Opposing Patterns of Spatial Synchrony in Lyme Disease Incidence.

Incidence of Lyme disease, a tick-borne illness prevalent in the US, is increasing in endemic regions and regions with no previous history of the disease, significantly impacting public health. We examined space-time patterns of Lyme disease incidence and the influence of ecological and social factors on spatial synchrony, i.e., correlated incidence fluctuations across US counties. Specifically, we addressed these questions: Does Lyme disease incidence exhibit spatial synchrony? If so, what geographic patterns does Lyme disease synchrony exhibit? Are geographic patterns of disease synchrony related to weather, land cover, access to health care, or tick-borne disease awareness? How do effects of these variables on Lyme disease synchrony differ geographically? We used network analysis and matrix regression to examine geographical patterns of Lyme disease synchrony and their potential mechanisms in 399 counties in the eastern and Midwestern US. We found two distinct regions of synchrony in Northeast and upper Midwest regions exhibiting opposing temporal fluctuations in incidence. Spatial patterns of Lyme disease synchrony were partly explained by land cover, weather, poverty, and awareness of tick-borne illness, with significant predictive variables changing regionally. However, the two regions may have become more synchronous over time, potentially leading to higher-amplitude nation-wide fluctuations in disease incidence.

Seasonality in Environment and Population Processes Alters Population Spatial Synchrony.

AbstractPopulation spatial synchrony-the tendency for temporal population fluctuations to be correlated across locations-is common and important to metapopulation stability and persistence. One common cause of spatial synchrony, termed the Moran effect, occurs when populations respond to environmental fluctuations, such as weather, that are correlated over space. Although the degree of spatial synchrony in environmental fluctuations can differ between seasons and different population processes occur in different seasons, the impact on population spatial synchrony is uncertain because prior work has largely assumed that the spatial synchrony of environmental fluctuations and their effect on populations are consistent over annual sampling intervals. We used theoretical models to examine how seasonality in population processes and the spatial synchrony of environmental drivers affect population spatial synchrony. We found that population spatial synchrony can depend not only on the spatial synchrony of environmental drivers but also on the degree to which environmental fluctuations are correlated across seasons, locally, and across space. Moreover, measurements of synchrony from "snapshot" population censuses may not accurately reflect synchrony during other parts of the year. Together, these results show that neglecting seasonality in environmental conditions and population processes is consequential for understanding population spatial synchrony and its driving mechanisms.

Using climate-driven leaf phenology and growth to improve predictions of gross primary productivity in North American forests.

Forest ecosystems are an important sink for terrestrial carbon sequestration. Hence, accurate modeling of the intra- and interannual variability of forest photosynthetic productivity remains a key objective in global biology. Applying climate-driven leaf phenology and growth in models may improve predictions of the forest gross primary productivity (GPP). We used a dynamic non-structural carbohydrates (NSC) model (FORCCHN2) that couples leaf development and phenology to investigate the relationships among photosynthesis and environmental factors. FORCCHN2 simulates spring and autumn phenological events from heat and chilling, respectively. Leaf area index data from satellites along with climate data estimated localized phenological parameters. NSC limitation, immediate temperature, accumulated heat, and growth potential comprised a daily leaf-growth model. Functionally, leaf growth was decoupled from photosynthesis. Leaf biomass determined overall photosynthetic production. We compared this model with outputs of the other six terrestrial biospheric models and with observations from the North American Carbon Program Site Interim Synthesis in 18 forest sites. This model improved the predicted performance of yearly GPP with a 57%-210% increase in correlation (median) and up to a 102% reduction in biases (median), compared to three prognostic models and three prescribed models. At the North America continental scale, the model predicted the average annual GPP of 7.38 Pg C/year from forest ecosystems during 1985-2016. The results showed an increasing trend of GPP in North America (1.0 Pg C/decade). The inclusion of climate-driven phenology and growth has a significant potential for improving dynamic vegetation models, and promotes a further understanding of the complex relationship between environment and photosynthesis.

Soil aggregates as biogeochemical reactors and implications for soil-atmosphere exchange of greenhouse gases-A concept.

Soil-atmosphere exchange significantly influences the global atmospheric abundances of carbon dioxide (CO2 ), methane (CH4 ), and nitrous oxide (N2 O). These greenhouse gases (GHGs) have been extensively studied at the soil profile level and extrapolated to coarser scales (regional and global). However, finer scale studies of soil aggregation have not received much attention, even though elucidating the GHG activities at the full spectrum of scales rather than just coarse levels is essential for reducing the large uncertainties in the current atmospheric budgets of these gases. Through synthesizing relevant studies, we propose that aggregates, as relatively separate micro-environments embedded in a complex soil matrix, can be viewed as biogeochemical reactors of GHGs. Aggregate reactivity is determined by both aggregate size (which determines the reactor size) and the bulk soil environment including both biotic and abiotic factors (which further influence the reaction conditions). With a systematic, dynamic view of the soil system, implications of aggregate reactors for soil-atmosphere GHG exchange are determined by both an individual reactor's reactivity and dynamics in aggregate size distributions. Emerging evidence supports the contention that aggregate reactors significantly influence soil-atmosphere GHG exchange and may have global implications for carbon and nitrogen cycling. In the context of increasingly frequent and severe disturbances, we advocate more analyses of GHG activities at the aggregate scale. To complement data on aggregate reactors, we suggest developing bottom-up aggregate-based models (ABMs) that apply a trait-based approach and incorporate soil system heterogeneity.

Terrestrial LiDAR-derived non-destructive woody biomass estimates for 10 hardwood species in Virginia.

This article contains data related to the research article entitled "Assessing terrestrial laser scanning for developing non-destructive biomass allometry" (Stovall et al., 2018 [1]) and presents 258 terrestrial LiDAR-derived estimates of tree volume and biomass. The terrestrial LiDAR acquisitions were completed in the Center for Tropical Forest Science - Forest Global Earth Observatory (CTFS-ForestGEO) plot in Front Royal, Virginia, USA. The data includes tree diameter at breast height (DBH), total tree height, tree length (correcting for tree lean), average wood density, estimated wood volume, and dry weight or biomass for all trees. These data were used to develop aboveground biomass models [1] and the reader is referred to this study for additional information, interpretation, and reflection on applying this data.

Biodiversity matters in feedbacks between climate change and air quality: a study using an individual-based model.

Air quality is closely associated with climate change via the biosphere because plants release large quantities of volatile organic compounds (VOC) that mediate both gaseous pollutants and aerosol dynamics. Earlier studies, which considered only leaf physiology and simply scale up from leaf-level enhancements of emissions, suggest that climate warming enhances whole forest VOC emissions, and these increased VOC emissions aggravate ozone pollution and secondary organic aerosol formation. Using an individual-based forest VOC emissions model, UVAFME-VOC, that simulates system-level emissions by explicitly simulating forest community dynamics to the individual tree level, ecological competition among the individuals of differing size and age, and radiative transfer and leaf function through the canopy, we find that climate warming only sometimes stimulates isoprene emissions (the single largest source of non-methane hydrocarbon) in a southeastern U.S. forest. These complex patterns result from the combination of higher temperatures' stimulating emissions at the leaf level but decreasing the abundance of isoprene-emitting taxa at the community level by causing a decline in the abundance of isoprene-emitting species (Quercus spp.). This ecological effect eventually outweighs the physiological one, thus reducing overall emissions. Such reduced emissions have far-reaching implications for the climate-air-quality relationships that have been established on the paradigm of warming-enhancement VOC emissions from vegetation. This local scale modeling study suggests that community ecology rather than only individual physiology should be integrated into future studies of biosphere-climate-chemistry interactions.

Evaluating carbon fluxes of global forest ecosystems by using an individual tree-based model FORCCHN.

The carbon budget of forest ecosystems, an important component of the terrestrial carbon cycle, needs to be accurately quantified and predicted by ecological models. As a preamble to apply the model to estimate global carbon uptake by forest ecosystems, we used the CO2 flux measurements from 37 forest eddy-covariance sites to examine the individual tree-based FORCCHN model's performance globally. In these initial tests, the FORCCHN model simulated gross primary production (GPP), ecosystem respiration (ER) and net ecosystem production (NEP) with correlations of 0.72, 0.70 and 0.53, respectively, across all forest biomes. The model underestimated GPP and slightly overestimated ER across most of the eddy-covariance sites. An underestimation of NEP arose primarily from the lower GPP estimates. Model performance was better in capturing both the temporal changes and magnitude of carbon fluxes in deciduous broadleaf forest than in evergreen broadleaf forest, and it performed less well for sites in Mediterranean climate. We then applied the model to estimate the carbon fluxes of forest ecosystems on global scale over 1982-2011. This application of FORCCHN gave a total GPP of 59.41±5.67 and an ER of 57.21±5.32PgCyr-1 for global forest ecosystems during 1982-2011. The forest ecosystems over this same period contributed a large carbon storage, with total NEP being 2.20±0.64PgCyr-1. These values are comparable to and reinforce estimates reported in other studies. This analysis highlights individual tree-based model FORCCHN could be used to evaluate carbon fluxes of forest ecosystems on global scale.

Forests and ozone: productivity, carbon storage, and feedbacks.

Tropospheric ozone is a serious air-pollutant, with large impacts on plant function. This study demonstrates that tropospheric ozone, although it damages plant metabolism, does not necessarily reduce ecosystem processes such as productivity or carbon sequestration because of diversity change and compensatory processes at the community scale ameliorate negative impacts at the individual level. This study assesses the impact of ozone on forest composition and ecosystem dynamics with an individual-based gap model that includes basic physiology as well as species-specific metabolic properties. Elevated tropospheric ozone leads to no reduction of forest productivity and carbon stock and to increased isoprene emissions, which result from enhanced dominance by isoprene-emitting species (which tolerate ozone stress better than non-emitters). This study suggests that tropospheric ozone may not diminish forest carbon sequestration capacity. This study also suggests that, because of the often positive relationship between isoprene emission and ozone formation, there is a positive feedback loop between forest communities and ozone, which further aggravates ozone pollution.

Carbon storage in old-growth forests of the Mid-Atlantic: toward better understanding the eastern forest carbon sink.

Few old-growth stands remain in the matrix of secondary forests that dominates the eastern North American landscape. These remnant stands offer insight on the potential carbon (C) storage capacity of now-recovering secondary forests. We surveyed the remaining old-growth forests on sites characteristic of the general Mid-Atlantic United States and estimated the size of multiple components of forest C storage. Within and between old-growth stands, variability in C density is high and related to overstory tree species composition. The sites contain 219 ± 46 Mg C/ha (mean ± SD), including live and dead aboveground biomass, leaf litter, and the soil O horizon, with over 20% stored in downed wood and snags. Stands dominated by tulip poplar (Liriodendron tulipifera) store the most live biomass, while the mixed oak (Quercus spp.) stands overall store more dead wood. Total C density is 30% higher (154 Mg C/ha), and dead wood C density is 1800% higher (46 Mg C/ha) in the old-growth forests than in the surrounding younger forests (120 and 5 Mg C/ha, respectively). The high density of dead wood in old growth relative to secondary forests reflects a stark difference in historical land use and, possibly, the legacy of the local disturbance (e.g., disease) history. Our results demonstrate the potential for dead wood to maintain the sink capacity of secondary forests for many decades to come.

Coyote (Canis latrans) mammalian prey diet shifts in response to seasonal vegetation change.

Drylands typically have strong seasonal variation in rainfall and primary productivity. This study examines the effects of seasonal change in grass-derived resource availability on the base of the food chain of a mammalian predator. Seasonal changes in live grass cover were measured in two vegetation types at the Sevilleta National Wildlife Refuge in central New Mexico, USA. Non-invasive genetic sampling of scat was used to identify individuals in the local coyote (Canis latrans) population. Stable carbon and nitrogen isotope analysis of hair removed from scats of 45 different coyotes was used to assess seasonal variation in the diet of mammalian coyote prey that came from C4 grasses. Live grass cover increased from the spring to the summer and fall; contribution of C4 grasses to the diet of mammalian coyote prey increased from the summer to the fall and was higher in grassland areas. There were significant differences in the seasonal patterns in the prey diet between grassland and shrubland areas.

Retrospective assessment of dryland soil stability in relation to grazing and climate change.

Accelerated soil erosion is an aspect of dryland degradation that is affected by repeated intense drought events and land management activities such as commercial livestock grazing. A soil stability index (SSI) that detects the erosion status and susceptibility of a landscape at the pixel level, i.e., stable, erosional, or depositional pixels, was derived from the spectral properties of an archived time series (from 1972 to 1997) of Landsat satellite data of a commercial ranch in northeastern Utah. The SSI was retrospectively validated with contemporary field measures of soil organic matter and erosion status that was surveyed by US federal land management agencies. Catastrophe theory provided the conceptual framework for retrospective assessment of the impact of commercial grazing and soil water availability on the SSI. The overall SSI trend was from an eroding landscape in the early drier 1970s towards stable conditions in the wetter mid-1980s and late 1990s. The landscape catastrophically shifted towards an extreme eroding state that was coincident with the "The Great North American Drought of 1988". Periods of landscape stability and trajectories toward stability were coincident with extremely wet El Niño events. Commercial grazing had less correlation with soil stability than drought conditions. However, the landscape became more susceptible to erosion events under multiple droughts and grazing. Land managers now have nearly a year warning of El Niño and La Niña events and can adjust their management decisions according to predicted landscape erosion conditions.

Global tests of biodiversity concordance and the importance of endemism.

Understanding patterns of biodiversity distribution is essential to conservation strategies, but severe data constraints make surrogate measures necessary. For this reason, many studies have tested the performance of terrestrial vertebrates as surrogates for overall species diversity, but these tests have typically been limited to a single taxon or region. Here we show that global patterns of richness are highly correlated among amphibians, reptiles, birds and mammals, as are endemism patterns. Furthermore, we demonstrate that although the correlation between global richness and endemism is low, aggregate regions selected for high levels of endemism capture significantly more species than expected by chance. Although areas high in endemism have long been targeted for the protection of narrow-ranging species, our findings provide evidence that endemism is also a useful surrogate for the conservation of all terrestrial vertebrates.

Insights into nitrogen and carbon dynamics of ectomycorrhizal and saprotrophic fungi from isotopic evidence.

The successful use of natural abundances of carbon (C) and nitrogen (N) isotopes in the study of ecosystem dynamics suggests that isotopic measurements could yield new insights into the role of fungi in nitrogen and carbon cycling. Sporocarps of mycorrhizal and saprotrophic fungi, vegetation, and soils were collected in young, deciduous-dominated sites and older, coniferous-dominated sites along a successional sequence at Glacier Bay National Park, Alaska. Mycorrhizal fungi had consistently higher δ15N and lower δ13C values than saprotrophic fungi. Foliar δ13C values were always isotopically depleted relative to both fungal types. Foliar δ15N values were usually, but not always, more depleted than those in saprotrophic fungi, and were consistently more depleted than in mycorrhizal fungi. We hypothesize that an apparent isotopic fractionation by mycorrhizal fungi during the transfer of nitrogen to plants may be attributed to enzymatic reactions within the fungi producing isotopically depleted amino acids, which are subsequently passed on to plant symbionts. An increasing difference between soil mineral nitrogen δ15N and foliar δ15N in later succession might therefore be a consequence of greater reliance on mycorrhizal symbionts for nitrogen supply under nitrogen-limited conditions. Carbon signatures of mycorrhizal fungi may be more enriched than those of foliage because the fungi use isotopically enriched photosynthate such as simple sugars, in contrast to the mixture of compounds present in leaves. In addition, some 13C fractionation may occur during transport processes from leaves to roots, and during fungal chitin biosynthesis. Stable isotopes have the potential to help clarify the role of fungi in ecosystem processes.

Interpretation of nitrogen isotope signatures using the NIFTE model.

Nitrogen cycling in forest soils has been intensively studied for many years because nitrogen is often the limiting nutrient for forest growth. Complex interactions between soil, microbes, and plants and the consequent inability to correlate δ15N changes with biologic processes have limited the use of natural abundances of nitrogen isotopes to study nitrogen (N) dynamics. During an investigation of N dynamics along the 250-year-old successional sequence in Glacier Bay, Alaska, United States, we observed several puzzling isotopic patterns, including a consistent decline in δ15N of the late successional dominant Picea at older sites, a lack of agreement between mineral N δ15N and foliar δ15N, and high isotopic signatures for mycorrhizal fungi. In order to understand the mechanisms creating these patterns, we developed a model of N dynamics and N isotopes (Nitrogen Isotope Fluxes in Terrestrial Ecosystems, NIFTE), which simulated the major transformations of the N cycle and predicted isotopic signatures of different plant species and soil pools. Comparisons with field data from five sites along the successional sequence indicated that NIFTE can duplicate observed patterns in δ15N of soil, foliage, and mineral N over time. Different scenarios that could account for the observed isotopic patterns were tested in model simulations. Possible mechanisms included increased isotopic fractionation on mineralization, fractionation during the transfer of nitrogen from mycorrhizal fungi to plants, variable fractionation on uptake by mycorrhizal fungi compared to plants, no fractionation on mycorrhizal transfer, and elimination of mycorrhizal fungi as a pool in the model. The model results suggest that fractionation during mineralization must be small (˜2‰), and that no fractionation occurs during plant or mycorrhizal uptake. A net fractionation during mycorrhizal transfer of nitrogen to vegetation provided the best fit to isotopic data on mineral N, plants, soils, and mycorrhizal fungi. The model and field results indicate that the importance of mycorrhizal fungi to N uptake is probably less under conditions of high N availability. Use of this model should encourage a more rigorous assessment of isotopic signatures in ecosystem studies and provide insights into the biologic transformations which affect those signatures. This should lead to an enhanced understanding of some of the fundamental controls on nitrogen dynamics.

State-of-the-Art of Models of Production-Decomposition Linkages in Conifer and Grassland Ecosystems.

We review the state-of-the-art of models of forests and grasslands that could be used to predict the impact of a future climate change arising from increased atmospheric carbon dioxide concentration. Four levels of resolution are recognized: physiologically based models, population models, ecosystem models, and regional or global models. At the physiological level a number of important processes can be described in great detail, but these models often treat inadequately interactions with nutrient cycles, which operate on longer time scales. Population and ecosystem models can, on the other hand, encapsulate relationships between the plants and the soil system, but at the expense of requiring more ad ho formulations of processes. At the regional and global scale we have so far only steady-state models, which cannot be used to predict transients caused by climate change. However, our conclusion is that, in spite of the gaps in knowledge, there are several models based on dominant processes that are well enough understood for the predictions of those models to be taken seriously.