameters revealed that population stability was most sensitive to changes in female breeder mortality, mortality of female dispersers and the number of fledglings produced per brood. Population behavior was insensitive to initial stage distribution; reducing the initial number of birds by one-half had a negligible effect. Most importantly, we found that the spatial distribution of territories had as strong an effect on response to demographic stochasticity as territory number. Populations were stable when territories were highly aggregated, with as few as 49 territories. When territories were highly dispersed, more than 169 territories were required to achieve stability. Model results indicate the importance of considering the spatial distribution of territories in management plans, and suggest that this approach is worthy of further development.