Adam Terando

The southern megalopolis: using the past to predict the future of urban sprawl in the Southeast U.S.

The future health of ecosystems is arguably as dependent on urban sprawl as it is on human-caused climatic warming. Urban sprawl strongly impacts the urban ecosystems it creates and the natural and agro-ecosystems that it displaces and fragments. Here, we project urban sprawl changes for the next 50 years for the fast-growing Southeast U.S. Previous studies have focused on modeling population density, but the urban extent is arguably as important as population density per se in terms of its ecological and conservation impacts. We develop simulations using the SLEUTH urban growth model that complement population-driven models but focus on spatial pattern and extent. To better capture the reach of low-density suburban development, we extend the capabilities of SLEUTH by incorporating street-network information. Our simulations point to a future in which the extent of urbanization in the Southeast is projected to increase by 101% to 192%. Our results highlight areas where ecosystem fragmentation is likely, and serve as a benchmark to explore the challenging tradeoffs between ecosystem health, economic growth and cultural desires.

Do cities simulate climate change? A comparison of herbivore response to urban and global warming

Cities experience elevated temperature, CO2 , and nitrogen deposition decades ahead of the global average, such that biological response to urbanization may predict response to future climate change. This hypothesis remains untested due to a lack of complementary urban and long-term observations. Here, we examine the response of an herbivore, the scale insect Melanaspis tenebricosa, to temperature in the context of an urban heat island, a series of historical temperature fluctuations, and recent climate warming. We survey M. tenebricosa on 55 urban street trees in Raleigh, NC, 342 herbarium specimens collected in the rural southeastern United States from 1895 to 2011, and at 20 rural forest sites represented by both modern (2013) and historical samples. We relate scale insect abundance to August temperatures and find that M. tenebricosa is most common in the hottest parts of the city, on historical specimens collected during warm time periods, and in present-day rural forests compared to the same sites when they were cooler. Scale insects reached their highest densities in the city, but abundance peaked at similar temperatures in urban and historical datasets and tracked temperature on a decadal scale. Although urban habitats are highly modified, species response to a key abiotic factor, temperature, was consistent across urban and rural-forest ecosystems. Cities may be an appropriate but underused system for developing and testing hypotheses about biological effects of climate change. Future work should test the applicability of this model to other groups of organisms.

Modeling Path Dependence in Agricultural Adaptation to Climate Variability and Change

Path dependence of farmers’ technical choices for managing climate risk combined with farmers’ difficulties in discerning climate change from natural variability might hamper adaptation to climate change. We examine the effects of climate variability and change on corn yields in the Southeast United States using a regional climate model nested within a global climate model (GCM) simulation of the equilibrium atmospheric CO2 concentration of 540 ppm. In addition to a climate scenario with normal variance, we modify the GCM outputs to simulate a scenario with a highly variable climate. We find that climate variability poses a serious challenge to the abilities of farmers and their supporting institutions to adapt. Consistently lower corn yields, especially in the scenario with a highly variable climate, illustrate that farmers’ abilities to make informed choices about their cropping decisions can be constrained by their inabilities to exit from their current technological regimes or path dependence. We also incorporate farmers’ responses to climate change using three adaptation scenarios: no adaptation, “perfect knowledge,” and a scenario that mimics diffusion of knowledge across the landscape. Regardless of adaptation scenario and variance structure, the most common result is a decline in corn production to the point where yield reductions of 1 percent to 20 percent occur across 60 percent to 80 percent of the region. The advantage of the perfect knowledge adaptation scenario declines through time compared to the diffusion-process adaptation scenario. We posit that the cost of path dependence to farmers, in the form of yield reductions, is likely unavoidable because the inherent variability of the climate system will result in adaptation choices that will be suboptimal for some years.

Ecological forecasting under climatic data uncertainty: a case study in phenological modeling

Forecasting ecological responses to climate change represents a challenge to the ecological community because models are often site-specific and climate data are lacking at appropriate spatial and temporal resolutions. We use a case study approach to demonstrate uncertainties in ecological predictions related to the driving climatic input data. We use observational records, derived observational datasets (e.g. interpolated observations from local weather stations and gridded data products) and output from general circulation models (GCM) in conjunction with site based phenology models to estimate the first flowering date (FFD) for three woody flowering species. Using derived observations over the modern time period, we find that cold biases and temperature trends lead to biased FFD simulations for all three species. Observational datasets resolved at the daily time step result in better FFD predictions compared to simulations using monthly resolution. Simulations using output from an ensemble of GCM and regional climate models over modern and future time periods have large intra-ensemble spreads and tend to underestimate observed FFD trends for the modern period. These results indicate that certain forcing datasets may be missing key features needed to generate accurate hindcasts at the local scale (e.g. trends, temporal resolution), and that standard modeling techniques (e.g. downscaling, ensemble mean, etc) may not necessarily improve the prediction of the ecological response. Studies attempting to simulate local ecological processes under modern and future climate forcing therefore need to quantify and propagate the climate data uncertainties in their simulations.

Climate Change Implications for Tropical Islands: Interpolating and Interpreting Statistically Downscaled GCM Projections for Management and Planning*

The potential ecological and economic effects of climate change for tropical islands were studied using output from 12 statistically downscaled general circulation models (GCMs) taking Puerto Rico as a test case. Two model selection/model averaging strategies were used: the average of all available GCMs and the average of the models that are able to reproduce the observed large-scale dynamics that control precipitation over the Caribbean. Five island-wide and multidecadal averages of daily precipitation and temperature were estimated by way of a climatology-informed interpolation of the site-specific downscaled climate model output. Annual cooling degree-days (CDD) were calculated as a proxy index for air-conditioning energy demand, and two measures of annual no-rainfall days were used as drought indices. Holdridge life zone classification was used to map the possible ecological effects of climate change. Precipitation is predicted to declineinbothmodelensembles,butthedecreasewasmoresevereinthe‘‘regionallyconsistent’’models.The precipitation declines cause gradual and linear increases in drought intensity and extremes. The warming from the 1960‐90 period to the 2071‐99 period was 4.68‐98C depending on the global emission scenarios and location.ThiswarmingmaycauseincreasesinCDD,andconsequentlyincreasingenergydemands.Lifezones may shift from wetter to drier zones with the possibility of losing most, if not all, of the subtropical rain forests and extinction risks to rain forest specialists or obligates.

Observed and Modeled Twentieth-Century Spatial and Temporal Patterns of Selected Agro-Climate Indices in North America

AbstractThe authors examine recent changes in three agro-climate indices (frost days, thermal time, and heat stress index) in North America (centered around the continental United States) using observations from a historical climate network and an ensemble of 17 global climate models (GCMs) from the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). Agro-climate indices provide the basis for analyzing agricultural time series that are unbiased by long-term technological intervention. Observations from the last 60 years (1951–2010) confirm conclusions of previous studies showing continuing declines in the number of frost days and increases in thermal time. Increases in heat stress are largely confined to the western half of the continent. The authors do not observe accelerating agro-climate warming trends in the most recent decade of observations. The spatial variability of the temporal trends in GCMs is lower compared to the observed patterns, which still show some regio...

Modeling climate change, urbanization, and fire effects on Pinus palustris ecosystems of the southeastern U.S.

Managing ecosystems for resilience and sustainability requires understanding how they will respond to future anthropogenic drivers such as climate change and urbanization. In fire-dependent ecosystems, predicting this response requires a focus on how these drivers will impact fire regimes. Here, we use scenarios of climate change, urbanization and management to simulate the future dynamics of the critically endangered and fire-dependent longleaf pine (Pinus palustris) ecosystem. We investigated how climate change and urbanization will affect the ecosystem, and whether the two conservation goals of a 135% increase in total longleaf area and a doubling of fire-maintained open-canopy habitat can be achieved in the face of these drivers. Our results show that while climatic warming had little effect on the wildfire regime, and thus on longleaf pine dynamics, urban growth led to an 8% reduction in annual wildfire area. The management scenarios we tested increase the ecosystems total extent by up to 62% and result in expansion of open-canopy longleaf by as much as 216%, meeting one of the two conservation goals for the ecosystem. We find that both conservation goals for this ecosystem, which is climate-resilient but vulnerable to urbanization, are only attainable if a greater focus is placed on restoration of non-longleaf areas as opposed to maintaining existing longleaf stands. Our approach demonstrates the importance of accounting for multiple relevant anthropogenic threats in an ecosystem-specific context in order to facilitate more effective management actions.

The Sensitivity of WRF Downscaled Precipitation in Puerto Rico to Cumulus Parameterization and Interior Grid Nudging

AbstractThe sensitivity of the precipitation over Puerto Rico that is simulated by the Weather Research and Forecasting (WRF) Model is evaluated using multiple combinations of cumulus parameterization (CP) schemes and interior grid nudging. The NCEP–DOE AMIP-II reanalysis (R-2) is downscaled to 2-km horizontal grid spacing both with convective-permitting simulations (CP active only in the middle and outer domains) and with CP schemes active in all domains. The results generally show lower simulated precipitation amounts than are observed, regardless of WRF configuration, but activating the CP schemes in the inner domain improves the annual cycle, intensity, and placement of rainfall relative to the convective-permitting simulations. Furthermore, the use of interior-grid-nudging techniques in the outer domains improves the placement and intensity of rainfall in the inner domain. Incorporating a CP scheme at convective-permitting scales ( 4 km) im...

Ad hoc instrumentation methods in ecological studies produce highly biased temperature measurements

Abstract In light of global climate change, ecological studies increasingly address effects of temperature on organisms and ecosystems. To measure air temperature at biologically relevant scales in the field, ecologists often use small, portable temperature sensors. Sensors must be shielded from solar radiation to provide accurate temperature measurements, but our review of 18 years of ecological literature indicates that shielding practices vary across studies (when reported at all), and that ecologists often invent and construct ad hoc radiation shields without testing their efficacy. We performed two field experiments to examine the accuracy of temperature observations from three commonly used portable data loggers (HOBO Pro, HOBO Pendant, and iButton hygrochron) housed in manufactured Gill shields or ad hoc, custom‐fabricated shields constructed from everyday materials such as plastic cups. We installed this sensor array (five replicates of 11 sensor‐shield combinations) at weather stations located in open and forested sites. HOBO Pro sensors with Gill shields were the most accurate devices, with a mean absolute error of 0.2°C relative to weather stations at each site. Error in ad hoc shield treatments ranged from 0.8 to 3.0°C, with the largest errors at the open site. We then deployed one replicate of each sensor‐shield combination at five sites that varied in the amount of urban impervious surface cover, which presents a further shielding challenge. Bias in sensors paired with ad hoc shields increased by up to 0.7°C for every 10% increase in impervious surface. Our results indicate that, due to variable shielding practices, the ecological literature likely includes highly biased temperature data that cannot be compared directly across studies. If left unaddressed, these errors will hinder efforts to predict biological responses to climate change. We call for greater standardization in how temperature data are recorded in the field, handled in analyses, and reported in publications.

Occupancy and Abundance of Eleutherodactylus Frogs in Coffee Plantations in Puerto Rico

Abstract Shaded coffee plantations are of conservation value for many taxa, particularly for resident avifauna in the face of extensive landscape changes. Yet, little is known about the value of coffee plantations for amphibians because there are scant demographic data to index their value among species with different habitat preferences. We estimated the probability of occupancy of three frog species: Eleutherodactylus wightmanae, a forest species; E. brittoni, a grassland species; and E. antillensis, an open habitat species. Occupancy was estimated in sun and shaded plantations, and in secondary forest, in the west-central mountains of Puerto Rico. We also estimated the probability that a survey station was occupied by no individuals, one, or >1 individual, as a proxy of abundance. The aforementioned parameters, and local colonization and extinction probability, were modeled as a function of weather conditions (temperature, humidity) and vegetation cover at the sampling station (5 m) and contextual (100 m) scales. Encounter histories were obtained with passive acoustic recorders between February and July in 2015. Consistent with known habitat preferences, the highest occupancies were associated with secondary forests for E. wightmanae and sun plantations for E. brittoni. Occupancy probability for E. antillensis was similar across habitat types, indicating no aversion to shaded–forested habitats. Shaded plantations harbored moderate levels of occupancy for all species, indicating their potential value for multispecies conservation. Local colonization rates increased with forest cover for E. wightmanae, and with open habitats for E. brittoni and E. antillensis. Open habitats harbored a higher abundance of E. brittoni and E antillensis, but lower values for E. wightmanae. Sun and shaded plantations could provide quality habitat for Eleutherodactylus spp. if managed for features that promote local colonization and abundance.