Spooner, Fiona Elizabeth Bridget; (2019) Predicting Population Trends Under Environmental Change: Comparing Methods Against Observed Data. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

In this thesis I examine a range of approaches for predicting the impact of recent climate change and land use change on observed population trends. The thesis is split into three main parts. Firstly, I used linear mixed effects models to provide the first global assessment of the effects of environmental change on bird and mammal population trends. I find that populations have declined more rapidly in areas which have experienced rapid warming, this effect was more pronounced in bird populations. // Secondly, I built habitat suitability models for 16 mammal species to explore the relationship between predicted habitat suitability and population abundance. I explored the correlations between time-series of rates of change in habitat suitability and corresponding time-series of observed population growth rates. There was little evidence to support the idea that population growth rates are directly linked to habitat suitability. However, when lagged responses are considered there is a stronger positive relationship between changes in habitat suitability and population growth rates, highlighting the importance of biodiversity time-series. // Lastly, I built coupled niche-demographic (CND) models for three mammal species: Alpine ibex, brown bear and red deer. These are habitat suitability models linked with population models with dispersal mechanisms; they can be used to predict species abundance trends. CND models have been assumed have greater predictive accuracy than habitat suitability models, but there has been limited validation of CND model predictions against observed data. I found that CND models are an improvement on habitat suitability models. However, both sets of models perform relatively poorly. Simpler linear mixed effects models were able to provide more accurate estimates of average population growth. This suggests that the high data requirements and computational resources needed to run CND models may be excessive, as currently, more parsimonious models provide better predictions of population growth rates.

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