Foliage Projective Cover Research Articles

Unravelling the impact of climate change on honey bees: An ensemble modelling approach to predict shifts in habitat suitability in Queensland, Australia.

Honey bees play a vital role in providing essential ecosystem services and contributing to global agriculture. However, the potential effect of climate change on honey bee distribution is still not well understood. This study aims to identify the most influential bioclimatic and environmental variables, assess their impact on honey bee distribution, and predict future distribution. An ensemble modelling approach using the biomod2 package in R was employed to develop three models: a climate-only model, an environment-only model, and a combined climate and environment model. By utilising bioclimatic data (radiation of the wettest and driest quarters and temperature seasonality) from 1990 to 2009, combined with observed honey bee presence and pseudo absence data, this model predicted suitable locations for honey bee apiaries for two future time spans: 2020-2039 and 2060-2079. The climate-only model exhibited a true skill statistic (TSS) value of 0.85, underscoring the pivotal role of radiation and temperature seasonality in shaping honey bee distribution. The environment-only model, incorporating proximity to floral resources, foliage projective cover, and elevation, demonstrated strong predictive performance, with a TSS of 0.88, emphasising the significance of environmental variables in determining habitat suitability for honey bees. The combined model had a higher TSS of 0.96, indicating that the combination of climate and environmental variables enhances the model's performance. By the 2020-2039 period, approximately 88% of highly suitable habitats for honey bees are projected to transition from their current state to become moderate (14.84%) to marginally suitable (13.46%) areas. Predictions for the 2060-2079 period reveal a concerning trend: 100% of highly suitable land transitions into moderately (0.54%), marginally (17.56%), or not suitable areas (81.9%) for honey bees. These results emphasise the critical need for targeted conservation efforts and the implementation of policies aimed at safeguarding honey bees and the vital apiary industry.

The ‘lawnification’ of Australia’s eastern grassy woodlands: the past, current and likely future spread of an invasive perennial grass, Bothriochloa pertusa

Many of today’s damaging invasive plants were intentionally introduced for pasture development and amenity. By examining the introduction history and consequent spread of these species, we can identify factors associated with their successful establishment and dominance. Using collated presence/absence and cover data, alongside a review of the literature and discussions with land managers, we present a comprehensive analysis of the introduction history and spread of the environmental and agricultural grassy weed, Bothriochloa pertusa (L.) A.Camus (Indian couch) throughout Queensland, Australia. Using this data, we also perform habitat suitability models to predict its potential distribution and local-scale cover across Queensland in relation to key environmental variables. We found that B. pertusa was introduced on multiple occasions and across a large area of Queensland, despite re-occurring doubts and poor evidence for its benefit to livestock production. Livestock grazing, associated disturbances (i.e. land clearing, soil erosion) and climatic extremes were commonly associated with its spread throughout the landscape. In 2020 the main area of B. pertusa invasion as indicated by occurrence records spanned 28,537,600 ha. Results from the habitat suitability models suggest the occurrence and local-scale cover of B. pertusa is largely determined by climate variables and the foliage projective cover of trees. Based on these results B. pertusa still has considerable capacity to spread and increase in dominance across many areas of Queensland, particularly further west and south of its current range. The introduction and spread history of B. pertusa suggests propagule pressure, traits, climate, land management and cultural perceptions are all key factors implicated in the spread of B. pertusa. Where B. pertusa has become dominant there has been a major shift in lifeform from native perennial tussock species to a grazing tolerant stoloniferous species. To slow this process of ‘lawnification’ we recommend more conservative grazing strategies and strategically selected protected areas to maintain cover of grazing sensitive native tussock grass species.

Factors influencing the activity ranges of feral pigs (Sus scrofa) across four sites in eastern Australia

Context Understanding the home-range size and the ecological drivers that influence the spatial distribution of feral pigs is of paramount importance for exotic-disease modelling and the improvement of pest management programs. Aims To investigate various factors affecting home- and core-range size and test selection of habitat, to better inform disease modelling and pest management programs. Methods In this study, 59 GPS-collared feral pigs were tracked over four sites in eastern Australia between 2017 and 2021. Using minimum convex polygon (MCP) and the nearest-neighbour–local convex hull (k-LoCoH) as home-range estimators and foliage projective cover (FPC) as an estimator of landscape-scale shelter, we investigated the influence of sex, site, season, year and body weight on range size and tested selection of habitat by using chi-squared and Jacob’s index tests. Key results Home-range sizes were highly variable, with k-LoCoH90 (home) ranges between 0.08 and 54.97 km2 and k-LoCoH50 (core) ranges between 0.01 and 7.02 km2. MCP90 ranged between 0.15 and 242.30 km2, with MCP50 being between 0.07 and 60.61 km2. Sex and site both significantly (P < 0.001) influenced home-range size, but season and year did not. Home-range size was shown to increase with body mass for both sexes (P = 0.001). Importantly, the data indicated that feral pigs prefer habitat within 20–40% FPC (woodland), whereas open forests (51–80% FPC) and closed forests (>80% FPC) were actively avoided. Typically, use of open vegetation (1–10% FPC) was also avoided, but this behaviour varied and was dependent on site. Conclusion Feral pig ranges are influenced by sex, site and body mass but not by season and year. Broad-scale selection for shelter indicated that feral pigs prefer habitat between 20% and 40% FPC. Implications Targeting or avoiding such areas respectively for control or monitoring tool placement may result in improved, efficient outcomes to monitor or manage feral pig populations. Feral pig distribution modelling may also find benefit in the consideration and further study of the above factors and the influence of food and water sources on the activity ranges and behaviour of feral pigs.

Carbon-dioxide-driven increase in foliage projective cover is not the same as increased woody plant density: lessons from an Australian tropical savanna

Carbon accounting in tropical savannas relies on a good understanding of the effects of atmospheric carbon dioxide (CO2) and land management on foliage projective cover (FPC) and vegetation structure. We used generalised additive modelling to track changes in Autumn Persistent Green (APG, a satellite-image-derived measure of FPC) in six vegetation types on Cape York Peninsula, Australia, over an 18-year period, and examined the influence of fire and grazing land tenure. We then used field monitoring and variography (analysis of spatial autocorrelation) in a smaller study area to determine whether changes in APG reflected vegetation structural change. APG increased through the 18-year study period and was significantly influenced by vegetation type, recent fire history and grazing land tenure. Residual year-on-year increases suggest CO2 fertilisation was the main driver of APG increase. APG was reduced by fires in the previous year, with early dry season fires having greater impact than late dry season fires, particularly in grassland and rainforest. This is consistent with leaves being most fire sensitive early in the year, when they are actively growing, than in the late dry season, when they are dormant. As seedlings and suckers would be particularly fire-sensitive, early fires may therefore be more effective than late fires at preventing woody encroachment. We demonstrated that variography provides a good indication of whether APG increases are caused by increases in FPC alone, or by an increase in tree density. We found support for increased woody plant density in grasslands, and that this increase was most pronounced on grazing lands. Conversely, we found no support for stem density increases in the dominant eucalypt woodland, despite APG increases being highest in this vegetation type. Hence, increases in FPC cannot always be equated to increases in woody biomass, and may occur in their absence. This conclusion has serious implications for global carbon accounting.

The post-fire stability index; a new approach to monitoring post-fire recovery by satellite imagery

Ecological resilience is the capacity of a system to maintain function following disturbance. With the frequency and severity of wildfire activity increasing due to warmer and drier global climate conditions, there are increasing reports of declines in ecological resilience and ecosystems at risk of collapse due to post-fire recovery failure. Observational monitoring of post-fire recovery at the landscape scale is important to understand drivers, identify vulnerable ecosystems and prioritize management intervention to support resilience. Defining a suitably representative baseline condition to compare post-fire recovery states against can be challenging, particularly in broad-scale remote sensing approaches. Here, we introduce a new approach to monitoring post-fire recovery by satellite imagery, the post-fire stability index. The method is based on the concept that a disturbed system state will be reflected by increasing or decreasing rates of system change, while undisturbed or recovered system states are characterised by near-zero rates of change. This reflects the typical pattern of diminishing rate of change in post-fire recovery trajectories. We demonstrate strong performance and suitability of the post-fire stability index in comparison to alternative approaches through time series analyses, and independent validation from post-fire vegetation surveys taken at one-year post-fire. The post-fire stability index was consistently the best performing model across all field measures of vegetative response following the fire event. In ecosystems which exhibit post-fire resprouting, higher values in the post-fire stability index were associated with higher levels of field-based measures of foliage projective cover and canopy cover (+/− resprouting), and with lower levels of basal and no resprouting. The post-fire stability index provides a relatively simple and practical solution for consistent broad-scale monitoring post-fire recovery with satellite imagery, which together with standardised fire severity mapping, provide extensive opportunities for further fire and landscape ecology research.

Determining the distributions of plant communities in subantarctic vegetation using species distribution models

Context Mapping of vegetation is important in understanding its dynamics in relation to climate change and disturbance. We investigated using species distribution models to predict plant species assemblages in a subantarctic environment where traditional image interpretation methods of vegetation mapping are limited by image availability and ability to discriminate vegetation types. Aims We test the efficacy for mapping of modelling the range and core range of common species. We also determine the relative importance of predictor variables for each of nine species. Methods We used random forest models to predict the total range and core range (>25% projected foliage cover) of nine potentially dominant plant species and determined the contributions of predictor variables to the models for each species. Key results Widespread species with extensively overlapping ranges were spatially more partitioned with modelling based on core range than with presence or absence modelling. The core range input produced a vegetation map that better approximated observed vegetation patterns than that from presence or absence data. The most important predictor variable varied between species, with elevation, distance from coast, latitude and an across island gradient (similar to longitude) being most influential. Conclusions Species distribution models using three categories (absent, <25% cover, ≥25% cover) and topographic variables derived from a digital elevation model can be used to model the distribution of vegetation assemblages in situations where presence or absence species models cannot discriminate assemblages. Implications Readily collected point location species data could be used to investigate change over time in the spatial extent of both species and vegetation types.

Impact of a prolonged decline in rainfall on eucalypt woodlands in southwestern Australia and its consequences for avifauna

Aims Our objective was to establish a relationship between long-term variation in the climatic environment, tree canopy decline and observed effects on the population dynamics of avifauna in the Dryandra Woodlands in southwestern Australia. These geographically isolated remnant woodlands are rich in endemic species and sustain a diverse range of ecological communities, but are threatened by habitat degradation and a decline in rainfall. Methods We used annual rainfall data, averaged from a series of weather stations within 100 km of the Dryandra Woodlands and a time series analysis to investigate long-term changes in annual rainfall. Satellite spectral observations of eight study sites at Dryandra was used to measure changes in Projected Foliage Cover (PFC) of old growth Eucalyptus wandoo at all sites. Our mist-net trapping study across three years and all eight sites, targeted two focal species; the rufous treecreeper (Climacteris rufa) and yellow-plumed honeyeater (Ptilotula ornata). We investigated the relationship between the captures of each species and variation in PFC, between sites and across years. Also in a separate demographic study, capture-mark-recapture data was used to estimate the apparent survival rate of each species, following the robust design for open and closed populations. Key results We demonstrate a long-term and continuing decline in average annual rainfall that is accelerating. We found the rainfall trend is concomitant with a long-term decline in PFC of E. wandoo and that the previous year’s annual rainfall is a predictor of average PFC across all sites. Additionally, we discovered that the PFC at each site, in each year, is a predictor of the number of yellow-plumed honeyeaters which prefer feeding on canopy insects and not a predictor of the predominantly ground-foraging rufous treecreeper. We also found a substantial difference in the apparent survival rates between the two species, with the apparent survival of yellow-plumed honeyeaters being approximately half that of rufous treecreepers. This difference was partially attributed to the likely movement outside of the study area due to decreasing habitat quality. Conclusions and implications Overall, our results do suggest that some impacts of long-term rainfall trends can be traced to particular species through PFC variation, but the response between species to habitat change will differ and depend on species-specific habitat requirements. As increasing greenhouse emissions are associated with declining rainfall in southwestern Australia, this study shows if rainfall decline and habitat degradation continue, it will have catastrophic consequences for woodland ecosystems.

An online system for calculating and delivering long-term carrying capacity information for Queensland grazing properties. Part 2: modelling and outputs

A combination of field data and models have been used to estimate long-term carrying capacity (LTCC) of domestic livestock in Queensland grazing lands. These methods have been synthesised and coupled with recent developments in science and information technology to provide a fully-automated approach of modelling LTCC through the FORAGE online system. In this study, the GRASP model was used to simulate pasture growth with parameter sets and safe pasture utilisation rates defined for 225 land types across Queensland. Distance to water points was used to assess the accessibility of pastures to livestock. Spatial analysis classified the property into unique areas based on paddock, land type and distance to water points, which estimated pasture growth, pasture utilisation and accessibility at a sub-paddock scale. Thirteen foliage projective cover (FPC) classes were used in modelling the pasture system to deal with the non-linear relationship between tree and grass interactions. As ‘proof of concept’, remotely-sensed individual-date green ground cover data were used to optimise the GRASP model parameters to improve the model performance, and a Monte Carlo analysis provided uncertainty estimates for model outcomes. The framework provides an efficient and standardised method for estimating LTCC. To test the system, LTCCs from 43 ‘benchmark’ properties were compared with simulated LTCCs, and 65% of the modelled LTCCs were within ± 25% of the benchmark LTCCs. Due to uncertainties in model inputs at the property scale and in model simulation, the modelled LTCC should be used as a starting point for further refinement of actual property LTCC.

The association of fractional cover, foliage projective cover and biodiversity with birthweight

IntroductionEnvironmental exposures can contribute both benefits and risks to human health. Maternal exposure to green space has been associated with improvements in birthweight, among other birth outcomes. Newer measures of green space have been developed, which allows for an exploration of the effect of different ground covers (green, dry and bare earth), as well as measures of biodiversity. This study explores the association of these novel green space measures with birthweight in a large birth cohort in Queensland, Australia. MethodsBirthweight was acquired from the routine health records. Records were allocated green space values for fractional cover, biodiversity and foliage projective cover. Directed acyclic graphs were developed to guide variable selection. Mixed-effects linear regression and generalised linear mixed-effects models were developed, with random intercepts for maternal residential locality and year of birth. Results are presented as standardised beta coefficients or odds ratios, with 95% confidence intervals. ResultsAn IQR increase of green cover (29.6 g, 95% CI 13.8–45.5) and foliage projective cover (26.0 g, 95% CI 10.8–41.3) are associated with birthweight in urban areas. An IQR increase in dry cover −34.4 g, 95% CI -60.4 to −8.4) and bare earth (−17.7 g, 95% CI -32.8 to −2.6) are associated with lower birthweight. Mothers living in rural areas had similar results, with an IQR increase in green cover (17.8 g, 95% CI 2.9–32.7) associated with higher birthweight, and bare earth (−27.7 g, 95% CI -45.7 to −9.7) was associated with lower birthweight. The biodiversity measure used in this study was not associated with any birthweight outcomes. ConclusionThis study finds that the types of ground cover within the maternal residential locality are associated with small, but significant, changes in estimated birthweight, and these effects are not limited to urban areas.

The Effect of Grazing on the Temperature Regime of the Alas Soils of Central Yakutia

There are numerous studies on the effect of grazing on the physical and chemical parameters of soils. However, the impact of grazing on the temperature regime of the alas soils in Central Yakutia is still poorly understood. This paper presents the results of long-term observations of the state of the soil-and-plant cover of thermokarst basins—i.e., alases—located in the Lena-Amga interfluve and actively used as pastureland. Observations of the process of the self-restoration of grass cover and changes in the temperature regime of alas soils were carried out on different variants (with isolation from grazing and without isolation). A significant increase in the average height of the grass stand and its foliage projective cover was observed with a gradual reduction in the number of species when isolated from grazing. Changes in the structure of the alas vegetation cover influence the microclimate of the soil. As a result of livestock grazing, the mean annual soil temperature rises and the amount and depth of the penetration of active temperatures increase. The most severe changes in the temperature regime occur in years with an abnormally high cover of snow. The soil warming observed during grazing undoubtedly has a favorable effect on soil organisms. However, in conditions of climate warming, grazing, especially overgrazing, can disrupt the permafrost regime and thereby provoke or intensify thermokarst phenomena.

Strong shrub expansion in tundra-taiga, tree infilling in taiga and stable tundra in central Chukotka (north-eastern Siberia) between 2000 and 2017

Vegetation is responding to climate change, which is especially prominent in the Arctic. Vegetation change is manifest in different ways and varies regionally, depending on the characteristics of the investigated area. Although vegetation in some Arctic areas has been thoroughly investigated, central Chukotka (NE Siberia) with its highly diverse vegetation, mountainous landscape and deciduous needle-leaf treeline remains poorly explored, despite showing strong greening in remote-sensing products. Here we quantify recent vegetation compositional changes in central Chukotka over 15 years between 2000/2001/2002 and 2016/2017. We numerically related field-derived information on foliage projective cover (percentage cover) of different plant taxa from 52 vegetation plots to remote-sensing derived (Landsat) spectral indices (Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI) and Normalised Difference Snow Index (NDSI)) using constrained ordination. Clustering of ordination scores resulted in four land-cover classes: (1) larch closed-canopy forest, (2) forest tundra and shrub tundra, (3) graminoid tundra and (4) prostrate herb tundra and barren areas. We produced land-cover maps for early (2000, 2001 or 2002) and recent (2016 or 2017) time-slices for four focus regions along the tundra-taiga vegetation gradient. Transition from graminoid tundra to forest tundra and shrub tundra is interpreted as shrubification and amounts to 20% area increase in the tundra-taiga zone and 40% area increase in the northern taiga. Major contributors of shrubification are alder, dwarf birch and some species of the heather family. Land-cover change from the forest tundra and shrub tundra class to the larch closed-canopy forest class is interpreted as tree infilling and is notable in the northern taiga. We find almost no land-cover changes in the present treeless tundra.

Modelling canopy gap probability, foliage projective cover and crown projective cover from airborne lidar metrics in Australian forests and woodlands

Tree canopy density metrics (TCDM) derived from airborne lidar data are used in a range of crucial environmental monitoring, forestry and natural resource management applications. The derivation of spatially and temporally consistent TCDM, however, typically requires field calibration to account for differences in instrument/survey parameters. Lidar surveys with no coincident field measurements consequently will have an unknown error associated with TCDM limiting their application. In this study, we analysed an extensive set of lidar captures with coincident field data to determine the lidar TCDM that best match the canopy gap probability (Pgap), foliage projective cover (FPC) and crown projective cover (CPC). Furthermore, we developed and evaluated models designed to reduce the bias introduced by variations in lidar instrument and survey acquisition parameters. The dataset incorporated 148 field sites (100 m diameter circular plots) coincident with 13 different lidar surveys between 2008 and 2015, distributed across a range of Australian forests and woodlands. The best lidar metric for 1 − Pgap, achieving a root mean square error (RMSE) of 6.7% with 95% confidence intervals (CI) of 6.1–7.3%, was the proportion of all returns greater than a canopy height threshold (tcanopy) of 1.5 m above ground (dall). The best metric for FPC (RMSE = 6.0%, CI = 5.3–6.7%) used the proportion of returns, weighted as the fraction of the number of returns recorded from each pulse (dweighted), with tcanopy of 1.7 m. The best metric for CPC (RMSE = 7.0%, CI = 6.4–7.7%) was the proportion of 0.5 m pixels greater than 0.8 m above the ground, for an interpolated canopy height model (dinterp). Overall bias for these metrics was low (~1%), however, the bias for individual surveys varied significantly. For example, for one survey dall consistently underestimated 1 − Pgap with a bias of −8.3%, while a different survey consistently overestimated 1 − Pgap with a bias of 3.8%. Elastic net regression models, using instrument, survey and plot parameters as predictor variables, were unable to consistently remove the bias. No relationships could be discerned between lidar parameters and the bias between lidar metrics and field measurements, potentially due to complex interactions between parameters, the spatial scale of the field plots, and uncertainties in field measurements and lidar attributes. Although the bias could not be modelled, the results provide metrics to derive Pgap, FPC and CPC with less than 10% error from lidar surveys captured with similar parameters across Australia (>600,000 km2).

Solidago canadensis impacts on tillable land withdrawn from the farming turnover

Solidago canadensis is the extremely aggressive alien species. Our study aimed to monitor the invasiveness of S. canadensis in the abandoned lands. The parameters characterizing its invasiveness (average shoot length, foliage projective cover, average number of vegetative shoots, average number of generative shoots in S. canadensis and the average number of associated species at the plots) were measured at two plant communities of the abandoned lands. According to our results, the invasion reduced the native plant species richness throughout 2016 and 2018. Therefore, the invasion of this species diverts of the vegetation succession, alters the mutual links between the native elements of these old fields.

A Decision Tree Approach for Spatially Interpolating Missing Land Cover Data and Classifying Satellite Images

Sustainable Development Goals (SDGs) are a set of priorities the United Nations and World Bank have set for countries to reach in order to improve quality of life and environment globally by 2030. Free satellite images have been identified as a key resource that can be used to produce official statistics and analysis to measure progress towards SDGs, especially those that are concerned with the physical environment, such as forest, water, and crops. Satellite images can often be unusable due to missing data from cloud cover, particularly in tropical areas where the deforestation rates are high. There are existing methods for filling in image gaps; however, these are often computationally expensive in image classification or not effective at pixel scale. To address this, we use two machine learning methods—gradient boosted machine and random forest algorithms—to classify the observed and simulated ‘missing’ pixels in satellite images as either grassland or woodland. We also predict a continuous biophysical variable, Foliage Projective Cover (FPC), which was derived from satellite images, and perform accurate binary classification and prediction using only the latitude and longitude of the pixels. We compare the performance of these methods against each other and inverse distance weighted interpolation, which is a well-established spatial interpolation method. We find both of the machine learning methods, particularly random forest, perform fast and accurate classifications of both observed and missing pixels, with up to 0.90 accuracy for the binary classification of pixels as grassland or woodland. The results show that the random forest method is more accurate than inverse distance weighted interpolation and gradient boosted machine for prediction of FPC for observed and missing data. Based on the case study results from a sub-tropical site in Australia, we show that our approach provides an efficient alternative for interpolating images and performing land cover classifications.

A Structural Classification of Australian Vegetation Using ICESat/GLAS, ALOS PALSAR, and Landsat Sensor Data

Australia has historically used structural descriptors of height and cover to characterize, differentiate, and map the distribution of woody vegetation across the continent but no national satellite-based structural classification has been available. In this study, we present a new 30-m spatial resolution reference map of Australian forest and woodland structure (height and cover), with this generated by integrating Landsat Thematic Mapper (TM) and Enhanced TM, Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) and Ice, Cloud, and land Elevation (ICESat),and Geoscience Laser Altimeter System (GLAS) data. ALOS PALSAR and Landsat-derived Foliage Projective Cover (FPC) were used to segment and classify the Australian landscape. Then, from intersecting ICESat waveform data, vertical foliage profiles and height metrics (e.g., 95% percentile height, mean height and the height to maximum vegetation density) were extracted for each of the classes generated. Within each class, and for selected areas, the variability in ICESat profiles was found to be similar with differences between segments of the same class attributed largely to clearance or disturbance events. ICESat metrics and profiles were then assigned to all remaining segments across Australia with the same class allocation. Validation against airborne LiDAR for a range of forest structural types indicated a high degree of correspondence in estimated height measures. On this basis, a map of vegetation height was generated at a national level and was combined with estimates of cover to produce a revised structural classification based on the scheme of the Australian National Vegetation Information System (NVIS). The benefits of integrating the three datasets for segmenting and classifying the landscape and retrieving biophysical attributes was highlighted with this leading the way for future mapping using ALOS-2 PALSAR-2, Landsat/Sentinel-2, Global Ecosystem Dynamics Investigation (GEDI), and ICESat-2 LiDAR data. The ability to map across large areas provides considerable benefits for quantifying carbon dynamics and informing on biodiversity metrics.

Morphological Variation Tracks Environmental Gradients in an Agricultural Pest, Phaulacridium vittatum (Orthoptera: Acrididae).

Invertebrate pests often show high morphological variation and wide environmental tolerances. Knowledge of how phenotypic variation is associated with environmental heterogeneity can elucidate the processes underpinning these patterns. Here we examine morphological variation and relative abundance along environmental gradients in a widespread agricultural pest, native to Australia, the wingless grasshopper Phaulacridium vittatum (Sjöstedt). We test for correlations between body size, wing presence, and stripe polymorphism with environmental variables. Using multiple regression and mixed-effects modeling, body size and stripe polymorphism were positively associated with solar radiation, and wing presence was positively associated with foliage projective cover (FPC). There were no associations between body size or morphological traits with relative abundance. However, relative abundance was positively associated with latitude, soil moisture, and wind speed, but was negatively associated with FPC. Therefore, sites with low relative abundance and high forest cover were more likely to contain winged individuals. Overall, our results suggest that environmental and climatic conditions strongly influence the relative abundance and the distribution of morphotypes in P. vittatum, which is likely to affect dispersal and fitness in different landscapes. This knowledge is useful for informing how environmental change might influence the future spread and impact of this agricultural pest.

The extent of mangrove change and potential for recovery following severe Tropical Cyclone Yasi, Hinchinbrook Island, Queensland, Australia.

Cyclones are significant drivers of change within mangrove ecosystems with the extent of initial damage determined by storm severity, location and distribution (exposure), and influenced by species composition and structure (e.g., height). The long‐term recovery of mangroves is often dependent upon hydrological regimes, as well as the frequency of storm events. On February 3, 2011, Tropical Cyclone Yasi (Category 5) made landfall on the coast of north Queensland Australia with its path crossing the extensive mangroves within and surrounding Hinchinbrook Island National Park. Based on a combination of Landsat‐derived foliage projective cover (FPC), Queensland Globe aerial imagery, and RapidEye imagery, 16% of the 13,795 ha of mangroves experienced severe windthrow during the storm. The greatest damage from the cyclone was inflicted on mangrove forests dominated primarily by Rhizophora stylosa, whose large prop roots were unable to support them as wind speeds exceeded 280 km/hr. Classification of 2016 RapidEye data indicated that many areas of damage had experienced no or very limited recovery in the period following the cyclone, with this confirmed by a rapid decline in Landsat‐derived FPC (from levels > 90% from 1986 to just prior to the cyclone to < 20% postcyclone) and no noticeable increase in subsequent years. Advanced Land Observing Satellite (ALOS‐1) Phased Arrayed L‐band Synthetic Aperture Radar (SAR) L‐band HH backscatter also increased initially and rapidly to 5 ± 2 dB (2007–2011) due to the increase in woody debris but then decreased subsequently to −20 ± 2 dB (postcyclone), as this decomposed or was removed. The lack of recovery in affected areas was attributed to the inability of mangrove species, particularly R. stylosa, to resprout from remaining plant material and persistent inundation due to a decrease in sediment elevation thereby preventing propagule establishment. This study indicates that increases in storm intensity predicted with changes in global climate may lead to a reduction in the area, diversity, and abundance of mangroves surrounding Hinchinbrook Island.

A spatio-temporal analysis of canopy dynamics and intra-stand competition in a riparian forest, south-eastern Australia

Widespread high stem density stands of riparian River Red Gum (Eucalyptus camaldulensis Dehnh.) (RRG) result from land management legacies in inland riverine ecosystems of Australia. These stands generally exhibit reduced structural complexity with many slender stems and few large trees. Higher competition for water among populations of RRG in dense stands may affect stand-level canopy condition, particularly when water is scarce. We investigated whether stem density and water availability affected canopy condition dynamics in RRG forests of Murray Valley National Park, New South Wales. We collected eight years of satellite derived Foliage Projective Cover (FPC) data (Landsat 5 TM (2008–2011) and Landsat 8 OLI (2013–2016)) encompassing some of the driest and wettest years on record in south-eastern Australia. We used Generalised Linear Mixed Effects Modelling to investigate the drivers of RRG canopy condition at the plot-level. Canopy condition dynamics were driven by different sets of variables under different climatic phases, with water availability at the plot and regional scale, as well as climate, being the primary drivers of trends in RRG canopy condition. Stem density was not found to be a significant predictor of overall RRG stand canopy condition, although results suggested that individuals in higher density stands have lower canopy condition on average. Live basal area (LBA) was positively correlated with FPC in all climatic phases, and stands with high LBA tended to have a greater abundance of large trees than low LBA stands. Research efforts should be made towards integrating site-specific tree size class distributions with remotely sensed metrics to guide management for biodiversity. Findings from this study provide a baseline understanding against which ecological restoration actions can be evaluated and a foundation upon which an ongoing monitoring program can be developed.

Relating foliage and crown projective cover in Australian tree stands

Tree cover is quantified using a variety of structural metrics that relate to canopy density, which are often modelled from remotely sensed data. Comparing different metrics, and maps of such metrics, is difficult due to a poor understanding as to how they relate to each other. Two commonly used metrics in Australia are crown projective cover (CPC) and foliage projective cover (FPC). CPC and FPC are the proportion of ground area covered by the vertical projection of tree crowns, and the foliage of tree crowns, respectively. They are dimensionless proportions that vary between zero and one. The relationship between CPC and FPC is a function of the plant area index (PAI), the foliage clumping factor at a zenith angle of zero, the foliage projection function at a zenith angle of zero, tree stand density, mean crown radii, and the proportion of wood to all canopy elements (α). The non-linear relationship was investigated using a dataset of 745 field sites across Australia, for which 1003 coincident CPC and FPC measurements had been made. As measurements of LAI and the other variables were not available, the parameter k was introduced to simplify the equations, which then had only two unknowns: α and k. Best-fit values of α and k were determined using non-linear weighted least-squares regression across all the field sites. Using these values to predict FPC from CPC, and vice versa, achieved low root mean square errors (0.05-0.07) across the field data. The models allow different mapping products to be compared, and also have the potential to facilitate the derivation of FPC from airborne lidar data when field measurements of FPC are not available for calibration. This was demonstrated using a lidar dataset and 12 coincident field sites, across which FPC was derived from a lidar fractional cover metric with an RMSE of 0.08. Further research is required to investigate the stability of this method across different areas and lidar systems.

Groundwater Depth and Soil Properties Are Associated with Variation in Vegetation of a Desert Riparian Ecosystem in an Arid Area of China

Groundwater is a major driving force for plant community distribution in arid areas worldwide. Although it is well known that groundwater has a significant impact on soil and vegetation, there is little information on how groundwater depth affects soil and vegetation in an arid inland basin desert riparian ecosystem. Therefore, quantitative analysis of the relationships among groundwater depth, soil properties and plant community distribution is necessary. A desert riparian ecosystem in the lower reaches of the Heihe River in an arid area of Northwest China was used to determine quantitative relationships among groundwater depth, soil and vegetation. Groundwater depth significantly increased with increased distance from the river. Soil and vegetation characteristics showed a significant trend with increasing groundwater depth. With increasing groundwater depth, soil water content, soil total nitrogen, soil total carbon, soil available phosphorus and soil available potassium decreased, while the soil bulk density and soil carbon:nitrogen (C:N) ratio increased. Soil pH and soil electrical conductivity followed quadratic function relationships with groundwater depth. Species richness, aboveground biomass, community coverage, community height, foliage projective cover and leaf area index all significantly decreased with increased groundwater depth. Groundwater depth and soil were associated with vegetation variance, explaining 85.8% of the vegetation variance. Groundwater depth was more important in explaining vegetation variance than soil properties (soil bulk density) and soil pH. Our observations indicate that changes in groundwater depth would have a significant influence on desert riparian forest vegetation, and that maintaining appropriate groundwater depth is necessary to preserve the riparian ecosystem.