Meha Jain

Functional and phylogenetic diversity as predictors of biodiversity–ecosystem-function relationships

How closely does variability in ecologically important traits reflect evolutionary divergence? The use of phylogenetic diversity (PD) to predict biodiversity effects on ecosystem functioning, and more generally the use of phylogenetic information in community ecology, depends in part on the answer to this question. However, comparisons of the predictive power of phylogenetic diversity and functional diversity (FD) have not been conducted across a range of experiments. To address how phylogenetic diversity and functional trait variation control biodiversity effects on biomass production, we summarized the results of 29 grassland plant experiments where both the phylogeny of plant species used in the experiments is well described and where extensive trait data are available. Functional trait variation was only partially related to phylogenetic distances between species, and the resulting FD values therefore correlate only partially with PD. Despite these differences, FD and PD predicted biodiversity effects across all experiments with similar strength, including in subsets that excluded plots with legumes and that focused on fertilization experiments. Two- and three-trait combinations of the five traits used here (leaf nitrogen percentage, height, specific root length, leaf mass per unit area, and nitrogen fixation) resulted in the FD values with the greatest predictive power. Both PD and FD can be valuable predictors of the effect of biodiversity on ecosystem functioning, which suggests that a focus on both community trait diversity and evolutionary history can improve understanding of the consequences of biodiversity loss.

The importance of rare species: a trait-based assessment of rare species contributions to functional diversity and possible ecosystem function in tall-grass prairies

The majority of species in ecosystems are rare, but the ecosystem consequences of losing rare species are poorly known. To understand how rare species may influence ecosystem functioning, this study quantifies the contribution of species based on their relative level of rarity to community functional diversity using a trait-based approach. Given that rarity can be defined in several different ways, we use four different definitions of rarity: abundance (mean and maximum), geographic range, and habitat specificity. We find that rarer species contribute to functional diversity when rarity is defined by maximum abundance, geographic range, and habitat specificity. However, rarer species are functionally redundant when rarity is defined by mean abundance. Furthermore, when using abundance-weighted analyses, we find that rare species typically contribute significantly less to functional diversity than common species due to their low abundances. These results suggest that rare species have the potential to play an important role in ecosystem functioning, either by offering novel contributions to functional diversity or via functional redundancy depending on how rare species are defined. Yet, these contributions are likely to be greatest if the abundance of rare species increases due to environmental change. We argue that given the paucity of data on rare species, understanding the contribution of rare species to community functional diversity is an important first step to understanding the potential role of rare species in ecosystem functioning.

Biophysical and Socioeconomic Factors Associated with Forest Transitions at Multiple Spatial and Temporal Scales

Forest transitions (FT) occur when socioeconomic development leads to a shift from net deforestation to reforestation; these dynamics have been observed in multiple countries across the globe, including the island of Puerto Rico in the Caribbean. Starting in the 1950s, Puerto Rico transitioned from an agrarian to a manufacturing and service economy reliant on food imports, leading to extensive reforestation. In recent years, however, net reforestation has leveled off. Here we examine the drivers of forest transition in Puerto Rico from 1977 to 2000 at two subnational, nested spatial scales (municipality and barrio) and over two time periods (1977-1991 and 1991-2000). This study builds on previous work by considering the social and biophysical factors that influence both reforestation and deforestation at multiple spatial and temporal scales. By doing so within one analysis, this study offers a comprehensive understanding of the relative importance of various social and biophysical factors for forest transitions and the scales at which they are manifest. Biophysical factors considered in these analyses included slope, soil quality, and land-cover in the surrounding landscape. We also considered per capita income, population density, and the extent of protected areas as potential factors associated with forest change. Our results show that, in the 1977-1991 period, biophysical factors that exhibit variation at municipality scales (~100 km²) were more important predictors of forest change than socioeconomic factors. In this period, forest dynamics were driven primarily by abandonment of less productive, steep agricultural land in the western, central part of the island. These factors had less predictive power at the smaller barrio scale (~10 km²) relative to the larger municipality scale during this time period. The relative importance of socioeconomic variables for deforestation, however, increased over time as development pressures on available land increased. From 1991-2000, changes in forest cover reflected influences from multiple factors, including increasing population densities, land development pressure from suburbanization, and the presence of protected areas. In contrast to the 1977-1991 period, drivers of deforestation and reforestation over this second interval were similar for the two spatial scales of analyses. Generally, our results suggest that although broader socioeconomic changes in a given region may drive the demand for land, biophysical factors ultimately mediate where development occurs. Although economic development may initially result in reforestation due to rural to urban migration and the abandonment of agricultural lands, increased economic development may lead to deforestation through increased suburbanization pressures.

Perceptional and socio-demographic factors associated with household drinking water management strategies in rural Puerto Rico.

Identifying which factors influence household water management can help policy makers target interventions to improve drinking water quality for communities that may not receive adequate water quality at the tap. We assessed which perceptional and socio-demographic factors are associated with household drinking water management strategies in rural Puerto Rico. Specifically, we examined which factors were associated with household decisions to boil or filter tap water before drinking, or to obtain drinking water from multiple sources. We find that households differ in their management strategies depending on the institution that distributes water (i.e. government PRASA vs community-managed non-PRASA), perceptions of institutional efficacy, and perceptions of water quality. Specifically, households in PRASA communities are more likely to boil and filter their tap water due to perceptions of low water quality. Households in non-PRASA communities are more likely to procure water from multiple sources due to perceptions of institutional inefficacy. Based on informal discussions with community members, we suggest that water quality may be improved if PRASA systems improve the taste and odor of tap water, possibly by allowing for dechlorination prior to distribution, and if non-PRASA systems reduce the turbidity of water at the tap, possibly by increasing the degree of chlorination and filtering prior to distribution. Future studies should examine objective water quality standards to identify whether current management strategies are effective at improving water quality prior to consumption.

Winter crop sensitivity to inter-annual climate variability in central India

India is predicted to be one of the most vulnerable agricultural regions to future climate changes. Here, we examined the sensitivity of winter cropping systems to inter-annual climate variability in a local market and subsistence-based agricultural system in central India, a data-rich validation site, in order to identify the climate parameters to which winter crops – mainly wheat and pulses in this region – might be sensitive in the future. We used satellite time-series data to quantify inter-annual variability in multiple climate parameters and in winter crop cover, agricultural census data to quantify irrigation, and field observations to identify locations for specific crop types. We developed three mixed-effect models (250 m to 1 km scale) to identify correlations between crop cover (wheat and pulses) and twenty-two climate and environmental parameters for 2001-2013. We find that winter daytime mean temperature (November–January) is the most significant factor affecting winter crops, irrespective of crop type, and is negatively associated with winter crop cover. With pronounced winter warming projected in the coming decades, effective adaptation by smallholder farmers in similar landscapes would require additional strategies, such as access to fine-scale temperature forecasts and heat-tolerant winter crop varieties.

Mapping Smallholder Wheat Yields and Sowing Dates Using Micro-Satellite Data

Remote sensing offers a low-cost method for developing spatially continuous crop production statistics across large areas and through time. Nevertheless, it has been difficult to characterize the production of individual smallholder farms, given that the land-holding size in most areas of South Asia (<2 ha) is smaller than the spatial resolution of most freely available satellite imagery, like Landsat and MODIS. In addition, existing methods to map yield require field-level data to develop and parameterize predictive algorithms that translate satellite vegetation indices to yield, yet these data are costly or difficult to obtain in many smallholder systems. To overcome these challenges, this study explores two issues. First, we employ new high spatial (2 m) and temporal (bi-weekly) resolution micro-satellite SkySat data to map sowing dates and yields of smallholder wheat fields in Bihar, India in the 2014–2015 and 2015–2016 growing seasons. Second, we compare how well we predict sowing date and yield when using ground data, like crop cuts and self-reports, versus using crop models, which require no on-the-ground data, to develop and parameterize prediction models. Overall, sow dates were predicted well (R2 = 0.41 in 2014–2015 and R2 = 0.62 in 2015–2016), particularly when using models that were parameterized using self-report sow dates collected close to the time of planting and when using imagery that spanned the entire growing season. We were also able to map yields fairly well (R2 = 0.27 in 2014–2015 and R2 = 0.33 in 2015–2016), with crop cut parameterized models resulting in the highest accuracies. While less accurate, we were able to capture the large range in sow dates and yields across farms when using models parameterized with crop model data and these estimates were able to detect known relationships between management factors (e.g., sow date, fertilizer, and irrigation) and yield. While these results are specific to our study site in India, it is likely that the methods employed and the lessons learned are applicable to smallholder systems more generally across the globe. This is of particular interest given that similar high spatio-temporal resolution micro-satellite data will become increasingly available in the coming years.

Top 40 questions in coupled human and natural systems (CHANS) research

Understanding and managing coupled human and natural systems (CHANS) is a central challenge of the 21st century, but more focus is needed to pursue the most important questions within this vast field given limited research capacity and funding. We present 40 important questions for CHANS research, identified through a two-part crowdsourcing exercise within the CHANS community. We solicited members of the International Network of Research on Coupled Human and Natural Systems (CHANS-Net) to submit up to three questions that they considered transformative, receiving 540 questions from 207 respondents. After editing for clarity and consistency, we asked the network’s members to each evaluate a random subset of 20 questions in importance on a scale from 1 (least important) to 7 (extremely important). Questions on land use and agriculture topped the list, with a median importance ranking of 5.7, followed by questions of scale, climate change and energy, sustainability and development, adaptation and resilience, in addition to seven other categories. We identified 40 questions with a median importance of 6.0 or above, which we highlight as the current view of researchers active in the field as research questions to pursue in order to maximize impact on understanding and managing coupled human and natural systems for achieving sustainable development goals and addressing emerging global challenges. (Less)

Feeding Prometheus: An Interdisciplinary Approach for Solving the Global Food Crisis

The current global food system is inadequate to meet the needs of the current world population without compromising future well-being. For example, current intensified production systems lead to undernutrition in some regions coupled with epidemics of obesity in others while compromising their underlying ecological foundations, such as creating areas of ocean hypoxia. Such common observations challenge the research community to ask new types of basic questions and apply novel analytical frameworks for analyzing them. Elaboration of an integrated applied research agenda is imperative to addressing these global food system challenges. We propose that core competencies of a new analytical framework lie at the intersection of four domains: 1) the ecology of agroecosystems; 2) equity in global and local food systems; 3) cultural dimensions of food and agriculture; and 4) human health. This intersection constitutes a new analytical framework for transitions toward global food system sustainability.