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1. Assessing Model Predictions of Carbon Dynamics in Global Drylands

   https://doi.org/10.3389/fenvs.2022.790200  

   Fawcett, Dominic ; Cunliffe, Andrew M. ; Sitch, Stephen ; O’Sullivan, Michael ; Anderson, Karen ; Brazier, Richard E. ; Hill, Timothy C. ; Anthoni, Peter ; Arneth, Almut ; Arora, Vivek K. ; et al ( April 2022 , Frontiers in Environmental Science)

   Drylands cover ca. 40% of the land surface and are hypothesised to play a major role in the global carbon cycle, controlling both long-term trends and interannual variation. These insights originate from land surface models (LSMs) that have not been extensively calibrated and evaluated for water-limited ecosystems. We need to learn more about dryland carbon dynamics, particularly as the transitory response and rapid turnover rates of semi-arid systems may limit their function as a carbon sink over multi-decadal scales. We quantified aboveground biomass carbon (AGC; inferred from SMOS L-band vegetation optical depth) and gross primary productivity (GPP; from PML-v2 inferred from MODIS observations) and tested their spatial and temporal correspondence with estimates from the TRENDY ensemble of LSMs. We found strong correspondence in GPP between LSMs and PML-v2 both in spatial patterns (Pearson’s r = 0.9 for TRENDY-mean) and in inter-annual variability, but not in trends. Conversely, for AGC we found lesser correspondence in space (Pearson’s r = 0.75 for TRENDY-mean, strong biases for individual models) and in the magnitude of inter-annual variability compared to satellite retrievals. These disagreements likely arise from limited representation of ecosystem responses to plant water availability, fire, and photodegradation that drive dryland carbon dynamics. Wemore »assessed inter-model agreement and drivers of long-term change in carbon stocks over centennial timescales. This analysis suggested that the simulated trend of increasing carbon stocks in drylands is in soils and primarily driven by increased productivity due to CO 2 enrichment. However, there is limited empirical evidence of this 50-year sink in dryland soils. Our findings highlight important uncertainties in simulations of dryland ecosystems by current LSMs, suggesting a need for continued model refinements and for greater caution when interpreting LSM estimates with regards to current and future carbon dynamics in drylands and by extension the global carbon cycle.« less
2. The emergent interactions that govern biodiversity change

   https://doi.org/10.1073/pnas.2003852117  

   Clark, James S. ; Scher, C. Lane ; Swift, Margaret ( July 2020 , Proceedings of the National Academy of Sciences)

   Observational studies have not yet shown that environmental variables can explain pervasive nonlinear patterns of species abundance, because those patterns could result from (indirect) interactions with other species (e.g., competition), and models only estimate direct responses. The experiments that could extract these indirect effects at regional to continental scales are not feasible. Here, a biophysical approach quantifies environment– species interactions (ESI) that govern community change from field data. Just as species interactions depend on population abundances, so too do the effects of environment, as when drought is amplified by competition. By embedding dynamic ESI within framework that admits data gathered on different scales, we quantify responses that are induced indirectly through other species, including probabilistic uncertainty in parameters, model specification, and data. Simulation demonstrates that ESI are needed for accurate interpretation. Analysis demonstrates how nonlinear responses arise even when their direct responses to environment are linear. Applications to experimental lakes and the Breeding Bird Survey (BBS) yield contrasting estimates of ESI. In closed lakes, interactions involving phytoplankton and their zooplankton grazers play a large role. By contrast, ESI are weak in BBS, as expected where year-to-year movement degrades the link between local population growth and species interactions. In both cases,more »nonlinear responses to environmental gradients are induced by interactions between species. Stability analysis indicates stability in the closed-system lakes and instability in BBS. The probabilistic framework has direct application to conservation planning that must weigh risk assessments for entire habitats and communities against competing interests.

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3. Dynamics of data availability in disease modeling: An example evaluating the trade-offs of ultra-fine-scale factors applied to human West Nile virus disease models in the Chicago area, USA

   https://doi.org/10.1371/journal.pone.0251517  

   Uelmen, J. A. ; Irwin, P. ; Brown, W. M. ; Karki, S. ; Ruiz, M. O. ; Li, B. ; Smith, R. L. ( May 2021 , PLOS ONE)

   Wen, Feng (Ed.)

   Background Since 1999, West Nile virus (WNV) has moved rapidly across the United States, resulting in tens of thousands of human cases. Both the number of human cases and the minimum infection rate (MIR) in vector mosquitoes vary across time and space and are driven by numerous abiotic and biotic forces, ranging from differences in microclimates to socio-demographic factors. Because the interactions among these multiple factors affect the locally variable risk of WNV illness, it has been especially difficult to model human disease risk across varying spatial and temporal scales. Cook and DuPage Counties, comprising the city of Chicago and surrounding suburbs, experience some of the highest numbers of human neuroinvasive cases of WNV in the United States. Despite active mosquito control efforts, there is consistent annual WNV presence, resulting in more than 285 confirmed WNV human cases and 20 deaths from the years 2014–2018 in Cook County alone. Methods A previous Chicago-area WNV model identified the fifty-five most high and low risk locations in the Northwest Mosquito Abatement District (NWMAD), an enclave ¼ the size of the combined Cook and DuPage county area. In these locations, human WNV risk was stratified by model performance, as indicated by differences inmore »studentized residuals. Within these areas, an additional two-years of field collections and data processing was added to a 12-year WNV dataset that includes human cases, MIR, vector abundance, and land-use, historical climate, and socio-economic and demographic variables, and was assessed by an ultra-fine-scale (1 km spatial x 1 week temporal resolution) multivariate logistic regression model. Results Multivariate statistical methods applied to the ultra-fine-scale model identified fewer explanatory variables while improving upon the fit of the previous model. Beyond MIR and climatic factors, efforts to acquire additional covariates only slightly improved model predictive performance. Conclusions These results suggest human WNV illness in the Chicago area may be associated with fewer, but increasingly critical, key variables at finer scales. Given limited resources, these findings suggest large variations in model performance occur, depending on covariate availability, and provide guidance in variable selection for optimal WNV human illness modeling.« less
4. Site Characteristics Mediate the Relationship Between Forest Productivity and Satellite Measured Solar Induced Fluorescence

   https://doi.org/10.3389/ffgc.2021.695269  

   Yazbeck, Theresia ; Bohrer, Gil ; Gentine, Pierre ; Ye, Luping ; Arriga, Nicola ; Bernhofer, Christian ; Blanken, Peter D. ; Desai, Ankur R. ; Durden, David ; Knohl, Alexander ; et al ( December 2021 , Frontiers in Forests and Global Change)

   Solar-Induced Chlorophyll Fluorescence (SIF) can provide key information about the state of photosynthesis and offers the prospect of defining remote sensing-based estimation of Gross Primary Production (GPP). There is strong theoretical support for the link between SIF and GPP and this relationship has been empirically demonstrated using ground-based, airborne, and satellite-based SIF observations, as well as modeling. However, most evaluations have been based on monthly and annual scales, yet the GPP:SIF relations can be strongly influenced by both vegetation structure and physiology. At the monthly timescales, the structural response often dominates but short-term physiological variations can strongly impact the GPP:SIF relations. Here, we test how well SIF can predict the inter-daily variation of GPP during the growing season and under stress conditions, while taking into account the local effect of sites and abiotic conditions. We compare the accuracy of GPP predictions from SIF at different timescales (half-hourly, daily, and weekly), while evaluating effect of adding environmental variables to the relationship. We utilize observations for years 2018–2019 at 31 mid-latitudes, forested, eddy covariance (EC) flux sites in North America and Europe and use TROPOMI satellite data for SIF. Our results show that SIF is a good predictor of GPP, when accountingmore »for inter-site variation, probably due to differences in canopy structure. Seasonally averaged leaf area index, fraction of absorbed photosynthetically active radiation (fPAR) and canopy conductance provide a predictor to the site-level effect. We show that fPAR is the main factor driving errors in the linear model at high temporal resolution. Adding water stress indicators, namely canopy conductance, to a multi-linear SIF-based GPP model provides the best improvement in the model precision at the three considered timescales, showing the importance of accounting for water stress in GPP predictions, independent of the SIF signal. SIF is a promising predictor for GPP among other remote sensing variables, but more focus should be placed on including canopy structure, and water stress effects in the relationship, especially when considering intra-seasonal, and inter- and intra-daily resolutions.« less
5. CHANGES IN SPRING ARRIVAL DATES OF RUFOUS HUMMINGBIRDS (SELASPHORUS RUFUS) IN WESTERN NORTH AMERICA IN THE PAST CENTURY

   Courter, Jason R. ( March 2017 , Wilson Ornithological Society Annual Meeting)

   Warming temperatures have been linked to advancing spring migration dates of birds, although most studies have been conducted at individual sites. Problems may arise if birds arrive or depart before or after associated food resources reach critical lifecycle stages. I compared mean first arrival dates of Rufous Hummingbird (Selaphorus rufus), a prolific pollinator and long-distance migrant, between 1895-1969 and 2006-2015 at eight locations in Oregon, Washington, and British Columbia. Historical arrivals were reported through the North American Bird Phenology Program and recent arrivals were estimated from temporal occupancy patterns using eBird checklists. Results indicated that hummingbirds arrived 8 and 11 days later in the recent time period in two coastal cities in Oregon and 7-17 days earlier in northern, more inland cities in Washington and British Columbia. Spring temperatures have increased in the past century in much of this region and birds arrived earlier in years with warmer spring temperatures, suggesting that migratory advancements were climate-related. Later arrivals reported in coastal regions of Oregon in the recent time period may suggest that Rufous Hummingbirds are bypassing coastal areas to take advantage of more predictable conditions along inland migratory routes, or are shifting their breeding ranges northward, notions both supported bymore »declining population trends observed in Breeding Bird Survey data. My results provide justification for the investigation of the ecological impacts of climate change on birds in coastal vs. inland environments and a framework for comparing information from two extensive and emerging datasets to better understand the impacts of climate change on bird migration.« less