Climatic limitation of alien weeds in New Zealand: enhancing species distribution models with field data

Abstract

Correlative species distribution models (SDMs) are often used to quantify the potential ranges of alien species. Despite rising popularity, there is ongoing debate surrounding whether SDMs can predict non-equilibrium species, how well they capture underlying biological mechanisms versus drawing spurious correlations, and how realistic the ensuing projections are. There have been numerous calls to integrate SDMs with real-world performance data to validate and improve projections, but such studies remain rare. In this thesis, I investigated the potential distributions of three alien plant species, Aeonium arboreum, A. haworthii and Cotyledon orbiculata, in their introduced ranges of New Zealand. I used a combination of SDMs, observational and experimental approaches. I firstly developed correlative SDMs for the three species. Secondly, I quantified the species’ climatic limits in the study region of Banks Peninsula, New Zealand, using field transplant experiments and surveys. Finally, I combined the aforementioned plant performance data into a single climate-driven population model, which I used to test and enhance the original SDM projections. I found that the New Zealand distributions of all three species are climatically novel relative to their distributions elsewhere, and constitute shifts in their realized niches. Although SDMs indicated that much of New Zealand is climatically suitable, transplant experiments on Banks Peninsula confirmed that the climate of Banks Peninsula is limiting. In all three species, low growth rates, low germination, and high mortality at high elevations will limit spread. In contrast, surveys found little evidence of direct climatic limitation to fecundity within the species’ current distributions on Banks Peninsula. The final step of validating SDM projections against the population model revealed that the SDM performed better than k-folds cross-validation against occurrence data would suggest. However, the SDMs over-predicted suitable climate in the region. I therefore adjusted SDM thresholds of modelled suitability to optimise parsimony with field data and provide more robust projections for Banks Peninsula. This is the first thorough study of climatic limitation of the target species. It is also one of few to experimentally test SDMs and use field performance data to enhance projections. Although I found support for the usefulness of SDMs, the results emphasise the need for scepticism and rigorous testing of outputs. Validating SDMs against field data was highly effective, and was a better test of model performance than conventional methods using occurrence data. Uptake of similar methods as outlined in this thesis would improve understanding of uncertainty in distribution modelling. I encourage the use of such techniques not only for improving confidence in model projections, but also in recognizing the relative impact of sources of error in our models. At a time when generating projections of species’ potential distributions has never been easier, the need for considered judgements in SDM building and cautious interpretation of outputs is emphasised by my findings.