James D. Shepherd’s research while affiliated with Manaaki Whenua - Landcare Research and other places

Aim Woody ecosystems provide critical ecosystem functions and services but are increasingly threatened as invasive pathogens spread globally. Myrtle rust, caused by Austropuccinia psidii, arrived in New Zealand in 2017 and infects at least 12 of 18 species in the susceptible Myrtaceae plant family. Among these are species of structural, successional and cultural importance. We aim to assess whether the functional consequences of Myrtaceae loss could be mitigated if co‐occurring species with shared functional attributes are able to replace them. Location New Zealand (but with concepts and methodologies that apply globally). Methods Using a nationwide forest and shrubland plot data set, we assessed community vulnerability to the loss of Myrtaceae species by analysing proportional changes in average trait values when they are absent and produced spatial predictions indicating where species loss might have the greatest impact on community functionality. We then assessed whether compensatory infilling by co‐occurring species would mediate community vulnerability. Results Forests and shrublands containing Kunzea ericoides and Leptospermum scoparium are highly vulnerable to their loss. Areas most vulnerable overall are the central and south‐eastern North Island, north‐eastern South Island and Stewart Island. For all species, compensatory infilling moderated the impact of their loss. However, if co‐occurring Myrtaceae were unable to respond, possibly if they were also infected, community vulnerability almost always increased because infilling species had different functional attributes, compounding the functional impact. Main Conclusions Early successional woody plant communities and Myrtaceae‐dominated old‐growth forests are at most risk. Our spatial assessment of species‐level functional impacts from myrtle rust will facilitate better‐informed landscape‐level responses. Management actions and monitoring can now be targeted to areas and communities at greatest risk of losing ecosystem‐level processes.

Remote sensing was used to map the invasion of yellow-flowered legumes on the Central Plateau of New Zealand to inform weed management strategy. The distributions of Cytisus scoparius (broom), Ulex europaeus (gorse) and Lupinus arboreus (tree lupin) were captured with high-resolution RGB photographs of the plants while flowering. The outcomes of herbicide operations to control C. scoparius and U. europaeus over time were also assessed through repeat photography and change mapping. A grid-square sampling tool previously developed by Manaaki Whenua—Landcare Research was used to help transfer data rapidly from photography to maps using manual classification. Artificial intelligence was trialled and ruled out because the number of false positives could not be tolerated. Future actions to protect the natural values and vistas of the Central Plateau from legume invasion were identified. While previous control operations have mostly targeted large, highly visible legume patches, the importance of removing outlying plants to prevent the establishment of new seed banks and slow spread has been underestimated. Outliers not only establish new, large, long-lived seed banks in previously seed-free areas, but they also contribute more to range expansion than larger patches. Our C. scoparius and U. europaeus change mapping confirms and helps to visualise the establishment and expansion of uncontrolled outliers. The power of visualizing weed control strategies through remote sensing has supported recommendations to improve outlier control to achieve long-term, sustainable landscape-scale suppression of invasive legumes.

Chapter 2: Baseline prevalence study of Phytophthora agathidicida and kauri dieback in the Waitakere Ranges and frequency of potential risk factors using a cross-sectional study. In: 2021 Waitakere Ranges Kauri Population Health Monitoring Survey, Technical Report 2022/8, Auckland Council, Auckland, New Zealand, 2022

The southern beech (genus Fuscospora and Lophozonia) forest in New Zealand periodically has “mast” years, during which very large volumes of seeds are produced. This excessive seed production results in a population explosion of rodents and mustelids, which then puts pressure on native birds. To protect the birds, extra pest controls, costing in the order of NZD 20 million, are required in masting areas. To plan pest control and keep it cost-effective, it would be helpful to have a map of the masting areas. In this study, we developed a remote sensing method for the creation of a national beech flowering map. It used a temporal sequence of Sentinel-2 satellite imagery to determine areas in which a yellow index, which was based on red and green reflectance (red-green)/(red + green), was higher than normal in spring. The method was used to produce national maps of heavy beech flowering for the years 2017 to 2021. In 2018, which was a major beech masting year, of the 4.1 million ha of beech forest in New Zealand, 27.6% was observed to flower heavily. The overall classification accuracy of the map was 90.8%. The method is fully automated and could be used to help to identify areas of potentially excessive seed fall across the whole of New Zealand, several months in advance of when pest control would be required.

Characterisation of native forests is essential for sustainable forest management and for maintenance of ecological and socio-economical functions. In New Zealand, knowledge of forest composition and extent informs predator control measures to protect native bird life, particularly in forests dominated by Southern beeches (Nothofagaceae). As high-resolution (> 1:50,000) maps of beech cover do not exist at national scale, we present a method to identify and map beech cover that combines multi-temporal spectral signatures from ESA’s Sentinel-2 satellite with forest plot survey data. A temporal stack of satellite imagery from 2016 to 2019 is used to derive annual metrics (mean and standard deviation) of vegetation indices which are used as input to a pixel-wise classification. A random forest classification, discriminating between beech/non-beech areas (with a beech relative cover threshold of 25%), and trained using 880 forest plots from the Land Use and Carbon Analysis System (LUCAS) natural forest network, achieved an accuracy of 87.7% (± 2.2%). This spectral classification captures both large- and local-scale spatial patterns of beech cover, which is confirmed by field visits and multi-source species occurrence information.

Removing vegetation cover from hill-slope land increases risk for soil erosion and delivery of sediment to waterways. In New Zealand’s productive landscapes, clear-fell harvesting of forestry blocks and winter forage grazing by agricultural livestock are two significant causes of vegetation removal. Bare ground exposed by these activities varies annually and seasonally in location and spatial extent. Modelling soil erosion therefore requires temporally and spatially explicit mapping of this bare ground. We have developed an automated mapping method using time-series satellite imagery, thereby enabling wide-area coverage and ease of updating. The temporal analysis identifies land use along with the period of vegetation removal. It produces results per land parcel (in vector format) for use in a Geographic Information System. We present a description of our method, national maps and statistics of bare ground extent in New Zealand’s hill-country forestry and winter forage grazing land in 2018, and an assessment of accuracy. The attributes of the mapped land parcels are designed for input into a soil erosion estimation model such as the New Zealand Universal Soil Loss Equation.

A national map of pasture productivity, in terms of mass of dry matter yield per unit area and time, enables evaluation of regional and local land-use suitability. Difficulty in measuring this quantity at scale directed this research, which utilises four years of Sentinel-2 satellite imagery and collected pasture yield measurements to develop a model of pasture productivity. The model uses a Normalised Difference Vegetation Index (NDVI), with spatio-temporal segmentation and averaging, to estimate mean annual pasture productivity across all of New Zealand’s grasslands with a standard error of prediction of 2.2 t/ha/y. Regional aggregates of pasture yield demonstrate expected spatial variations. The pasture productivity map may be used to classify grasslands objectively into stratified levels of production on a national scale. Due to its ability to highlight areas of land use intensification suitability, the national map of pasture productivity is of value to landowners, land users, and environmental scientists.