Co-limitation towards lower latitudes shapes global forest diversity gradients.

The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers.

The number of tree species on Earth.

One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness.

The relationship between urban refuse with fecundity and nestlings' success of a generalist seabird in the Río de la Plata Estuary - Uruguay.

Resource acquisition and allocation impacts individual fitness. Using pellet analysis of breeding adults and stable isotopes of carbon and nitrogen of down feathers of Kelp Gull (Larus dominicanus) nestlings, we evaluated the relationship between urban refuse (beef and chicken) and natural food (fish) consumption of individual females during the pre-incubation period, with fecundity and young nesting's success in the Río de la Plata Estuary in Uruguay. Assimilated urban refuse positively correlated with egg weight and negatively with young nestling's success. This suggests a possible impact of urban refuse foraged by females during the pre-incubation period on their immediate fecundity (positively) and young nestling's survival (negatively). Differences between studies at the individual and colony levels are also discussed in light of an "ecological fallacy" of interpretation and we thus argue for the need of additional research to evaluate this relationship further, considering potential confounding factors.

Spatial identification and determinants of trade-offs among multiple land use functions in Jiangsu Province, China.

Understanding the relationships among multiple land use functions (LUFs) is crucial for land-based spatial planning that can guide targeted land use policy-making in complex socio-ecological systems. However, few studies concerned the interactions among various LUFs integrating the issues of economy, environment, and society at a fine scale. In this study, we quantified 12 LUFs using a geospatial model and statistical analysis at the grid scale in Jiangsu Province. Then, we identified the relationships among three primary LUFs-agricultural production function (APF), urban-rural living function (ULF), and ecological maintenance function (EMF)-and further explored the determinants of LUF trade-offs aimed to provide a reference for policy-makers to make decisions in future land use planning and management. The results revealed that the high trade-off areas for APF and ULF are mainly distributed in central and northern Jiangsu, and the trade-offs for both APF-EMF and ULF-EMF were higher in the area covered with water and forest. The determinants of LUF trade-offs mainly refers to land use/land cover, potential evapotranspiration, and vegetation coverage ratio. Moreover, landscape configuration metrics and distance to the nearest county and nearest road also have remarkable impacts on the trade-offs of APF-EMF and ULF-EMF. Finally, we proposed that the concepts of LUF trade-offs should be incorporated into the processes of delineating boundaries for urban growth, farmland, and natural areas. We also propose that land consolidation projects should be implemented in an orderly manner to alleviate LUF trade-offs.

Boreal chorus frog (Pseudacris maculata) spring calling activity, habitat metrics, and associated environmental data.

Data were collected using multimodal monitoring technologies pairing sound recorders with time-lapse camera systems. In the spring of 2015, 2016, and 2017, sound recordings and imagery were collected at a wet meadow and forested slough in the Central Platte River Valley of Nebraska. Boreal chorus frog (Pseudacris maculata) calling activity was obtained from analysing sound recordings. Habitat metrics including vegetation phenology and wet meadow hydropattern were derived from image analysis. This dataset can be used to assess phenology, anuran vocalization activity, and environmental change, as well as to further understanding of wetland ecology. Data are co-submitted with manuscript Brinley Buckley, E. M., Gottesman, B. L., Caven, A. J., Harner, M. J., and Pijanowski, B. C., Assessing ecological and environmental influences on Boreal Chorus Frog (Pseudacris maculata) spring calling phenology using multimodal passive monitoring technologies. Ecological Indicators (In Press).

A SWAT-based optimization tool for obtaining cost-effective strategies for agricultural conservation practice implementation at watershed scales.

To address the harmful algal blooms problem in Lake Erie, one solution is to determine the most cost-effective strategies for implementing agricultural best management practices (BMPs) in the Maumee River watershed. An optimization tool, which combines multi-objective optimization algorithms, SWAT (Soil and Water Assessment Tool), and a computational efficient framework, was created to optimally identify agricultural BMPs at watershed scales. The optimization tool was demonstrated in the Matson Ditch watershed, an agricultural watershed in the Maumee River basin considering critical areas (25% of the watershed with the greatest pollutant loadings per area) and the entire watershed. The initial implementation of BMPs with low expenditures greatly reduced pollutant loadings; beyond certain levels of pollutant reductions, additional expenditures resulted in less significant reductions in pollutant loadings. Compared to optimization for the entire watershed, optimization in critical areas can greatly reduce computational time and obtain similar optimization results for initial reductions in pollutant loadings, which were 10% for Dissolved Reactive Phosphorus (DRP) and 38% for Total Phosphorus (TP); however, for greater reductions in pollutant loadings, critical area optimization was less cost-effective. With the target of simultaneously reducing March-July DRP/TP losses by 40%, the optimized scenario that reduced DRP losses by 40% was found to reduce 51.1% of TP; however, the optimized scenario that reduced TP losses by 40% can only decrease 11.3% of DRP. The optimization tool can help stakeholders identify optimal types, quantities, and spatial locations of BMPs that can maximize reductions in pollutant loadings with the lowest BMP costs.

The impact of anthropogenic food subsidies on a generalist seabird during nestling growth.

Anthropogenic food subsidies, such as refuse, are an important driver of animal population changes and gulls heavily forage on this food source. Foraging on refuse during the rearing period could affect the acquisition of resources with potential demographic consequences. Using conventional diet analysis and stable isotopes of δ13C and δ15N of blood of Kelp Gull (Larus dominicanus) nestlings, we studied the variation of the chick growth in response to foraging on refuse on a reproductive colony in the Rio de la Plata Estuary in Uruguay. Using Bayesian mixing models on isotopic data, we estimated the proportion and variation of natural food and refuse in the diet of nestlings. Then, we modelled the variation between the mean posterior densities of the food sources and their standard deviation with the nestling morphometric measurements of different sizes. We found that refuse was gradually delivered to Kelp Gull nestlings during the chick rearing period. Additionally, variation of refuse incorporated into nestling tissues increased with nestlings' size. We propose that parents use more isotopically unique food sources during the nestling growth thereby increasing isotopic diversity. This study highlights the need to improve the current waste management system, which is being reviewed in Uruguay. We believe that decision makers should consider the results of this study, which show that refuse is directly impacting coastal ecosystems through mechanisms poorly explored by the environmental sciences.

Analysis on urban densification dynamics and future modes in southeastern Wisconsin, USA.

Urban change (urbanization) has dominated land change science for several decades. However, few studies have focused on what many scholars call the urban densification process (i.e., urban intensity expansion) despite its importance to both planning and subsequent impacts to the environment and local economies. This paper documents past urban densification patterns and uses this information to predict future densification trends in southeastern Wisconsin (SEWI) by using a rich dataset from the United States and by adapting the well-known Land Transformation Model (LTM) for this purpose. Urban densification is a significant and progressive process that often accompanies urbanization more generally. The increasing proportion of lower density areas, rather than higher density areas, was the main characteristic of the urban densification in SEWI from 2001 to 2011. We believe that improving urban land use efficiency to maintain rational densification are effective means toward a sustainable urban landscape. Multiple goodness-of-fit metrics demonstrated that the reconfigured LTM performed relatively well to simulate urban densification patterns in 2006 and 2011, enabling us to forecast densification to 2016 and 2021. The predicted future urban densification patterns are likely to be characterized by higher densities continue to increase at the expense of lower densities. We argue that detailed categories of urban density and specific relevant predictor variables are indispensable for densification prediction. Our study provides researchers working in land change science with important insights into urban densification process modeling. The outcome of this model can help planners to identify the current trajectory of urban development, enabling them to take informed action to promote planning objectives, which could benefit sustainable urbanization definitely.

Biological and environmental datasets from the August 2017 total solar eclipse.

The datasets in this article are associated with the research article 'Assessing biological and environmental effects of a total solar eclipse with passive multimodal technologies' (Brinley Buckley et al., 2018). We documented biotic and abiotic changes during a total solar eclipse on 21 August 2017, in south-central Nebraska, USA, with a multimodal suite of tools, including time-lapse camera systems, data loggers, and sound recording devices. Time-lapse images were used to approximate changes in light, data loggers were used to record temperature and humidity, and sound recordings were used to calculate acoustic indices characterizing variation in the soundscape, as well as to manually identify and estimate avian vocalization activity.

Spatial identification and dynamic analysis of land use functions reveals distinct zones of multiple functions in eastern China.

Land use function (LUF) is a valuable concept that allows for more integrated assessments of land system change. Identifying the changes and relationships of multiple LUFs is pertinent to land use planning and management. Selection and quantification of LUF indicators are critical for LUFs assessment. However, past studies have mostly assessed LUFs using socioeconomic data, which are not suitable for spatial variable quantification. In this study, we proposed a new LUFs classification system based on spatial planning goals, and we applied the system to assess 12 LUFs across 63 counties in Jiangsu Province of eastern China based on multi-source data using geospatial modeling tools combined with statistical analysis of socioeconomic data. We also analyzed the change in LUFs between 2000 and 2015, as well as the interactions among multiple functions. Finally, we identified distinct function zones based on the LUFs assessment in 2000 and 2015 using k-means clustering. The result showed that 12 LUFs displayed significant changes and interactions between 2000 and 2015, which can be explained by differing topography and social-ecological characteristics among counties. Additionally, we found four distinct LUF zones that are spatially agglomerated in similar landscapes and characterize specific LUF relationships in each cluster. In the future, local LUFs and their changes over time should be taken into consideration for land use planning and management, which provide a reference for policy-makers to make decisions that better match local development realities.

Agricultural implications of providing soil-based constraints on urban expansion: Land use forecasts to 2050.

Urbanization onto adjacent farmlands directly reduces the agricultural area available to meet the resource needs of a growing society. Soil conservation is a common objective in urban planning, but little focus has been placed on targeting soil value as a metric for conservation. This study assigns commodity and water storage values to the agricultural soils across all of the watersheds in Michigan's Lower Peninsula to evaluate how cities might respond to a soil conservation-based urbanization strategy. Land Transformation Model (LTM) simulations representing both traditional and soil conservation-based urbanization, are used to forecast urban area growth from 2010 to 2050 at five year intervals. The expansion of urban areas onto adjacent farmland is then evaluated to quantify the conservation effects of soil-based development. Results indicate that a soil-based protection strategy significantly conserves total farmland, especially more fertile soils within each soil type. In terms of revenue, ∼$88 million (in current dollars) would be conserved in 2050 using soil-based constraints, with the projected savings from 2011 to 2050 totaling more than $1.5 billion. Soil-based urbanization also increased urban density for each major metropolitan area. For example, there were 94,640 more acres directly adjacent to urban land by 2050 under traditional development compared to the soil-based urbanization strategy, indicating that urban sprawl was more tightly contained when including soil value as a metric to guide development. This study indicates that implementing a soil-based urbanization strategy would better satisfy future agricultural resource needs than traditional urban planning.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges.

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Optimal implementation of green infrastructure practices to minimize influences of land use change and climate change on hydrology and water quality: Case study in Spy Run Creek watershed, Indiana.

Nutrient loading from the Maumee River watershed is a significant reason for the harmful algal blooms (HABs) problem in Lake Erie. The nutrient loading from urban areas needs to be reduced with the installation of green infrastructure (GI) practices. The Long-Term Hydrologic Impact Assessment-Low Impact Development 2.1 (L-THIA-LID 2.1) model was used to explore the influences of land use (LU) and climate change on water quantity and quality in Spy Run Creek watershed (SRCW) (part of Maumee River watershed), decide whether and where excess phosphorus loading existed, identify critical areas to understand where the greatest amount of runoff/pollutants originated, and optimally implement GI practices to obtain maximum environmental benefits with the lowest costs. Both LU/climate changes increased runoff/pollutants generated from the watershed. Areas with the highest runoff/pollutant amount per area, or critical areas, differed for various environmental concerns, land uses (LUs), and climates. Compared to optimization considering all areas, optimization conducted only in critical areas can provide similar cost-effective results with decreased computational time for low levels of runoff/pollutant reductions, but critical area optimization results were not as cost-effective for higher levels of runoff/pollutant reductions. Runoff/pollutants for 2011/2050 LUs/climates could be reduced to amounts of 2001 LU/climate by installation of GI practices with annual expenditures of $0.34 to $2.05 million. The optimization scenarios that were able to obtain the 2001 runoff level in 2011/2050, can also reduce all pollutants to 2001 levels in this watershed.

Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the Artificial Bee Colony Optimization Algorithm.

Cellular Automata (CA) is one of the most common techniques used to simulate the urbanization process. CA-based urban models use transition rules to deliver spatial patterns of urban growth and urban dynamics over time. Determining the optimum transition rules of the CA is a critical step because of the heterogeneity and nonlinearities existing among urban growth driving forces. Recently, new CA models integrated with optimization methods based on swarm intelligence algorithms were proposed to overcome this drawback. The Artificial Bee Colony (ABC) algorithm is an advanced meta-heuristic swarm intelligence-based algorithm. Here, we propose a novel CA-based urban change model that uses the ABC algorithm to extract optimum transition rules. We applied the proposed ABC-CA model to simulate future urban growth in Urmia (Iran) with multi-temporal Landsat images from 1997, 2006 and 2015. Validation of the simulation results was made through statistical methods such as overall accuracy, the figure of merit and total operating characteristics (TOC). Additionally, we calibrated the CA model by ant colony optimization (ACO) to assess the performance of our proposed model versus similar swarm intelligence algorithm methods. We showed that the overall accuracy and the figure of merit of the ABC-CA model are 90.1% and 51.7%, which are 2.9% and 8.8% higher than those of the ACO-CA model, respectively. Moreover, the allocation disagreement of the simulation results for the ABC-CA model is 9.9%, which is 2.9% less than that of the ACO-CA model. Finally, the ABC-CA model also outperforms the ACO-CA model with fewer quantity and allocation errors and slightly more hits.

Optimal selection and placement of green infrastructure to reduce impacts of land use change and climate change on hydrology and water quality: An application to the Trail Creek Watershed, Indiana.

The adverse impacts of urbanization and climate change on hydrology and water quality can be mitigated by applying green infrastructure practices. In this study, the impacts of land use change and climate change on hydrology and water quality in the 153.2 km(2) Trail Creek watershed located in northwest Indiana were estimated using the Long-Term Hydrologic Impact Assessment-Low Impact Development 2.1 (L-THIA-LID 2.1) model for the following environmental concerns: runoff volume, Total Suspended Solids (TSS), Total Phosphorous (TP), Total Kjeldahl Nitrogen (TKN), and Nitrate+Nitrite (NOx). Using a recent 2001 land use map and 2050 land use forecasts, we found that land use change resulted in increased runoff volume and pollutant loads (8.0% to 17.9% increase). Climate change reduced runoff and nonpoint source pollutant loads (5.6% to 10.2% reduction). The 2050 forecasted land use with current rainfall resulted in the largest runoff volume and pollutant loads. The optimal selection and placement of green infrastructure practices using L-THIA-LID 2.1 model were conducted. Costs of applying green infrastructure were estimated using the L-THIA-LID 2.1 model considering construction, maintenance, and opportunity costs. To attain the same runoff volume and pollutant loads as in 2001 land uses for 2050 land uses, the runoff volume, TSS, TP, TKN, and NOx for 2050 needed to be reduced by 10.8%, 14.4%, 13.1%, 15.2%, and 9.0%, respectively. The corresponding annual costs of implementing green infrastructure to achieve the goals were $2.1, $0.8, $1.6, $1.9, and $0.8 million, respectively. Annual costs of reducing 2050 runoff volume/pollutant loads were estimated, and results show green infrastructure annual cost greatly increased for larger reductions in runoff volume and pollutant loads. During optimization, the most cost-efficient green infrastructure practices were selected and implementation levels increased for greater reductions of runoff and nonpoint source pollutants.

Terrestrial Soundscapes: Status of Ecological Research in Natural and Human-Dominated Landscapes.

Soundscape ecological research in terrestrial systems is relatively new. In this paper, I present a brief summary of the origins of this research area, describe research questions related to several research thrusts that are ongoing, summarize several soundscape projects that exist and how these relate to the research thrusts, and briefly describe the work of a global network of scientists, musicians, and engineers that are attempting to move this new field forward.

Characterizing long-term land use/cover change in the United States from 1850 to 2000 using a nonlinear bi-analytical model.

We relate the historical (1850-2000) spatial and temporal changes in cropland cover in the conterminous United States to several socio-economic and biophysical determinants using an eco-region based spatial framework. Results show population density as a major determinant during the nineteenth century, and biophysical suitability as the major determinant during the twentieth century. We further examine the role of technological innovations, socio-economic and socio-ecological feedbacks that have either sustained or altered the cropland trajectories in different eco-regions. The cropland trajectories for each of the 84 level-III eco-regions were analyzed using a nonlinear bi-analytical model. In the Eastern United States, low biophysically suitable eco-regions, e.g., New England, have shown continual decline in the cropland after reaching peak levels. The cropland trajectories in high biophysically suitable regions, e.g., Corn Belt, have stabilized after reaching peak levels. In the Western United States, low-intensity crop cover (<10 %) is sustained with irrigation support. A slower rate of land conversion was found in the industrial period. Significant effect of Conservation Reserve Program on planted crop area is found in last two decades (1990-2010).

The impact of future land use scenarios on runoff volumes in the Muskegon River Watershed.

In this article we compared the response of surface water runoff to a storm event for different rates of urbanization, reforestation and riparian buffer setbacks across forty subwatersheds of the Muskegon River Watershed located in Michigan, USA. We also made these comparisons for several forecasted and one historical land use scenarios (over 140 years). Future land use scenarios to 2040 for forest regrowth, urbanization rates and stream setbacks were developed using the Land Transformation Model (LTM). Historical land use information, from 1900 at 5-year time step intervals, was created using a Backcast land use change model configured using artificial neural network and driven by agriculture and housing census information. We show that (1) controlling the rate of development is the most effective policy option to reduce runoff; (2) establishing setbacks along the mainstem are not as effective as controlling urban growth; (3) reforestation can abate some of the runoff effects from urban growth but not all; (4) land use patterns of the 1970s produced the least amount of runoff in most cases in the Muskegon River Watershed when compared to land use maps from 1900 to 2040; and, (5) future land use patterns here not always lead to increased (worse) runoff than the past. We found that while ten of the subwatersheds contained futures that were worse than any past land use configuration, twenty-five (62.5%) of the subwatersheds produced the greatest amount of runoff in 1900, shortly after the entire watershed was clear-cut. One third (14/40) of the subwatersheds contained the minimum amount of runoff in the 1960s and 1970s, a period when forest amounts were greatest and urban amounts relatively small.

Identifying relationships between baseflow geochemistry and land use with synoptic sampling and R-mode factor analysis.

The relationship between land use and stream chemistry is often explored through synoptic sampling of rivers at baseflow conditions. However, baseflow chemistry is likely to vary temporally and spatially with land use. The purpose of our study is to examine the usefulness of the synoptic sampling approach for identifying the relationship between complex land use configurations and stream water quality. This study compares biogeochemical data from three synoptic sampling events representing the temporal variability of baseflow chemistry and land use using R-mode factor analysis. Separate R-mode factor analyses of the data from individual sampling events yielded only two consistent factors. Agricultural activity was associated with elevated levels of Ca2+, Mg2+, alkalinity, and frequently K+, SO4(2-), and NO3-. Urban areas were associated with higher concentrations of Na+, K+, and Cl-. Other retained factors were not consistent among sampling events, and some factors were difficult to interpret in the context of biogeochemical sources and processes. When all data were combined, further associations were revealed such as an inverse relationship between the proportion of wetlands and stream nitrate concentrations. We also found that barren lands were associated with elevated sulfate levels. This research suggests that an individual sampling event is unlikely to characterize adequately the complex processes controlling interactions between land use and stream chemistry. Combining data collected over two years during three synoptic sampling events appears to enhance our ability to understand processes linking stream chemistry and land use.