Greenhouse gas mitigation potential in smallholder agroecosystem of southern Ethiopia

In developing countries, it is critical that novel and swift strategies are devised to help direct and prioritize potential greenhouse gas (GHG) mitigation activities. The Carbon Benefit Project (CBP) analysis tool is a modular, web-based system that allows a consistent comparison of various projects by providing a standardized GHG benefits protocol. In this study, we used the CBP tool to estimate the GHG mitigation potential of the agriculture, forestry, and other land uses (AFOLU) sector and prioritize components for their GHG benefits in three districts of Wolaita Zone, southern Ethiopia. The study area is 90,731 ha of which about 2% was covered by forest, 7% by grassland, 78% by annual crops, 12% by home garden and 1% by settlements. The livestock population in the study area was 512,622 heads. Using the CBP's Detailed Assessment, we estimated mitigation potential in the AFOLU consisting of different managements strategies for a period between 2016 and 2030 in the smallholder agricultural landscape. The results showed an overall GHG benefit of 1,725,052 (±5%) Mg CO₂e from the projected scenario in the study area. The GHG benefit was in the order of biomass C (683,757 Mg CO₂e) > soil C (619,210 Mg CO₂e) > livestock (408,981 Mg CO₂e) illustrating the greater mitigation potential of trees in different systems. The soil C plus biomass C was high in agroforestry systems, and this component had the highest priority for GHG mitigation. This was followed by high enteric methane emission reduction in the livestock category. The GHG emission from manure increased by 71,633 Mg CO₂e in the project because manure was not managed. The surprisingly low GHG benefit of the forest was primarily because of its low land cover (i.e., about 2%) in the agroecosystem. Despite the low GHG benefit in the cropland from best management practices, the improved soil quality in it can affect GHG benefits from other land uses by contributing to their conservation through food security. Thus, a comprehensive project may be a viable strategy in a mitigation effort at the agroecosystem level because of the interactions amongst the components. The CBP analysis tool is useful in prioritizing mitigation activities and may be an option to quantify GHG benefits if studies collate Teir 2 factors in data scarce areas.

INHABIT: A web-based decision support tool for invasive plant species habitat visualization and assessment across the contiguous United States

Narrowing the communication and knowledge gap between producers and users of scientific data is a longstanding problem in ecological conservation and land management. Decision support tools (DSTs), including websites or interactive web applications, provide platforms that can help bridge this gap. DSTs can most effectively disseminate and translate research results when producers and users collaboratively and iteratively design content and features. One data resource seldom incorporated into DSTs are species distribution models (SDMs), which can produce spatial predictions of habitat suitability. Outputs from SDMs can inform management decisions, but their complexity and inaccessibility can limit their use by resource managers or policy makers. To overcome these limitations, we present the Invasive Species Habitat Tool (INHABIT), a novel, web-based DST built with R Shiny to display spatial predictions and tabular summaries of habitat suitability from SDMs for invasive plants across the contiguous United States. INHABIT provides actionable science to support the prevention and management of invasive species. Two case studies demonstrate the important role of end user feedback in confirming INHABIT's credibility, utility, and relevance.

Grassification and Fast-Evolving Fire Connectivity and Risk in the Sonoran Desert, United States

In the southwestern United States, non-native grass invasions have increased wildfire occurrence in deserts and the likelihood of fire spread to and from other biomes with disparate fire regimes. The elevational transition between desertscrub and montane grasslands, woodlands, and forests generally occurs at ∼1,200 masl and has experienced fast suburbanization and an expanding wildland-urban interface (WUI). In summer 2020, the Bighorn Fire in the Santa Catalina Mountains burned 486 km2 and prompted alerts and evacuations along a 40-km stretch of WUI below 1,200 masl on the outskirts of Tucson, Arizona, a metropolitan area of >1M people. To better understand the changing nature of the WUI here and elsewhere in the region, we took a multidimensional and timely approach to assess fire dynamics along the Desertscrub-Semi-desert Grassland ecotone in the Catalina foothills, which is in various stages of non-native grass invasion. The Bighorn Fire was principally a forest fire driven by a long-history of fire suppression, accumulation of fine fuels following a wet winter and spring, and two decades of hotter droughts, culminating in the hottest and second driest summer in the 125-yr Tucson weather record. Saguaro (Carnegia gigantea), a giant columnar cactus, experienced high mortality. Resprouting by several desert shrub species may confer some post-fire resiliency in desertscrub. Buffelgrass and other non-native species played a minor role in carrying the fire due to the patchiness of infestation at the upper edge of the Desertscrub biome. Coupled state-and-transition fire-spread simulation models suggest a marked increase in both burned area and fire frequency if buffelgrass patches continue to expand and coalesce at the Desertscrub/Semi-desert Grassland interface. A survey of area residents six months after the fire showed awareness of buffelgrass was significantly higher among residents that were evacuated or lost recreation access, with higher awareness of fire risk, saguaro loss and declining property values, in that order. Sustained and timely efforts to document and assess fast-evolving fire connectivity due to grass invasions, and social awareness and perceptions, are needed to understand and motivate mitigation of an increasingly fire-prone future in the region.

Using community photography to investigate phenology: A case study of coat molt in the mountain goat (Oreamnos americanus) with missing data

Participatory approaches, such as community photography, can engage the public in questions of societal and scientific interest while helping advance understanding of ecological patterns and processes. We combined data extracted from community-sourced, spatially explicit photographs with research findings from 2018 fieldwork in the Yukon, Canada, to evaluate winter coat molt patterns and phenology in mountain goats (Oreamnos americanus), a cold-adapted, alpine mammal. Leveraging the community science portals iNaturalist and CitSci, in less than a year we amassed a database of almost seven hundred unique photographs spanning some 4,500 km between latitudes 37.6°N and 61.1°N from 0 to 4,333 m elevation. Using statistical methods accounting for incomplete data, a common issue in community science datasets, we identified the effects of intrinsic (sex and presence of offspring) and broad environmental (latitude and elevation) factors on molt onset and rate and compared our findings with published data. Shedding occurred over a 3-month period between 29 May and 6 September. Effects of sex and offspring on the timing of molt were consistent between the community-sourced and our Yukon data and with findings on wild mountain goats at a long-term research site in west-central Alberta, Canada. Males molted first, followed by females without offspring (4.4 days later in the coarse-grained, geographically wide community science sample; 29.2 days later in our fine-grained Yukon sample) and lastly females with new kids (6.2; 21.2 days later, respectively). Shedding was later at higher elevations and faster at northern latitudes. Our findings establish a basis for employing community photography to examine broad-scale questions about the timing of ecological events, as well as sex differences in response to possible climate drivers. In addition, community photography can help inspire public participation in environmental and outdoor activities specifically with reference to iconic wildlife.

Assessing ecological uncertainty and simulation model sensitivity to evaluate an invasive plant species' potential impacts to the landscape

Ecological forecasts of the extent and impacts of invasive species can inform conservation management decisions. Such forecasts are hampered by ecological uncertainties associated with non-analog conditions resulting from the introduction of an invader to an ecosystem. We developed a state-and-transition simulation model tied to a fire behavior model to simulate the spread of buffelgrass (Cenchrus ciliaris) in Saguaro National Park, AZ, USA over a 30-year period. The simulation models forecast the potential extent and impact of a buffelgrass invasion including size and frequency of fire events and displacement of saguaro cacti and other native species. Using simulation models allowed us to evaluate how model uncertainties affected forecasted landscape outcomes. We compared scenarios covering a range of parameter uncertainties including model initialization (landscape susceptibility to invasion) and expert-identified ecological uncertainties (buffelgrass patch infill rates and precipitation). Our simulations showed substantial differences in the amount of buffelgrass on the landscape and the size and frequency of fires for dry years with slow patch infill scenarios compared to wet years with fast patch infill scenarios. We identified uncertainty in buffelgrass patch infill rates as a key area for research to improve forecasts. Our approach could be used to investigate novel processes in other invaded systems.

A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales

Predictions of habitat suitability for invasive plant species can guide risk assessments at regional and national scales and inform early detection and rapid-response strategies at local scales. We present a general approach to invasive species modeling and mapping that meets objectives at multiple scales. Our methodology is designed to balance trade-offs between developing highly customized models for few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. To ensure efficiency, we used largely automated modeling approaches and human input only at key junctures. We explore and present uncertainty by using two alternative sources of background samples, including five statistical algorithms, and constructing model ensembles. We demonstrate the use and efficiency of the Software for Assisted Habitat Modeling [SAHM 2.1.2], a package in VisTrails, which performs the majority of the modeling analyses. Our workflow includes solicitation of expert feedback on model outputs such as spatial prediction results and variable response curves, and iterative improvement based on new data availability and directed field validation of initial model results. We highlight the utility of the models for decision-making at regional and local scales with case studies of two plant species that invade natural areas: fountain grass (Pennisetum setaceum) and goutweed (Aegopodium podagraria). By balancing model automation with human intervention, we can efficiently provide land managers with mapped predicted distributions for multiple invasive species to inform decisions across spatial scales.

Evaluating Potential Distribution of High-Risk Aquatic Invasive Species in the Water Garden and Aquarium Trade at a Global Scale Based on Current Established Populations

Aquatic non-native invasive species are commonly traded in the worldwide water garden and aquarium markets, and some of these species pose major threats to the economy, the environment, and human health. Understanding the potential suitable habitat for these species at a global scale and at regional scales can inform risk assessments and predict future potential establishment. Typically, global habitat suitability models are fit for freshwater species with only climate variables, which provides little information about suitable terrestrial conditions for aquatic species. Remotely sensed data including topography and land cover data have the potential to improve our understanding of suitable habitat for aquatic species. In this study, we fit species distribution models using five different model algorithms for three non-native aquatic invasive species with bioclimatic, topographic, and remotely sensed covariates to evaluate potential suitable habitat beyond simple climate matches. The species examined included a frog (Xenopus laevis), toad (Bombina orientalis), and snail (Pomacea spp.). Using a unique modeling approach for each species including background point selection based on known established populations resulted in robust ensemble habitat suitability models. All models for all species had test area under the receiver operating characteristic curve values greater than 0.70 and percent correctly classified values greater than 0.65. Importantly, we employed multivariate environmental similarity surface maps to evaluate potential extrapolation beyond observed conditions when applying models globally. These global models provide necessary forecasts of where these aquatic invasive species have the potential for establishment outside their native range, a key component in risk analyses.

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM)

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Regional data refine local predictions: modeling the distribution of plant species abundance on a portion of the central plains

Species distribution models are frequently used to predict species occurrences in novel conditions, yet few studies have examined the consequences of extrapolating locally collected data to regional landscapes. Similarly, the process of using regional data to inform local prediction for species distribution models has not been adequately evaluated. Using boosted regression trees, we examined errors associated with extrapolating models developed with locally collected abundance data to regional-scale spatial extents and associated with using regional data for predictions at a local extent for a native and non-native plant species across the northeastern central plains of Colorado. Our objectives were to compare model results and accuracy between those developed locally and extrapolated regionally, those developed regionally and extrapolated locally, and to evaluate extending species distribution modeling from predicting the probability of presence to predicting abundance. We developed models to predict the spatial distribution of plant species abundance using topographic, remotely sensed, land cover and soil taxonomic predictor variables. We compared model predicted mean and range abundance values to observed values between local and regional. We also evaluated model prediction performance based on Pearson's correlation coefficient. We show that: (1) extrapolating local models to regional extents may restrict predictions, (2) regional data can help refine and improve local predictions, and (3) boosted regression trees can be useful to model and predict plant species abundance. Regional sampling designed in concert with large sampling frameworks such as the National Ecological Observatory Network may improve our ability to monitor changes in local species abundance.

Cloning, isolation, and characterization of mammalian legumain, an asparaginyl endopeptidase

Legumain is a cysteine endopeptidase that shows strict specificity for hydrolysis of asparaginyl bonds. The enzyme belongs to peptidase family C13, and is thus unrelated to the better known cysteine peptidases of the papain family, C1 (Rawlings, N. D., and Barrett, A. J. (1994) Methods Enzymol. 244, 461-486). To date, legumain has been described only from plants and a blood fluke, Schistosoma mansoni. We now show that legumain is present in mammals. We have cloned and sequenced human legumain and part of pig legumain. We have also purified legumain to homogeneity (2200-fold, 8% yield) from pig kidney. The mammalian sequences are clearly homologous with legumains from non-mammalian species. Pig legumain is a glycoprotein of about 34 kDa, decreasing to 31 kDa on deglycosylation. It is an asparaginyl endopeptidase, hydrolyzing Z-Ala-Ala-Asn-7-(4-methyl)coumarylamide and benzoyl-Asn-p-nitroanilide. Maximal activity is seen at pH 5.8 under normal assay conditions, and the enzyme is irreversibly denatured at pH 7 and above. Mammalian legumain is a cysteine endopeptidase, inhibited by iodoacetamide and maleimides, but unaffected by compound E64 (trans-epoxysuccinyl-L-leucylamido-(4-guanidino)butane). It is inhibited by ovocystatin (cystatin from chicken egg white) and human cystatin C with Ki values < 5 nM. We discuss the significance of the discovery of a cysteine endopeptidase of a new family and distinctive specificity in man and other mammals.

Monocular and binocular visual evoked potentials before and after cataract surgery

Visual evoked cortical potentials (VEPs) were recorded from an individual with a mature cataract in one eye. Stimulation was both monocular and binocular and VEPs were obtained from three occipital scalp sites. Comparisons of recordings taken prior to cataract removal and after removal showed a dramatic increase in amplitude of potentials derived through stimulation of the affected eye. Slight differences in hemispheric amplitude prior to surgery suggest a greater degree of opacity in one portion of the affected lens.

The effect of shearing pregnant ewes on lamb birth weights

Shearing housed pregnant ewes at about the 11th week of pregnancy resulted in increased ewe liveweight gain (P greater than 0-01) and heavier twin litter weights (P greater than 0-01). The lambs from the shorn ewes also grew faster when put out to pasture. Unshorn pregnant ewes had a faster and more variable respiration rate, probably due to their being too hot in the prevailing conditions. The effect of this apparent heat stress on fetal nutrition is discussed.