Conversion from agriculture to grassland builds soil organic matter on decadal timescales.

Soil organic matter (SOM) often increases when agricultural fields are converted to perennial vegetation, yet decadal scale rates and the mechanisms that underlie SOM accumulation are not clear. We measured SOM accumulation and changes in soil properties on a replicated chronosequence of former agricultural fields in the midwestern United States that spanned 40 years after perennial-grassland establishment. Over this time period, soil organic carbon (SOC) in the top 10 cm of soil accumulated at a constant rate of 62.0 g x m(-2) x yr(-1), regardless of whether the vegetation type was dominated by C3 or C4 grasses. At this rate, SOC contents will be equivalent to unplowed native prairie sites within 55-75 years after cultivation ceased. Both labile (short turnover time) and recalcitrant (long turnover time) carbon pools increased linearly for 40 years, with recalcitrant pools increasing more rapidly than expected. This result was consistent across several different methods of measuring labile SOC. A model that investigates the mechanisms of SOM formation suggests that rapid formation of stable carbon resulted from biochemically resistant microbial products and plant material. Former agricultural soils of the Great Plains may function as carbon sinks for less than a century, although much of the carbon stored is stable.     

An adaptive governance approach to disaster-related behavioural health services

This paper explores the provision of disaster-related behavioural and mental health (DBH) services as a problem of institutional collective action in the United States. This study reviews the challenges that providers have in surmounting multi-organizational disconnects, unstable professional legitimacy, ambiguous information, and shifting disaster needs in developing a system for delivering DBH services. Based on the adaptive governance framework, it argues that existing protocols such as the National Incident Management System (NIMS) and Incident Command System (ICS) may be helpful in advancing collective action, but that real progress will depend on a recognition of norms, expectations, and credentials across many spheres-in other words, on the ability of responders to continuously adjust their procedures and administrative boundaries for behavioural health institutions.     

The development and application of remote sensing to monitor sand dune habitats

Sand dunes are complex systems that contain several habitats, often as mosaics or transitions between types. Several of these habitats are afforded protection under European Legislation and in the UK nationally within Special Areas of Conservation (SAC) and Sites of Special Scientific Interest (SSSI). Natural England has a statutory duty to report to Europe on the conservation status and condition of sand dunes; and is required to report to the UK Government on designated sites. To achieve this we have sought ways of capturing, analysing and interpreting data on the extent and location of sand dune habitats. This requires an ability to be able to obtain data over large areas of coastline in an efficient way. Natural England and Environment Agency Geomatics have worked collaboratively for over 16 years, sharing data and ecological knowledge. In 2012 work started to evaluate the use of remote sensing to map UK BAP and Annex I sand dune habitats. A methodology has now been developed and tested to map sand dune habitats. The key objective was to provide an operational tool that will help to map these habitats and understand change on sites around England. This has been achieved through analysis of LIDAR and Compact Airborne Spectrographic Imager (CASI) data using Object Orientated Image Analysis. Quality Control (QC) and accuracy assessments have shown this approach to be successful and 11 sites have been mapped to date. These techniques are providing a new approach to monitoring change in coastal vegetation communities and informing management of protected sites.     

Quantifying sample biases of inland lake sampling programs in relation to lake surface area and land use/cover

We quantified potential biases associated with lakes monitored using non-probability based sampling by six state agencies in the USA (Michigan, Wisconsin, Iowa, Ohio, Maine, and New Hampshire). To identify biases, we compared state-monitored lakes to a census population of lakes derived from the National Hydrography Dataset. We then estimated the probability of lakes being sampled using generalized linear mixed models. Our two research questions were: (1) are there systematic differences in lake area and land use/land cover (LULC) surrounding lakes monitored by state agencies when compared to the entire population of lakes? and (2) after controlling for the effects of lake size, does the probability of sampling vary depending on the surrounding LULC features? We examined the biases associated with surrounding LULC because of the established links between LULC and lake water quality. For all states, we found that larger lakes had a higher probability of being sampled compared to smaller lakes. Significant interactions between lake size and LULC prohibit us from drawing conclusions about the main effects of LULC; however, in general lakes that are most likely to be sampled have either high urban use, high agricultural use, high forest cover, or low wetland cover. Our analyses support the assertion that data derived from non-probability-based surveys must be used with caution when attempting to make generalizations to the entire population of interest, and that probability-based surveys are needed to ensure unbiased, accurate estimates of lake status and trends at regional to national scales.     

Comparing Hydrogeomorphic Approaches to Lake Classification

A classification system is often used to reduce the number of different ecosystem types that governmental agencies are charged with monitoring and managing. We compare the ability of several different hydrogeomorphic (HGM)—based classifications to group lakes for water chemistry/clarity. We ask: (1) Which approach to lake classification is most successful at classifying lakes for similar water chemistry/clarity? (2) Which HGM features are most strongly related to the lake classes? and, (3) Can a single classification successfully classify lakes for all of the water chemistry/clarity variables examined? We use univariate and multivariate classification and regression tree (CART and MvCART) analysis of HGM features to classify alkalinity, water color, Secchi, total nitrogen, total phosphorus, and chlorophyll a from 151 minimally disturbed lakes in Michigan USA. We developed two MvCART models overall and two CART models for each water chemistry/clarity variable, in each case comparing: local HGM characteristics alone and local HGM characteristics combined with regionalizations and landscape position. The combined CART models had the highest strength of evidence (ω_i range 0.92–1.00) and maximized within class homogeneity (ICC range 36–66%) for all water chemistry/clarity variables except water color and chlorophyll a. Because the most successful single classification was on average 20% less successful in classifying other water chemistry/clarity variables, we found that no single classification captures variability for all lake responses tested. Therefore, we suggest that the most successful classification (1) is specific to individual response variables, and (2) incorporates information from multiple spatial scales (regionalization and local HGM variables).     

Who owns the Earth? A challenge for the land system science community

ABSTRACT

Reflecting on the 2019 Open Science Meeting of the Global Land Program and on commentaries since, we argue that the time is ripe for the land system science community to fully embrace the thorny issue of land ownership and control. Beyond land governance and institutions, the issue of who actually owns and controls land, and how land holding and rents are distributed across society, is central to the future of sustainability initiatives, biodiversity protection, social justice, climate change mitigation, and long-term food security and sovereignty. By explicitly tracking and visualizing just Who Owns the Earth, the land system science community could provide much-needed data and insights to inform public debate and advance political action in these arenas.     

Grouping Lakes for Water Quality Assessment and Monitoring: The Roles of Regionalization and Spatial Scale

Regionalization frameworks cluster geographic data to create contiguous regions of similar climate, geology and hydrology by delineating land into discrete regions, such as ecoregions or watersheds, often at several spatial scales. Although most regionalization schemes were not originally designed for aquatic ecosystem classification or management, they are often used for such purposes, with surprisingly few explicit tests of the relative ability of different regionalization frameworks to group lakes for water quality monitoring and assessment. We examined which of 11 different lake grouping schemes at two spatial scales best captures the maximum amount of variation in water quality among regions for total nutrients, water clarity, chlorophyll, overall trophic state, and alkalinity in 479 lakes in Michigan (USA). We conducted analyses on two data sets: one that included all lakes and one that included only minimally disturbed lakes. Using hierarchical linear models that partitioned total variance into within-region and among-region components, we found that ecological drainage units and 8-digit hydrologic units most consistently captured among-region heterogeneity at their respective spatial scales using all lakes (variation among lake groups = 3% to 50% and 12% to 52%, respectively). However, regionalization schemes capture less among-region variance for minimally disturbed lakes. Diagnostics of spatial autocorrelation provided insight into the relative performance of regionalization frameworks but also demonstrated that region size is only partly responsible for capturing variation among lakes. These results suggest that regionalization schemes can provide useful frameworks for lake water quality assessment and monitoring but that we must identify the appropriate spatial scale for the questions being asked, the type of management applied, and the metrics being assessed.