University of New Hampshire

About: University of New Hampshire is a education organization based out in Durham, New Hampshire, United States. It is known for research contribution in the topics: Population & Solar wind. The organization has 9379 authors who have published 24025 publications receiving 1020112 citations. The organization is also known as: UNH.

Macrosystems ecology: understanding ecological patterns and processes at continental scales

Abstract: Macrosystems ecology is the study of diverse ecological phenomena at the scale of regions to continents and their interactions with phenomena at other scales. This emerging subdiscipline addresses ecological questions and environmental problems at these broad scales. Here, we describe this new field, show how it relates to modern ecological study, and highlight opportunities that stem from taking a macrosystems perspective. We present a hierarchical framework for investigating macrosystems at any level of ecological organization and in relation to broader and finer scales. Building on well-established theory and concepts from other subdisci- plines of ecology, we identify feedbacks, linkages among distant regions, and interactions that cross scales of space and time as the most likely sources of unexpected and novel behaviors in macrosystems. We present three examples that highlight the importance of this multiscaled systems perspective for understanding the ecology of regions to continents.

A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

Abstract: Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282.

The ecologist's field guide to sequence‐based identification of biodiversity

Abstract: Summary The past 100 years of ecological research has seen substantial progress in understanding the natural world and likely effects of change, whether natural or anthropogenic. Traditional ecological approaches underpin such advances, but would additionally benefit from recent developments in the sequence-based quantification of biodiversity from the fields of molecular ecology and genomics. By building on a long and rich history of molecular taxonomy and taking advantage of the new generation of DNA sequencing technologies, we are gaining previously impossible insights into alpha and beta diversity from all domains of life, irrespective of body size. While a number of complementary reviews are available in specialist journals, our aim here is to succinctly describe the different technologies available within the omics toolbox and showcase the opportunities available to contemporary ecologists to advance our understanding of biodiversity and its potential roles in ecosystems. Starting in the field, we walk the reader through sampling and preservation of genomic material, including typical taxonomy marker genes used for species identification. Moving on to the laboratory, we cover nucleic acid extraction approaches and highlight the principal features of using marker gene assessment, metagenomics, metatranscriptomics, single-cell genomics and targeted genome sequencing as complementary approaches to assess the taxonomic and functional characteristics of biodiversity. We additionally provide clear guidance on the forms of DNA found in the environmental samples (e.g. environmental vs. ancient DNA) and highlight a selection of case studies, including the investigation of trophic relationships/food webs. Given the maturity of sequence-based identification of prokaryotes and microbial eukaryotes, more exposure is given to macrobial communities. We additionally illustrate current approaches to genomic data analysis and highlight the exciting prospects of the publicly available data underpinning published sequence-based studies. Given that ecology ‘has to count’, we identify the impact that molecular genetic analyses have had on stakeholders and end-users and predict future developments for the fields of biomonitoring. Furthermore, we conclude by highlighting future opportunities in the field of systems ecology afforded by effective engagement between the fields of traditional and molecular ecology.