Uppermost global tree elevations are primarily limited by low temperature or insufficient moisture.

The impact of anthropogenic global warming has induced significant upward dispersal of trees to higher elevations at alpine treelines. Assessing vertical deviation from current uppermost tree distributions to potential treeline positions is crucial for understanding ecosystem responses to evolving global climate. However, due to data resolution constraints and research scale limitation, comprehending the global pattern of alpine treeline elevations and driving factors remains challenging. This study constructed a comprehensive quasi-observational dataset of uppermost tree distribution across global mountains using Google Earth imagery. Validating the isotherm of mean growing-season air temperature at 6.6 ± 0.3°C as the global indicator of thermal treeline, we found that around two-thirds of uppermost tree distribution records significantly deviated from it. Drought conditions constitute the primary driver in 51% of cases, followed by mountain elevation effect which indicates surface heat (27%). Our analyses underscore the multifaceted determinants of global patterns of alpine treeline, explaining divergent treeline responses to climate warming. Moisture, along with temperature and disturbance, plays the most fundamental roles in understanding global variation of alpine treeline elevation and forecasting alpine treeline response to ongoing global warming.

Interactions and constraints in model species response to environmental heteroscedasticity.

Increasing environmental variability could exacerbate the effects of climate change on ecological processes such as population dynamics, or positive and negative effects (favorable or unfavorable weather) could balance. Such a balance could depend on constraints of the processes. Biological and spatial constraints are represented in a spatially explicit individual based simulation of an ecotone reduced to two species on a single environmental gradient. The effects of climate amelioration are simulated from a plant's-eye-view by increasing the establishment and decreasing the mortality rates. Variability is introduced as a random multiplier of these rates, and the strength of the variation is increased through the period of climate change. The biological constraints limit change in the rates, and the extent of the simulation grid represents a spatial constraint. A small increase in environmental variation, multiplied through time with climate change, increases extinction rates. The biological and spatial constraints have little effect on the response of populations. Instead, competition, based on the form of the species response functions to the environmental gradient at the point where they intersect, determines differences in population responses. Positive and negative variations in the environment do not balance because the responses are hierarchical and asymmetric. Differences persist because extinction during a negative anomaly cannot be reversed by a later positive one.

Climate Shocks and Migration: An Agent-Based Modeling Approach.

This is a study of migration responses to climate shocks. We construct an agent-based model that incorporates dynamic linkages between demographic behaviors, such as migration, marriage, and births, and agriculture and land use, which depend on rainfall patterns. The rules and parameterization of our model are empirically derived from qualitative and quantitative analyses of a well-studied demographic field site, Nang Rong district, Northeast Thailand. With this model, we simulate patterns of migration under four weather regimes in a rice economy: 1) a reference, 'normal' scenario; 2) seven years of unusually wet weather; 3) seven years of unusually dry weather; and 4) seven years of extremely variable weather. Results show relatively small impacts on migration. Experiments with the model show that existing high migration rates and strong selection factors, which are unaffected by climate change, are likely responsible for the weak migration response.

Diversity differs among three variations of the stress gradients hypothesis in two representations of niche space.

How does the stress-gradient hypothesis affect coexistence in relation to established theory? For two orthogonal stress gradients, a spatially explicit agent based simulation is used to project diversity for simple competitive and facilitative interactions and for three variations of the stress-gradient hypothesis: intraspecific and interspecific competitive and facilitative interactions are a function of the abiotic environment; interactions are relative to species-specific fitness along gradients; or interaction is fixed by species regardless of the abiotic environment. Simulations are run with two orthogonal environmental gradients for two representations of niche. Facilitation can increase diversity by maintaining larger source populations and thus higher establishment rates and sink populations. With species hierarchically related in niche space, the simulations show that positive interactions and changing interactions along a stress gradient maintain greater diversity through intraspecific competition that is effective where dominance would occur and through facilitation where stress is high. A changing environment that favors some species and harms others decreases diversity in the hierarchical cases, where poor competitors most likely subject to interspecific interaction respond most strongly. Diversity outcomes differ among the three stress gradient variations because the intensity of interactions differs across the environmental gradients, not because of change in the environment.

Neighborhood functions alter unbalanced facilitation on a stress gradient.

The stress-gradient hypothesis states that individual and species competitive and facilitative effects change in relative importance or intensity along environmental gradients of stress. The importance of the number of facilitators in the neighborhood of a potential beneficiary has not been explored. Evenly distributed and stress-correlated facilitation and the increase in the intensity of facilitation with neighbors as linear, logarithmic, and unimodal functions is simulated for two hypothetical species, both of which improve the local environment. The mutualism is unbalanced in that the establishment of one species is enhanced by neighbors more than the other. Compared to no facilitation or evenly distributed facilitation, the stress gradient produces more edges in the spatially advancing population, more overall intensity of facilitation, and more individuals further advanced into the area of higher stress; the more enhanced species has increased population relative to the other - to the point where they are equal. Among three neighborhood functions, little difference exists in outcomes between the linear and logarithmic functions, but the unimodal function, which shifts peak facilitation intensity to fewer neighbors, increases the above state variables more than the differences between the even and stress gradient facilitation scenarios; the population of the beneficiary species exceeds that of the other. Different neighborhood functions change the effects of spatial pattern on the biological outcome. The unbalanced mutualism may be important where additional species alter the basic interaction in the high stress area of the environmental gradient, such as ecotones where the spatial pattern becomes central to facilitation.

Changing crops in response to climate: virtual Nang Rong, Thailand in an agent based simulation.

The effects of extended climatic variability on agricultural land use were explored for the type of system found in villages of northeastern Thailand. An agent based model developed for the Nang Rong district was used to simulate land allotted to jasmine rice, heavy rice, cassava, and sugar cane. The land use choices in the model depended on likely economic outcomes, but included elements of bounded rationality in dependence on household demography. The socioeconomic dynamics are endogenous in the system, and climate changes were added as exogenous drivers. Villages changed their agricultural effort in many different ways. Most villages reduced the amount of land under cultivation, primarily with reduction in jasmine rice, but others did not. The variation in responses to climate change indicates potential sensitivity to initial conditions and path dependence for this type of system. The differences between our virtual villages and the real villages of the region indicate effects of bounded rationality and limits on model applications.

Design of an Agent-Based Model to Examine Population-Environment Interactions in Nang Rong District, Thailand.

The design of an Agent-Based Model (ABM) is described that integrates Social and Land Use Modules to examine population-environment interactions in a former agricultural frontier in Northeastern Thailand. The ABM is used to assess household income and wealth derived from agricultural production of lowland, rain-fed paddy rice and upland field crops in Nang Rong District as well as remittances returned to the household from family migrants who are engaged in off-farm employment in urban destinations. The ABM is supported by a longitudinal social survey of nearly 10,000 households, a deep satellite image time-series of land use change trajectories, multi-thematic social and ecological data organized within a GIS, and a suite of software modules that integrate data derived from an agricultural cropping system model (DSSAT - Decision Support for Agrotechnology Transfer) and a land suitability model (MAXENT - Maximum Entropy), in addition to multi-dimensional demographic survey data of individuals and households. The primary modules of the ABM are the Initialization Module, Migration Module, Assets Module, Land Suitability Module, Crop Yield Module, Fertilizer Module, and the Land Use Change Decision Module. The architecture of the ABM is described relative to module function and connectivity through uni-directional or bi-directional links. In general, the Social Modules simulate changes in human population and social networks, as well as changes in population migration and household assets, whereas the Land Use Modules simulate changes in land use types, land suitability, and crop yields. We emphasize the description of the Land Use Modules - the algorithms and interactions between the modules are described relative to the project goals of assessing household income and wealth relative to shifts in land use patterns, household demographics, population migration, social networks, and agricultural activities that collectively occur within a marginalized environment that is subjected to a suite of endogenous and exogenous dynamics.

Land Use Change on Household Farms in the Ecuadorian Amazon: Design and Implementation of an Agent-Based Model.

This paper describes the design and implementation of an Agent-Based Model (ABM) used to simulate land use change on household farms in the Northern Ecuadorian Amazon (NEA). The ABM simulates decision-making processes at the household level that is examined through a longitudinal, socio-economic and demographic survey that was conducted in 1990 and 1999. Geographic Information Systems (GIS) are used to establish spatial relationships between farms and their environment, while classified Landsat Thematic Mapper (TM) imagery is used to set initial land use/land cover conditions for the spatial simulation, assess from-to land use/land cover change patterns, and describe trajectories of land use change at the farm and landscape levels. Results from prior studies in the NEA provide insights into the key social and ecological variables, describe human behavioral functions, and examine population-environment interactions that are linked to deforestation and agricultural extensification, population migration, and demographic change. Within the architecture of the model, agents are classified as active or passive. The model comprises four modules, i.e., initialization, demography, agriculture, and migration that operate individually, but are linked through key household processes. The main outputs of the model include a spatially-explicit representation of the land use/land cover on survey and non-survey farms and at the landscape level for each annual time-step, as well as simulated socio-economic and demographic characteristics of households and communities. The work describes the design and implementation of the model and how population-environment interactions can be addressed in a frontier setting. The paper contributes to land change science by examining important pattern-process relations, advocating a spatial modeling approach that is capable of synthesizing fundamental relationships at the farm level, and links people and environment in complex ways.

How's the Flu Getting Through? Landscape genetics suggests both humans and birds spread H5N1 in Egypt.

First introduced to Egypt in 2006, H5N1 highly pathogenic avian influenza has resulted in the death of millions of birds and caused over 350 infections and at least 117 deaths in humans. After a decade of viral circulation, outbreaks continue to occur and diffusion mechanisms between poultry farms remain unclear. Using landscape genetics techniques, we identify the distance models most strongly correlated with the genetic relatedness of the viruses, suggesting the most likely methods of viral diffusion within Egyptian poultry. Using 73 viral genetic sequences obtained from infected birds throughout northern Egypt between 2009 and 2015, we calculated the genetic dissimilarity between H5N1 viruses for all eight gene segments. Spatial correlation was evaluated using Mantel tests and correlograms and multiple regression of distance matrices within causal modeling and relative support frameworks. These tests examine spatial patterns of genetic relatedness, and compare different models of distance. Four models were evaluated: Euclidean distance, road network distance, road network distance via intervening markets, and a least-cost path model designed to approximate wild waterbird travel using niche modeling and circuit theory. Samples from backyard farms were most strongly correlated with least cost path distances. Samples from commercial farms were most strongly correlated with road network distances. Results were largely consistent across gene segments. Results suggest wild birds play an important role in viral diffusion between backyard farms, while commercial farms experience human-mediated diffusion. These results can inform avian influenza surveillance and intervention strategies in Egypt.

Predicting Avian Influenza Co-Infection with H5N1 and H9N2 in Northern Egypt.

Human outbreaks with avian influenza have been, so far, constrained by poor viral adaptation to non-avian hosts. This could be overcome via co-infection, whereby two strains share genetic material, allowing new hybrid strains to emerge. Identifying areas where co-infection is most likely can help target spaces for increased surveillance. Ecological niche modeling using remotely-sensed data can be used for this purpose. H5N1 and H9N2 influenza subtypes are endemic in Egyptian poultry. From 2006 to 2015, over 20,000 poultry and wild birds were tested at farms and live bird markets. Using ecological niche modeling we identified environmental, behavioral, and population characteristics of H5N1 and H9N2 niches within Egypt. Niches differed markedly by subtype. The subtype niches were combined to model co-infection potential with known occurrences used for validation. The distance to live bird markets was a strong predictor of co-infection. Using only single-subtype influenza outbreaks and publicly available ecological data, we identified areas of co-infection potential with high accuracy (area under the receiver operating characteristic (ROC) curve (AUC) 0.991).

Post-fire regeneration strategies of Californian coastal sage shrubs.

Regeneration methods for coastal sage srub vegetation after fire were studied in the coastal Santa Monica Mountains of southern California. Six sites were sampled two years after a large fire of fall, 1978. The intensity of fire varied. Foliar cover and flowering incidence were recorded for individuals regenerating by resprouting or from seed. Resprouting plants contributed most to post-fire recovery, comprising 95% of the relative foliar shrub cover; 84% of resprout and 47% of seedling cover had flowered. An ANOVA of reproductive mode and fire intensity indicates that resprout total cover and individual size are significantly greater than those of seedlings, regardless of fire intensity. Among sites the average foliar cover of resprouts exceeded that of seedlings by factors ranging from 9 to 63. All coastal sage species examined resprout, although the potential vigor of resprouting appears to vary widely within genera (e.g. Encelia, Eriogonum, and Salvia) and even within species. In the second growing season following fire seedling density increased due to seeds shed by resprouted shrubs. Most of the cover on these stands of coastal sage scrub is destined to be either crown-sprouted individuals or their progeny.

Optimized spatial priorities for biodiversity conservation in China: a systematic conservation planning perspective.

By addressing several key features overlooked in previous studies, i.e. human disturbance, integration of ecosystem- and species-level conservation features, and principles of complementarity and representativeness, we present the first national-scale systematic conservation planning for China to determine the optimized spatial priorities for biodiversity conservation. We compiled a spatial database on the distributions of ecosystem- and species-level conservation features, and modeled a human disturbance index (HDI) by aggregating information using several socioeconomic proxies. We ran Marxan with two scenarios (HDI-ignored and HDI-considered) to investigate the effects of human disturbance, and explored the geographic patterns of the optimized spatial conservation priorities. Compared to when HDI was ignored, the HDI-considered scenario resulted in (1) a marked reduction (∼9%) in the total HDI score and a slight increase (∼7%) in the total area of the portfolio of priority units, (2) a significant increase (∼43%) in the total irreplaceable area and (3) more irreplaceable units being identified in almost all environmental zones and highly-disturbed provinces. Thus the inclusion of human disturbance is essential for cost-effective priority-setting. Attention should be targeted to the areas that are characterized as moderately-disturbed, <2,000 m in altitude, and/or intermediately- to extremely-rugged in terrain to identify potentially important regions for implementing cost-effective conservation. We delineated 23 primary large-scale priority areas that are significant for conserving China's biodiversity, but those isolated priority units in disturbed regions are in more urgent need of conservation actions so as to prevent immediate and severe biodiversity loss. This study presents a spatially optimized national-scale portfolio of conservation priorities--effectively representing the overall biodiversity of China while minimizing conflicts with economic development. Our results offer critical insights for current conservation and strategic land-use planning in China. The approach is transferable and easy to implement by end-users, and applicable for national- and local-scale systematic conservation prioritization practices.

Simulated village locations in Thailand: A multi-scale model including a neural network approach.

The simulation of rural land use systems, in general, and rural settlement dynamics in particular has developed with synergies of theory and methods for decades. Three current issues are: linking spatial patterns and processes, representing hierarchical relations across scales, and considering nonlinearity to address complex non-stationary settlement dynamics. We present a hierarchical simulation model to investigate complex rural settlement dynamics in Nang Rong, Thailand. This simulation uses sub-models to allocate new villages at three spatial scales. Regional and sub-regional models, which involve a nonlinear space-time autoregressive model implemented in a neural network approach, determine the number of new villages to be established. A dynamic village niche model, establishing pattern-process link, was designed to enable the allocation of villages into specific locations. Spatiotemporal variability in model performance indicates the pattern of village location changes as a settlement frontier advances from rice-growing lowlands to higher elevations. Experiments results demonstrate this simulation model can enhance our understanding of settlement development in Nang Rong and thus gain insight into complex land use systems in this area.