Comparison of visual estimation and line point intercept vegetation survey methods on annual grass-invaded rangelands of Wyoming

Scientists and natural resource managers require suitable vegetation survey methods to assess the success of rangeland restoration projects. Visual estimation and point intercept methods are commonly used to evaluate vegetation cover. This study compared the performance of one visual (quadrat-based) and two line point intercept (LPI – canopy and basal) methods to assess biodiversity, cover, and to estimate biomass production on sites invaded by introduced annual grasses across Wyoming, USA. Greater species richness and higher Shannon index values were measured in quadrats, while introduced annual and native perennial graminoid cover values were higher in LPI canopy in general. Overall, these outcomes indicate quadrats as the most suitable survey method when biodiversity monitoring is the primary objective, while suggesting LPI canopy when monitoring vegetation cover is prioritized. Finally, our regression models indicated quadrat-based estimates as the most reliable to predict introduced annual and native perennial graminoid biomass.

Future land use change simulations for the Lepelle River Basin using Cellular Automata Markov model with Land Change Modeller-generated transition areas

Background: Land use/land cover (LULC), change is one of the major contributors to global environmental and climate variations. The ability to predict future LULC is crucial for environmental engineers, civil engineers, urban designers, and natural resource managers for planning activities. Methods: TerrSet Geospatial Monitoring and Modelling System in conjunction with ArcGIS Pro 2.8 were used to process LULC data for the region of the Lepelle River Basin (LRB) of South Africa. Driver variables such as population density, slope, elevation as well as the Euclidean distances of cities, roads, highways, railroads, parks and restricted areas, towns to the LRB in combination with LULC data were analysed using the Land Change Modeller (LCM) and Cellular-Automata Markov (CAM) model. Results: The results reveal an array of losses (-) and gains (+) for certain LULC classes in the LRB by the year 2040: natural vegetation (+8.5%), plantations (+3.5%), water bodies (-31.6%), bare ground (-8.8%), cultivated land (-29.3%), built-up areas (+10.6%) and mines (+14.4%). Conclusions: The results point to the conversion of land uses from natural to anthropogenic by 2040. These changes also highlight how the potential losses associated with resources such as water will negatively impact society and ecosystem functioning in the LRB by exacerbating water scarcity driven by climate change. This modelling study seeks to provides a decision support system for predicting future land resource utilization in the LRB and perhaps assist for planning purposes.

Immersive landscapes: modelling ecosystem reference conditions in virtual reality

Context Understanding the variability and dynamics of ecosystems, as well as their responses to climate or land use change, is challenging for policy makers and natural resource managers. Virtual reality (VR) can be used to render virtual landscapes as immersive, visceral experiences and communicate ecosystem dynamics to users in an effective and engaging way. Objectives To illustrate the potential and believability of VR, a team of landscape ecologists and immersive visualisation researchers modelled a reference Australian Box Gum Grassy Woodland landscape, an endangered eucalypt woodland ecosystem that is difficult to observe in its pre-European colonisation form. Methods We document considerations for designing the immersive virtual landscape, including the creation of animated three-dimensional (3D) plants that alternate between the seasons, and soundscapes that change through the course of a simulated day. We used a heuristic evaluation with experts to assess the potential of immersive VR landscape modeling. Results This cross disciplinary collaboration resulted in a VR experience that was evaluated in a series of meetings by 27 ecologists and managers in biodiversity conservation, many of whom were familiar with Box Gum Grassy Woodlands. 88% of participants stated that the simulation was believable and participants thought that virtual landscapes held great potential for education, public engagement and land management. Conclusions Possible future directions include open-source libraries of ecological 3D models, and the visual simulation of historic landscapes and future climate change scenarios.

Improving Estimates of Natural Resources Using Model-Based Estimators: Impacts of Sample Design, Estimation Technique, and Strengths of Association

Natural resource managers need accurate depictions of existing resources to make informed decisions. The classical approach to describing resources for a given area in a quantitative manner uses probabilistic sampling and design-based inference to estimate population parameters. While probabilistic designs are accepted as being necessary for design-based inference, many recent studies have adopted non-probabilistic designs that do not include elements of random selection or balance and have relied on models to justify inferences. While common, model-based inference alone assumes that a given model accurately depicts the relationship between response and predictors across all populations. Within complex systems, this assumption can be difficult to justify. Alternatively, models can be trained to a given population by adopting design-based principles such as balance and spread. Through simulation, we compare estimates of population totals and pixel-level values using linear and nonlinear model-based estimators for multiple sample designs that balance and spread sample units. The findings indicate that model-based estimators derived from samples spread and balanced across predictor variable space reduce the variability of population and unit-level estimators. Moreover, if samples achieve approximate balance over feature space, then model-based estimates of population totals approached simple expansion-based estimates of totals. Finally, in all comparisons made, improvements in estimation were achieved using model-based estimation over design-based estimation alone. Our simulations suggest that samples drawn from a probabilistic design, that are spread and balanced across predictor variable space, improve estimation accuracy.

Multiyear defoliations in southern New England increases oak mortality

After decades of multiyear defoliation episodes in southern New England, Lymantria dispar dispar (previously gypsy moth) populations diminished with the appearance of the L. dispar fungus in 1989. Multiyear defoliations did not occur again until 2015-2018. To assess the impact of the return of multiyear defoliations, we examined 3095 oaks on 29 permanent study areas in Connecticut and Rhode Island that were established at least eleven years before the latest outbreaks. Pre-defoliation stand level oak mortality averaged 2% (three-year basis). Post-defoliation mortality did not differ between managed and unmanaged stands, but was much higher in severely defoliated stands (36%) than in stands with moderate (7%) or low-no defoliation (1%). Pre-defoliation mortality of individual trees differed among species, was lower for larger diameter trees and on unmanaged than managed stands. Post-defoliation mortality on plots with no to moderate defoliation was similar to pre-defoliation mortality levels. Following multiyear defoliations, white oak mortality was higher than for northern red and black oak. There was weak evidence that mortality was elevated on stands with higher basal area following severe defoliation. Natural resource managers should not assume that oaks that survived earlier multiyear defoliations episodes will survive future multiyear outbreaks, possibly because trees are older.

Chromosome level genome assembly and annotation of highly invasive Japanese stiltgrass (Microstegium vimineum)

The invasive Japanese stiltgrass (Microstegium vimineum) affects a wide range of ecosystems and threatens biodiversity across the eastern USA. However, the mechanisms underlying rapid adaptation, plasticity, and epigenetics in the invasive range are largely unknown. We present a chromosome-level assembly for M. vimineum to investigate genome dynamics, evolution, adaptation, and the genomics of phenotypic plasticity. We generated a 1.12 Gb genome with scaffold N50 length of 53.44 Mb respectively, taking a de novo assembly approach that combined PacBio and Dovetail Genomics Omni-C sequencing. The assembly contains 23 pseudochromosomes, representing 99.96% of the genome. BUSCO assessment indicated that 80.3% of Poales gene groups are present in the assembly. The genome is predicted to contain 39,604 protein-coding genes, of which 26,288 are functionally annotated. Furthermore, 66.68% of the genome is repetitive, of which unclassified (35.63%) and long terminal repeat (LTR) retrotransposons (26.90%) are predominant. Similar to other grasses, Gypsy (41.07%) and Copia (32%) are the most abundant LTR-retrotransposon families. The majority of LTR-retrotransposons are derived from a significant expansion in the past 1-2 million years, suggesting the presence of relatively young LTR-retrotransposon lineages. We find corroborating evidence from Ks plots for a stiltgrass-specific duplication event, distinct from the more ancient grass-specific duplication event. The assembly and annotation of M. vimineum will serve as an essential genomic resource facilitating studies of the invasion process, the history and consequences of polyploidy in grasses, and provides a crucial tool for natural resource managers.

Climate and Coast: Overview and Introduction

Coasts are those regions of the world where the land has an impact on the state of the sea, and that part of the land is in turn affected by the sea. This land–sea interaction may take various forms—geophysical, biological, chemical, sociocultural, and economic. Coasts are conditioned by specific regional conditions. These unique characteristics result, in heavily fragmented regional and disciplinary research agendas, among them geographers, meteorologists, oceanographers, coastal engineers, and a variety of social and cultural sciences. Coasts are regions where the effects and risks of climate impact societal and ecological life. Such occurrences as coastal flooding, storms, saltwater intrusion, invasive species, declining fish stocks, and coastal retreat and morphological change are challenging natural resource managers and local governments to mitigate these impacts. Societies are confronted with the challenge of dealing with these changes and hazards by developing appropriate cultural practices and technical measures. Key aspects and concepts of these dimensions are presented here and will be examined in more detail in the future to expand on their characteristics and significance.

Coastal flood risk within a peri-urban area: SUSSEX Inlet district, SE Australia

The peri-urban interface (PUI) exhibits characteristic qualities of both urban and rural regions, and this complexity has meant that risk assessments and long-term planning for PUI are lagging, despite these areas representing new developing settlement frontiers. This study aims to address this knowledge gap by modifying an existing approach to quantify and assess flood risk. The risk triangle framework was used to map exposure, vulnerability and biophysical variables; however, in a novel application, the risk triangle framework was adapted by presuming that there is a variation in the degree of exposure, vulnerability and biophysical variables. Within Australia and globally, PUIs are often coastal, and flood risk associated with rainfall and coastal inundation poses considerable risk to communities in the PUI; these risks will be further exacerbated should projections of increasing frequency of extreme rainfall events and accelerating sea-level rise eventuate. An indicator-based approach using the risk triangle framework that maps flood hazard, exposure and vulnerability was used to integrate the biophysical and socio-economic flooding risk for communities in PUI of the St Georges Basin and Sussex Inlet catchments of south-eastern Australia. Integrating the flood risk triangle with future scenarios of demographic and climate change, and considering factors that contribute to PUI flood risk, facilitated the identification of planning strategies that would reduce the future rate of increase in flood risk. These planning strategies are useful for natural resource managers and land use planners across Australia and globally, who are tasked with balancing socio-economic prosperity for a changing population, whilst maintaining and enhancing ecosystem services and values. The indicator-based approach used in this study provides a cost-effective first-pass risk assessment and is a valuable tool for decision makers planning for flood risk across PUIs in NSW and globally.