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The Effects of Paleoclimate on the Distributions of Some North West African Lizards. Lee Ellis a Thesis Submitted in Partial

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The Effects of Paleoclimate on the Distributions of Some North West African Lizards. Lee Ellis a Thesis Submitted in Partial The effects of paleoclimate on the distributions of some North West African Lizards. Lee Ellis A thesis submitted in partial fulfilment of the requirements of Liverpool John Moores University for the degree of Master of Philosophy July 2018 1 Abstract As awareness grows regarding impacts of global climate change, so does concern over the effects these changes have on a species habitat and distribution. Climate change is thought to have a major effect on the distribution of species, with the potential to cause isolated/fragmented populations, which could lead to genetic divergence. In this study species distribution modelling was applied to species occurrence data on northwest African lizards from Morocco, with corresponding environmental data. The aim was to identify how intraspecific divergence might be related to historical climatic events. Species distribution models (SDMs) were used to quantify a species niche and define the constraining factors that affect that niche. SDMs predict areas of suitable habitat under different climatic scenarios that replicate prehistoric climates, and used to examine if there is evidence to suggest historical divergence or historical splits in distributions that correspond to current patterns of geographical divergence within species. MaxEnt was used to develop the SDMs and define the species niche and variable constraints. Previous studies have shown that the estimated divergence times of species discussed in this study range between 1–15 Ma. Environmental data dating back to these divergence times are unavailable or unreliable. Therefore, the Last Interglacial (LIG ~120,000 -140,000 years BP) and Last Glacial Maxima (LGM~ 21,000 years BP) datasets were used as a surrogate to earlier interglacial and glacial maximum climates, to analyse species distributions under earlier climatic scenarios which can then be inferred. The models produced from this study portray geographical fragmentation/isolations of suitable habitat between currently recognised subspecies for all species studied. The results from this study give insights into potential events that could cause intraspecific divergence. Given that glacial patterns occur in a cyclic manner during the Earth’s history, it is clear that they provide potential opportunities for disrupting a species habitat range and causing divergence due to oscillations between arid and humid environment. 2 Contents Abstract ......................................................................................................................................... 1 Introduction .................................................................................................................................. 5 Species Distribution Models (SDMs) ......................................................................................... 5 Constraints on a species distribution ........................................................................................ 6 Environmental ....................................................................................................................... 6 Historical ............................................................................................................................... 8 Species niche concept applied in SDMs .................................................................................... 9 Niche evolution and conservation. ......................................................................................... 10 Niche comparative methods ................................................................................................... 11 Principal component analysis ............................................................................................. 11 Niche overlap analysis ......................................................................................................... 12 Predictions through a SDM ..................................................................................................... 12 Maximum Entropy ...................................................................................................................... 15 MaxEnt .................................................................................................................................... 15 Options in MaxEnt .................................................................................................................. 16 Model Validation ..................................................................................................................... 17 Jackknife .............................................................................................................................. 18 AUC ..................................................................................................................................... 18 Pearson correlation coefficient ........................................................................................... 18 Kappa .................................................................................................................................. 19 Sensitivity and Specificity analysis ...................................................................................... 19 Null hypothesis evaluation .................................................................................................. 20 Datasets used in SDMs ............................................................................................................ 20 Environmental datasets (predictor variables) .................................................................... 20 Observational Data ............................................................................................................. 21 Quality & accuracy with species observational data .............................................................. 23 Error in species observational data .................................................................................... 23 Bias in observational data ................................................................................................... 24 Species distribution in Paleobiology ....................................................................................... 25 The use of SDM’s to predict future or ancestral distributions ................................................... 26 Paleoclimate and speciation within Africa .................................................................................. 26 Paleoclimate studies around suggested speciation times ...................................................... 29 Last Interglacial Climate. ............................................................................................................. 31 3 Last Glacial Maximum Climate .................................................................................................... 32 Climates associated between and within glacial extremes. ............................................... 33 Rationale ..................................................................................................................................... 34 Methods ...................................................................................................................................... 35 Species Studied ....................................................................................................................... 35 Observation records ............................................................................................................ 42 Climate data ............................................................................................................................ 42 Data Formats ........................................................................................................................... 43 Study Area ............................................................................................................................... 44 Modelling ................................................................................................................................ 45 Assumptions made .............................................................................................................. 45 MaxEnt options ....................................................................................................................... 46 Model Validation ..................................................................................................................... 47 Principle component Analysis (PCA) .................................................................................... 48 Niche overlap ...................................................................................................................... 49 Null hypothesis.................................................................................................................... 49 Sensitivity and specificity analysis ...................................................................................... 50 Results ......................................................................................................................................... 51 Jack knife ................................................................................................................................. 51 Null hypothesis Evaluation ...................................................................................................... 53 Sensitivity and Specificity Analysis .........................................................................................
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