Accuracy Assessment for a Simulation Model of Amazonian Deforestation

The neutral model posits that random variation in extinction and speciation events, coupled with limited dispersal, can account for many community properties, including the relative abundance distribution. There are important analogies between this model in ecology and a three‐tiered hierarchy of models in evolution (Hardy Weinburg, drift, drift and selection). Because it invokes random processes and is used in statistical tests of empirical data, the neutral model can be interpreted as a specialized form of a null model. However, the application and interpretation of neutral models differs from that of standard null models in three important ways: 1) whereas most null models incorporate species‐level constraints that are often associated with niche differences, the neutral model assumes that all species are functionally equivalent. 2) Null models are usually fit with constraints that are measured directly from the data set itself. In contrast, the neutral model requires parameters for speciation, extinction, and migration rates that are almost never measured directly, so their values must be guessed at or fitted. 3) Most important, null models are viewed as simple statistical descriptors: unspecified “random” forces generate variation in a simple model that excludes particular biological mechanisms (usually species interactions). Although the neutral model was originally framed as a null model, recent proponents of the neutral model have begun to treat it as a literal process‐based description of community assembly.These differences lie at the heart of much of the recent controversy over the neutral model. If the neutral model is truly a process‐based model, then its assumptions should be directly tested, and its predictions should be compared to those of an appropriate null model. Such tests are rarely informative, and most empirical data sets can be fit more parsimoniously to a simple log‐normal distribution. Because unknown parameters in the neutral model must usually be guessed at or fit in ad‐hoc ways, classical frequentist tests are compromised, and may be biased towards finding a good fit with the model. There has been little analysis of the potential for type I and type II errors in statistical tests of the neutral model.The neutral model has recently been proposed as a specific form of more general null models in biogeography (the mid‐domain effect) and community ecology (species co‐occurrence). In both cases, the neutral model is qualitatively, but not quantitatively, similar to the predictions of classic null models. However, because the important parameters in the neutral model can rarely be measured directly, it may be of limited value as a null hypothesis for empirical tests.Future progress may come from moving beyond dichotomous tests of neutral versus null models. Instead, the neutral model might be viewed as a mechanism that contributes to pattern along with other processes. Alternatively, the fit of data to the neutral model can be compared to the fit to other process‐based models that are not based on neutrality assumptions. Finally, the neutral model can also be tested directly if its parameters can be estimated independently of the test data. However, these approaches may require more data than are often available. For these reasons, simple null model tests will continue to be important in the evaluation of the neutral model.

ObjectivesThe objective of this article is to describe the unique challenges and present potential solutions and approaches for economic evaluations of precision medicine (PM) interventions using simulation modeling methods. MethodsGiven the large and growing number of PM interventions and applications, methods are needed for economic evaluation of PM that can handle the complexity of cascading decisions and patient-specific heterogeneity reflected in the myriad testing and treatment pathways. Traditional approaches (eg, Markov models) have limitations, and other modeling techniques may be required to overcome these challenges. Dynamic simulation models, such as discrete event simulation and agent-based models, are used to design and develop mathematical representations of complex systems and intervention scenarios to evaluate the consequence of interventions over time from a systems perspective. ResultsSome of the methodological challenges of modeling PM can be addressed using dynamic simulation models. For example, issues regarding companion diagnostics, combining and sequencing of tests, and diagnostic performance of tests can be addressed by capturing patient-specific pathways in the context of care delivery. Issues regarding patient heterogeneity can be addressed by using patient-level simulation models. ConclusionThe economic evaluation of PM interventions poses unique methodological challenges that might require new solutions. Simulation models are well suited for economic evaluation in PM because they enable patient-level analyses and can capture the dynamics of interventions in complex systems specific to the context of healthcare service delivery.

Selecting a Dynamic Simulation Modeling Method for Health Care Delivery Research—Part 2: Report of the ISPOR Dynamic Simulation Modeling Emerging Good Practices Task Force

Amazon Basin shows reduced forest loss but increased forest spatial fragmentation in 1992-2020.

Big Data and Its Role in Health Economics and Outcomes Research: A Collection of Perspectives on Data Sources, Measurement, and Analysis.

The exon shuffling theory posits that intronic recombination creates new domain combinations, facilitating the evolution of novel protein function. This theory predicts that introns will be preferentially situated near domain boundaries. Many studies have sought evidence for exon shuffling by testing the correspondence between introns and domain boundaries against chance intron positioning. Here, we present an empirical investigation of how the choice of null model influences significance. Although genome-wide studies have used a uniform null model, exclusively, more realistic null models have been proposed for single gene studies. We extended these models for genome-wide analyses and applied them to 21 metazoan and fungal genomes. Our results show that compared with the other two models, the uniform model does not recapitulate genuine exon lengths, dramatically underestimates the probability of chance agreement, and overestimates the significance of intron-domain correspondence by as much as 100 orders of magnitude. Model choice had much greater impact on the assessment of exon shuffling in fungal genomes than in metazoa, leading to different evolutionary conclusions in seven of the 16 fungal genomes tested. Genome-wide studies that use this overly permissive null model may exaggerate the importance of exon shuffling as a general mechanism of multidomain evolution.

Analyses of habitat use for individuals occupying discrete home ranges are typically based on comparison with null models that implicitly assume no spatial context for habitat use within the home range. For species that regularly return to a central place, a more appropriate null model for estimation of habitat selection may be that of a declining expectation of resource use with distance from the central place, such as a nest site. When this null expectation is ignored and a uniform-use expectation is used for central-place foragers, we predicted (1) positive bias of selection for habitat types near the central place, and (2) bias will increase with the degree to which habitat types are spatially correlated to the central place. We explored these predictions with simulated data, using a range of selection intensities and spatial correlations. Results from the simulations confirmed our predictions: biases were large and positive for those habitat types proximal to the central place. To correct for these biases, we included distance from the central place as an explanatory variable in habitat selection models of simulated central-place foraging, and we found that including distance as a linear factor successfully reduced these biases. We then applied these models to field data from northern spotted owls (Strix occidentalis caurina) and red-cockaded woodpeckers (Picoides borealis). For both species, distance-based models performed better than the nonspatial (uniform) model: the models were both statistically superior and produced results more in concordance with our biological understandings. Estimates of selection for habitat types that were disproportionately located near the central place were lower in the distance-based models than in the uniform model, corroborating the results from the simulations. The simple distance-based models we used provide a reasonable means to estimate foraging habitat selection for animals for which a central place can be identified.

Abstract: The Amazon basin is experiencing rapid forest loss and fragmentation. Fragmented forests are more prone than intact forests to periodic damage from El Niño–Southern Oscillation ( ENSO) droughts, which cause elevated tree mortality, increased litterfall, shifts in plant phenology, and other ecological changes, especially near forest edges. Moreover, positive feedbacks among forest loss, fragmentation, fire, and regional climate change appear increasingly likely. Deforestation reduces plant evapotranspiration, which in turn constrains regional rainfall, increasing the vulnerability of forests to fire. Forest fragments are especially vulnerable because they have dry, fire‐prone edges, are logged frequently, and often are adjoined by cattle pastures, which are burned regularly. The net result is that there may be a critical “deforestation threshold” above which Amazonian rainforests can no longer be sustained, particularly in relatively seasonal areas of the basin. Global warming could exacerbate this problem if it promotes drier climates or stronger ENSO droughts. Synergisms among many simultaneous environmental changes are posing unprecedented threats to Amazonian forests.

Partner choice in species interaction networks, that is, between frugivorous birds and fruiting plants, is largely determined by matching of functional traits. However, the composition of functional traits in plant communities changes along land‐use gradients. Understanding how flexible consumers react to changes in the trait composition of resources is crucial to project consequences for ecosystem functions, such as seed dispersal. We investigated the ability of birds to consume fruits with different sets of traits in natural and fragmented tropical montane forests across an elevational gradient. We developed a novel, trait‐based approach to quantify the functional shifts of consumers between resources with different functional traits. We expected the degree of functional shifts to be associated with bird traits related to food choice, such as bill width and degree of frugivory, as well as foraging behaviour, such as wing shape and foraging stratum. We sampled the plant–frugivore networks at three elevations and two habitat types (natural and fragmented forest) and measured the functional traits for each plant and bird species. We calculated the trait space of the plant community at each elevation and projected the interacting birds into it. Finally, we calculated the functional shift, which is the trait‐based distance between the preferred fruit resources in the two habitat types for each bird species. We found differences in the functional trait space of the fruiting plant community between natural and fragmented forests across all elevations. Birds' observed functional shifts between habitat types at each elevation were generally larger than the shifts expected by null models. Wing shape was the most important trait related to the functional shifts across the elevational gradient, whereas bill width, degree of frugivory, foraging stratum and phylogeny were not important. We conclude that birds with pointed wings respond flexibly to changes in the trait composition of fruit resources, probably due to the high mobility of these species. Our results emphasize that linking species interaction networks and functional trait analyses yield new insights into how consumer species respond to changes in biotic factors and can improve projections of how human impacts modify trophic interactions and associated ecosystem functions. A plain language summary is available for this article.

BackgroundSystems science methods such as dynamic simulation modelling are well suited to address questions about public health policy as they consider the complexity, context and dynamic nature of system-wide behaviours. Advances in technology have led to increased accessibility and interest in systems methods to address complex health policy issues. However, the involvement of policy decision makers in health-related simulation model development has been lacking. Where end-users have been included, there has been limited examination of their experience of the participatory modelling process and their views about the utility of the findings. This paper reports the experience of end-user decision makers, including senior public health policy makers and health service providers, who participated in three participatory simulation modelling for health policy case studies (alcohol related harm, childhood obesity prevention, diabetes in pregnancy), and their perceptions of the value and efficacy of this method in an applied health sector context.MethodsSemi-structured interviews were conducted with end-user participants from three participatory simulation modelling case studies in Australian real-world policy settings. Interviewees were employees of government agencies with jurisdiction over policy and program decisions and were purposively selected to include perspectives at different stages of model development.ResultsThe ‘co-production’ aspect of the participatory approach was highly valued. It was reported as an essential component of building understanding of the modelling process, and thus trust in the model and its outputs as a decision-support tool. The unique benefits of simulation modelling included its capacity to explore interactions of risk factors and combined interventions, and the impact of scaling up interventions. Participants also valued simulating new interventions prior to implementation in the real world, and the comprehensive mapping of evidence and its gaps to prioritise future research. The participatory aspect of simulation modelling was time and resource intensive and therefore most suited to high priority complex topics with contested options for intervening.ConclusionThese findings highlight the value of a participatory approach to dynamic simulation modelling to support its utility in applied health policy settings.

Biodiversity is severely threatened by habitat destruction. As a consequence of habitat destruction, the remaining habitat becomes more fragmented. This results in time-lagged population extirpations in remaining fragments when these are too small to support populations in the long term. If these time-lagged effects are ignored, the long-term impacts of habitat loss and fragmentation will be underestimated. We quantified the magnitude of time-lagged effects of habitat fragmentation for 157 nonvolant terrestrial mammal species in Madagascar, one of the biodiversity hotspots with the highest rates of habitat loss and fragmentation. We refined species' geographic ranges based on habitat preferences and elevation limits and then estimated which habitat fragments were too small to support a population for at least 100 years given stochastic population fluctuations. We also evaluated whether time-lagged effects would change the threat status of species according to the International Union for the Conservation of Nature (IUCN) Red List assessment framework. We used allometric relationships to obtain the population parameters required to simulate the population dynamics of each species, and we quantified the consequences of uncertainty in these parameter estimates by repeating the analyses with a range of plausible parameter values. Based on the median outcomes, we found that for 34 species (22% of the 157 species) at least 10% of their current habitat contained unviable populations. Eight species (5%) had a higher threat status when accounting for time-lagged effects. Based on 0.95-quantile values, following a precautionary principle, for 108 species (69%) at least 10% of their habitat contained unviable populations, and 51 species (32%) had a higher threat status. Our results highlight the need to preserve continuous habitat and improve connectivity between habitat fragments. Moreover, our findings may help to identify species for which time-lagged effects are most severe and which may thus benefit the most from conservation actions.

Tropical deforestation remains a critical issue given its present rate and a widespread consensus regarding its implications for the global carbon cycle and biodiversity. Nowhere is the problem more pronounced than in the Amazon basin, home to the world’s largest intact, tropical forest. This article addresses land cover change processes at household level in the Amazon basin, and to this end adapts a concept of domestic life cycle to the current institutional environment of tropical frontiers. In particular, it poses a risk minimization model that integrates demography with market-based factors such as transportation costs and accessibility. In essence, the article merges the theory of Chayanov with the household economy framework, in which markets exist for inputs (including labor), outputs, and capital. The risk model is specified and estimated, using survey data for 261 small producers along the Transamazon Highway in the eastern sector of the Brazilian Amazon.

Changing indigenous cultures, economies and landscapes: The case of the Tsimane’, Bolivian Amazon

Landscape structure, habitat fragmentation, and the ecology of insects

Biodiversity patterns are shaped by the combination of dispersal, environment, and stochasticity, but how the influence of these drivers changes in fragmented habitats remains poorly understood. We examined patterns and relationships among total (γ) and site-level (α) diversity, and site-to-site variation in composition (β-diversity) of tree communities in structurally contiguous and fragmented tropical rainforests within a human-modified landscape in India's Western Ghats. First, for the entire landscape, we assessed the extent to which habitat type (fragment or contiguous forest), space and environment explained variation in α-diversity and composition. Next, within fragments and contiguous forest, we assessed the relative contribution of spatial proximity, environmental similarity, and their joint effects in explaining β-diversity. We repeated these assessments with β-diversity values corrected for the confounding effects of α- and γ-diversity using null models (β-deviation). Lower γ-diversity of fragments resulted from both lower α- and β-diversity compared to contiguous forests. However, β-deviation did not differ between contiguous forests and fragments. Fragmented and contiguous forest clearly diverged in floristic composition, which was attributable to β-diversity being driven by differences in elevation and MAP. Within fragmented forest, neither space nor environment explained β-diversity, but β-deviation increased with greater elevational differences. In contiguous forests by comparison, environment alone (mainly elevation) explained the most variation in β-diversity and β-deviation of both species' occurrences and abundances. Spatial gradients in environmental conditions played a larger role than dispersal limitation in shaping diversity and composition of tree communities across forest fragments. Thus, location of remnant patches at different elevations was a key factor underlying site-to-site variability in species abundances across fragments. Understanding the environmental characteristics of remnant forests in human-modified landscapes, combined with knowledge of species-environment relationships across different functional groups, would therefore be important considerations for management and restoration planning in human-modified landscapes.