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)

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

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

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

Global patterns and drivers of alpine plant species richness

Aim Alpine ecosystems differ in area, macroenvironment and biogeographical history across the Earth, but the relationship between these factors and plant species richness is still unexplored. Here, we assess the global patterns of plant species richness in alpine ecosystems and their association with environmental, geographical and historical factors at regional and community scales. Location Global. Time period Data collected between 1923 and 2019. Major taxa studied Vascular plants. Methods We used a dataset representative of global alpine vegetation, consisting of 8,928 plots sampled within 26 ecoregions and six biogeographical realms, to estimate regional richness using sample‐based rarefaction and extrapolation. Then, we evaluated latitudinal patterns of regional and community richness with generalized additive models. Using environmental, geographical and historical predictors from global raster layers, we modelled regional and community richness in a mixed‐effect modelling framework. Results The latitudinal pattern of regional richness peaked around the equator and at mid‐latitudes, in response to current and past alpine area, isolation and the variation in soil pH among regions. At the community level, species richness peaked at mid‐latitudes of the Northern Hemisphere, despite a considerable within‐region variation. Community richness was related to macroclimate and historical predictors, with strong effects of other spatially structured factors. Main conclusions In contrast to the well‐known latitudinal diversity gradient, the alpine plant species richness of some temperate regions in Eurasia was comparable to that of hyperdiverse tropical ecosystems, such as the páramo. The species richness of these putative hotspot regions is explained mainly by the extent of alpine area and their glacial history, whereas community richness depends on local environmental factors. Our results highlight hotspots of species richness at mid‐latitudes, indicating that the diversity of alpine plants is linked to regional idiosyncrasies and to the historical prevalence of alpine ecosystems, rather than current macroclimatic gradients

Episodic Occurrence of Favourable Weather Constrains Recovery of a Cold Desert Shrubland After Fire

Key to the long-term resilience of dryland ecosystems is the recovery of foundation plant species following disturbance. In ecosystems with high interannual weather variability, understanding the influence of short-term environmental conditions on establishment of foundation species is essential for identifying vulnerable landscapes and developing restoration strategies. We asked how annual environmental conditions affect post-fire establishment of Artemisia tridentata, a shrub species that dominates landscapes across much of the western United States, and evaluated the influence of episodic establishment on population recovery. We collected A. tridentata stem samples from 33 plots in 12 prescribed fire sites that burned 8–11 years before sampling. We determined individual establishment years using annual growth rings. We measured seasonal soil environmental conditions at the study sites and asked if these conditions predicted annual establishment density. We then evaluated whether establishment patterns could be predicted by site-level climate or dominant subspecies. Finally, we tested the effect of the magnitude and frequency of post-fire establishment episodes on long-term population recovery. Annual post-fire recruitment of A. tridentata was driven by the episodic availability of spring soil moisture. Annual establishment was highest with wetter spring soils (relative influence [RI] = 19.4%) and later seasonal dry-down (RI = 11.8%) in the year of establishment. Establishment density declined greatly 4 to 5 years after fire (RI = 17.1%). Post-fire establishment patterns were poorly predicted by site-level mean climate (marginal R2 ≤ 0.18) and dominant subspecies (marginal R2 ≤ 0.43). Population recovery reflected the magnitude, but not the frequency, of early post-fire establishment pulses. Post-fire A. tridentata density and cover (measured 8–11 years after fire) were more strongly related to the magnitude of the largest establishment pulse than to establishment frequency, suggesting that population recovery may occur with a single favourable establishment year. Synthesis and applications. This study demonstrates the importance of episodic periods of favourable weather for long-term plant population recovery following disturbance. Management strategies that increase opportunities for seed availability to coincide with favourable weather conditions, such as retaining unburned patches or repeated seeding treatments, can improve restoration outcomes in high-priority areas

Post-glacial determinants of regional species pools in alpine grasslands

[Aim] Alpine habitats support unique biodiversity confined to high-elevation areas in the current interglacial. Plant diversity in these habitats may respond to area, environment, connectivity and isolation, yet these factors have been rarely evaluated in concert. Here we investigate major determinants of regional species pools in alpine grasslands, and the responses of their constituent species groups.[Location] European mountains below 50° N.[Time period] Between 1928 and 2019.[Major taxa studied] Vascular plants.[Methods] We compiled species pools from alpine grasslands in 23 regions, including 794 alpine species and 2,094 non-alpines. We used species–area relationships to test the influence of the extent of alpine areas on regional richness, and mixed-effects models to compare the effects of 12 spatial and environmental predictors. Variation in species composition was addressed by generalized dissimilarity models and by a coefficient of dispersal direction to assess historical links among regions.[Results] Pool sizes were partially explained by current alpine areas, but the other predictors largely contributed to regional differences. The number of alpine species was influenced by area, calcareous bedrock, topographic heterogeneity and regional isolation, while non-alpines responded better to connectivity and climate. Regional dissimilarity of alpine species was explained by isolation and precipitation, but non-alpines only responded to isolation. Past dispersal routes were correlated with latitude, with alpine species showing stronger connections among regions.[Main conclusions] Besides area effects, edaphic, topographic and spatio-temporal determinants are important to understand the organization of regional species pools in alpine habitats. The number of alpine species is especially linked to refugia and isolation, but their composition is explained by past dispersal and post-glacial environmental filtering, while non-alpines are generally influenced by regional floras. New research on the dynamics of alpine biodiversity should contextualize the determinants of regional species pools and the responses of species with different ecological profiles.The authors thank Daniela Gaspar for support in GIS analyses. B.J.-A. thanks the Marie Curie Clarín-COFUND program of the Principality of Asturias-EU (ACB17-26), the regional grant IDI/2018/000151, and the Spanish Research Agency grant AEI/ 10.13039/501100011033. J.V.R.-D. was supported by the ACA17-02FP7 Marie Curie COFUND-Clarín grant. G.P.M. was funded by US National Science Foundation award 1853665. C.M. was funded by grant no. 19-28491 of the Czech Science Foundation.Peer reviewe

Breaking Functional Connectivity into Components: A Novel Approach Using an Individual-Based Model, and First Outcomes

Landscape connectivity is a key factor determining the viability of populations in fragmented landscapes. Predicting ‘functional connectivity’, namely whether a patch or a landscape functions as connected from the perspective of a focal species, poses various challenges. First, empirical data on the movement behaviour of species is often scarce. Second, animal-landscape interactions are bound to yield complex patterns. Lastly, functional connectivity involves various components that are rarely assessed separately. We introduce the spatially explicit, individual-based model FunCon as means to distinguish between components of functional connectivity and to assess how each of them affects the sensitivity of species and communities to landscape structures. We then present the results of exploratory simulations over six landscapes of different fragmentation levels and across a range of hypothetical bird species that differ in their response to habitat edges. i) Our results demonstrate that estimations of functional connectivity depend not only on the response of species to edges (avoidance versus penetration into the matrix), the movement mode investigated (home range movements versus dispersal), and the way in which the matrix is being crossed (random walk versus gap crossing), but also on the choice of connectivity measure (in this case, the model output examined). ii) We further show a strong effect of the mortality scenario applied, indicating that movement decisions that do not fully match the mortality risks are likely to reduce connectivity and enhance sensitivity to fragmentation. iii) Despite these complexities, some consistent patterns emerged. For instance, the ranking order of landscapes in terms of functional connectivity was mostly consistent across the entire range of hypothetical species, indicating that simple landscape indices can potentially serve as valuable surrogates for functional connectivity. Yet such simplifications must be carefully evaluated in terms of the components of functional connectivity they actually predict