Coexistence patterns and diversity in a trait-based metacommunity on an environmental gradient

The dynamics of trait-based metacommunities have attracted much attention, but not much is known about how dispersal and spatial environmental variability mutually interact with each other to drive coexistence patterns and diversity. Here, we present a spatially explicit model of competition for two essential resources in a metacommunity on a one-dimensional environmental gradient. We find that both the strength of dispersal and the range of spatial environmental variability affect coexistence patterns, spatial structure, trait distribution, and local and regional diversity. Without dispersal, species are sorted according to their optimal growth conditions on the gradient. With the onset of dispersal, source-sink effects are initiated, which increases the effects of environmental filtering and interspecific competition and generates trait lumping, so that only a few species from an environment-defined trait range can survive. Interestingly, for very large dispersal rates, species distributions become spatially homogeneous, but nevertheless two species at the extreme ends of the trade-off curve can coexist for large environmental variability. Local species richness follows a classic hump-shaped dependence on dispersal rate, while local and regional diversity exhibit a pronounced peak for intermediate values of the environmental variability. Our findings provide important insights into the factors that shape the structure of trait-based metacommunities.

Effects of habitat destruction on coevolving metacommunities

Habitat destruction is a growing threat to biodiversity and ecosystem services. The ecological consequences of habitat loss and fragmentation involve reductions in species abundance and even the extinction of species and interactions. However, we do not yet understand how habitat loss can alter the coevolutionary trajectories of the remaining species or how coevolution, in turn, affects their response to habitat loss. To investigate this, we develop a spatially explicit model which couples metacommunity and coevolutionary dynamics. We show that, by changing the size, composition and structure of local networks, habitat destruction increases the diversity of coevolutionary outcomes across the landscape. Furthermore, we show that while coevolution dampens the negative effects of habitat destruction in mutualistic networks, its effects on the persistence of antagonistic communities are less predictable.

Effects of demand-side restrictions on high-deforestation palm oil in Europe on deforestation and emissions in Indonesia

Demand-side restrictions on high-deforestation commodities are expanding as a climate policy, but their impact on reducing tropical deforestation and emissions has yet to be quantified. Here we model the effects of demand-side restrictions on high-deforestation palm oil in Europe on deforestation and emissions in Indonesia. We do so by integrating a model of global trade with a spatially explicit model of land-use change in Indonesia. We estimate a European ban on high-deforestation palm oil from 2000–2015 would have led to a 8.9% global price premium on low-deforestation palm oil, resulting in 21,374 ha/yr (1.60%) less deforestation and 21.1 million tCO2/yr (1.91%) less emissions from deforestation in Indonesia relative to what occurred. A hypothetical Indonesia-wide carbon price would have achieved equivalent emission reductions at $0.81/tCO2. Impacts of a ban are small because: 52% of Europe’s imports of high-deforestation palm oil would have shifted to non-participating countries; the price elasticity of supply of high-deforestation oil palm cropland is small (0.13); and conversion to oil palm was responsible for only 32% of deforestation in Indonesia. If demand-side restrictions succeed in substantially reducing deforestation, it is likely to be through non-price pathways.

Earth history events shaped the evolution of uneven biodiversity across tropical moist forests

Far from a uniform band, the biodiversity found across Earth’s tropical moist forests varies widely between the high diversity of the Neotropics and Indomalaya and the relatively lower diversity of the Afrotropics. Explanations for this variation across different regions, the “pantropical diversity disparity” (PDD), remain contentious, due to difficulty teasing apart the effects of contemporary climate and paleoenvironmental history. Here, we assess the ubiquity of the PDD in over 150,000 species of terrestrial plants and vertebrates and investigate the relationship between the present-day climate and patterns of species richness. We then investigate the consequences of paleoenvironmental dynamics on the emergence of biodiversity gradients using a spatially explicit model of diversification coupled with paleoenvironmental and plate tectonic reconstructions. Contemporary climate is insufficient in explaining the PDD; instead, a simple model of diversification and temperature niche evolution coupled with paleoaridity constraints is successful in reproducing the variation in species richness and phylogenetic diversity seen repeatedly among plant and animal taxa, suggesting a prevalent role of paleoenvironmental dynamics in combination with niche conservatism. The model indicates that high biodiversity in Neotropical and Indomalayan moist forests is driven by complex macroevolutionary dynamics associated with mountain uplift. In contrast, lower diversity in Afrotropical forests is associated with lower speciation rates and higher extinction rates driven by sustained aridification over the Cenozoic. Our analyses provide a mechanistic understanding of the emergence of uneven diversity in tropical moist forests across 110 Ma of Earth’s history, highlighting the importance of deep-time paleoenvironmental legacies in determining biodiversity patterns.

PREDICTING THE INFRARED UAV IMAGERY OVER THE COAST

The infrared (IR) imagery provides additional information to the visible (red-green-blue, RGB) about vegetation, soil, water, mineral, or temperature, and has become essential for various disciplines, such as geology, hydrology, ecology, archeology, meteorology or geography. The integration of the IR sensors, ranging from near-IR (NIR) to thermal-IR through mid-IR, constitutes a baseline for Earth Observation satellites but not for unmanned airborne vehicles (UAV). Given the hyperspatial and hypertemporal characteristics associated with the UAV survey, it is relevant to benefit from the IR waveband in addition to the visible imagery for mapping purposes. This paper proposes to predict the NIR reflectance from RGB digital number predictors collected with a consumer-grade UAV over a structurally and compositionally complex coastal area. An array of 15 000 data, distributed into calibration, validation and test datasets across 15 representative coastal habitats, was used to build and compare the performance of the standard least squares, decision tree, boosted tree, bootstrap forest and fully connected neural network (NN) models. The NN family surpassed the four other ones, and the best NN model (R2 = 0.67) integrated two hidden layers provided, each, with five nodes of hyperbolic tangent and five nodes of Gaussian activation functions. This perceptron enabled to produce a NIR reflectance spatially-explicit model deprived of original artifacts due to the flight constraints. At the habitat scale, sedimentary and dry vegetation environments were satisfactorily predicted (R2 > 0.6), contrary to the healthy vegetation (R2 < 0.2). Those innovative findings will be useful for scientists and managers tasked with hyperspatial and hypertemporal mapping.

A spatially explicit model for estimating risks of pesticide exposure to bird populations

Pesticides are used widely in agriculture and have the potential to affect non-target organisms, including birds. We developed an integrated modeling system to allow for spatially-explicit evaluation of potential impacts to bird populations following exposures to pesticides. Our novel methodology builds upon three existing models: the Terrestrial Investigation Model (TIM), the Markov Chain Nest Productivity Model (MCnest), and HexSim to simulate population dynamics. We parameterized the integrated modeling system using information required under the Federal Insecticide, Fungicide, and Rodenticide Act, together with species habitat and life history data available from the scientific literature as well as landcover data representing agricultural areas and species habitat. Our case study of the federally threatened California Gnatcatcher (Polioptila californica) illustrates how the integrated modeling system can estimate the population-scale consequences of pesticide applications. We simulated impacts from two insecticides applied to wheat: one causing mortality (survival stressor), and the other causing reproductive failure (reproductive stressor). We observed declines in simulated gnatcatcher abundance and changes in the species’ distribution following applications of each pesticide; however, the impacts of the two pesticides were different. Our methodology attempts to strike a balance between biological realism and model complexity and should be applicable to a wide array of species, systems, and stressors.

Intrinsic ecological dynamics drive biodiversity turnover in model metacommunities

Turnover of species composition through time is frequently observed in ecosystems. It is often interpreted as indicating the impact of changes in the environment. Continuous turnover due solely to ecological dynamics—species interactions and dispersal—is also known to be theoretically possible; however the prevalence of such autonomous turnover in natural communities remains unclear. Here we demonstrate that observed patterns of compositional turnover and other important macroecological phenomena can be reproduced in large spatially explicit model ecosystems, without external forcing such as environmental change or the invasion of new species into the model. We find that autonomous turnover is triggered by the onset of ecological structural instability—the mechanism that also limits local biodiversity. These results imply that the potential role of autonomous turnover as a widespread and important natural process is underappreciated, challenging assumptions implicit in many observation and management tools. Quantifying the baseline level of compositional change would greatly improve ecological status assessments.

The ecology of heterogeneity: soil bacterial communities and C dynamics

&lt;p&gt;Heterogeneity is a fundamental property of soil that is often overlooked in microbial ecology. Although it is generally accepted that the heterogeneity of soil underpins the emergence and maintenance of microbial diversity, the profound and far-reaching consequences that heterogeneity can have on many aspects of microbial ecology and activity have yet to be fully apprehended and have not been fully integrated into our understanding of microbial functioning.&lt;/p&gt;&lt;p&gt;Heterogeneity in soils has multiple facets, from the molecular heterogeneity of the diversity of substrate available, the activity heterogeneity due to the activity of microbial species and the spatial heterogeneity of the soil structure and the distribution of organisms.&lt;/p&gt;&lt;p&gt;In this contribution we present a simple, spatially explicit model that can be used to understand how the interactions between the heterogeneity of decomposers (in terms of species and spatial distribution) and environmental heterogeneity (in terms of the diversity of substrates and their spatial distribution) affect the bacterial decomposition of organic matter. We found that environmental heterogeneity is a key element in determining the variability of the decomposition process.&lt;/p&gt;

Spatially Explicit Model for Anaerobic Co-Digestion Facilities Location and Pre-Dimensioning Considering Spatial Distribution of Resource Supply and Biogas Yield in Northwest Portugal

The high volumes of animal manure and sewage sludge, as a consequence of the development of intensive and specialized cattle dairy farms in peri-urban areas, pose challenges to local environmental quality and demands for systems innovation. Besides these negative impacts, energy recovery from biogas produced in anaerobic co-digestion processes should contribute to local sustainable development. This research considers technical data obtained from the optimization of biomethanization processes using sewage sludge and cattle manure liquid fraction, aiming to develop a spatially explicit model including multicriteria evaluation and an analytical hierarchy process to locate biogas production facilities, allocate energy resources and consider biogas unit pre-dimensioning analysis. According to the biophysical conditions and socioeconomic dynamics of the study area (Vila do Conde, Northwest Portugal), a spatially explicit model using multicriteria and multiobjective techniques allowed the definition of suitable locations, as well as the allocation of resources and support pre-dimensioning of biogas facilities. A p-median model allowed us to allocate resources and pre-dimensioning biogas facilities according to distance and accessibility elements. The results indicate: (i) the location of areas with adequate environmental conditions and socioeconomic suitability advantages to install biogas production facilities, and (ii) the ability to compare the options of centralized or distributed location alternatives and associated pre-dimensioning.