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Biological control systems are integral to New Zealand's success as a nation reliant on exporting quality agricultural, forestry and horticultural products. The likely impacts of climate change projections to 2090 on one weed and four invertebrate management systems in differing production sectors were investigated, and it was concluded that most natural enemies will track the changing distributions of their hosts. The key climate change challenges identified were: disparities in natural enemy capability to change distribution, lack of frosts leading to emergence of new pests and additional pest generations, non-target impacts from range and temperature changes, increased disruptions caused by extreme weather events, disruption of host-natural enemy synchrony, and insufficient genetic diversity to allow evolutionary adaptation. Five classical biological control systems based on the introduced species Longitarsus jacobaeae, Cotesia kazak, Aphelinus mali, Microctonus aethiopoides and Microctonus hyperodae are discussed in more detail.

Environmental variation is a crucial driver of ecological pattern, and spatial layers representing this variation are key to understanding and predicting important ecosystem distributions and processes. A national, standardised collection of different environmental gradients has the potential to support a variety of large-scale research questions, but to date these data sets have been limited and difficult to obtain. Here we describe the New Zealand Environmental Data Stack (NZEnvDS), a comprehensive set of 72 environmental layers quantifying spatial patterns of climate, soil, topography and terrain, as well as geographical distance at 100 m resolution, covering New Zealand’s three main islands and surrounding inshore islands. NZEnvDS includes layers from the Land Environments of New Zealand (LENZ), additional layers generated for LENZ but never publicly released, and several additional layers generated more recently. We also include an analysis of correlation between variables. All final NZEnvDS layers, their original source layers, and the R-code used to generate them are available publicly for download at https://doi.org/10.7931/m6rm-vz40.

Abstract.A soil organic carbon (SOC) and SOC change model for New Zealand is developed for use in national SOC inventory reporting. The foundation for the model is a generalised least-squares regression, based on explanatory variables of land use, soil-climate class, and erosivity. The SOC change model is based on the assumption that changes in SOC over a decadal timescale are usually restricted to transitions in land use. Improvements to the model are then considered that are intended to reduce the uncertainty of SOC changes through reduction of the standard error of the land-use effects. Stochastic gradient boosting is used to find data layers most strongly associated with SOC. The most influential of these were then used in a general least-squares model after stepwise refinement. The stepwise-refined model significantly reduced the standard error for SOC, but did not result in a consistent reduction in the standard error for land-use classes, nor did it result in an improvement in the SOC change model. The method of calculating SOC change from a transition between two land-use classes is described, along with the significance of the transition, by use of a multi-comparison significance procedure.

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