Spatial Modelling, Phytogeography and Conservation in the Eastern Arc Mountains of Tanzania ...

243 Pages·2012·30.2 MB·English

by  Philip Platts

Spatial Modelling, Phytogeography and Conservation
in the Eastern Arc Mountains of Tanzania and Kenya

Philip John Platts
PhD

THE UNIVERSITY OF YORK
York Institute for Tropical Ecosystem Dynamics
Environment Department

March 2012

This thesis is dedicated to my father
Philip Graham Platts

27th November 1937 – 11th February 2012

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Thesis Abstract

Forests in the Eastern Arc Mountains are amongst the oldest and most biodiverse on Earth.
They are a global priority for conservation and provide ecosystem services to millions of
people. This thesis explores how spatial modelling can provide direction for conservation
and botanical survey, and contribute to understanding of phytogeographical relationships.
The ecoregion is rigorously defined by terrain complexity, vegetation distribution and
established geoclimatic divisions, providing a coherent platform upon which to collate and
monitor biological and socioeconomic information. Accordingly, 570 vascular plant taxa
(species, subspecies and varieties) are found to be strictly endemic. The human population
exceeds two million, with median density more than double the Tanzania average.
Population pressure (accrued across the landscape) is shown to be greatest adjacent to the
most floristically unique forests. Current knowledge on species distributions is subject to
sampling bias, but could be systematically improved by iterative application of the
bioclimatic models presented here, combined with targeted fieldwork. Tree data account for
80% of botanical records, but only 18% of endemic plant species; since conservation
priorities differ by plant growth form, future fieldwork should aim to redress the balance.
Concentrations of rare species correlate most strongly with moisture availability, whilst
overall richness is better predicted by temperature gradients. Climate change impacts are
projected to be highly variable, both across space and between species. Concordant with the
theory that past climatic stability facilitated the accumulation of rare species, contemporary
climates at sites of known endemic richness are least likely to be lost from dispersal-limiting
mountain blocs during the 21st century. Faced with rapid population growth and the
uncertainty of climate change, priorities for governance are to facilitate sustainable forest
use and to maintain/restore habitat connectivity wherever possible. Overall, the thesis
demonstrates that decision makers concerned with biodiversity conservation, particularly in
mountain and coastal regions, should be wary of inferring local patterns of change from
broad-scale models. The current study is a step toward spatially refined understanding of
conservation priorities in the Eastern Arc Mountains, whilst novel methodologies have wider
application in the fields of species distribution modelling and mountain analysis.
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Contents

Thesis Abstract
Contents
List of Tables
List of Figures
Acknowledgements
Author’s Declaration

1. General Introduction 1
Overview 3
Forest degradation and loss 4
Conservation prioritisation 5
Species distribution modelling 6
Describing the response 8
Prediction 10
Thesis aims and objectives 11
Study region 13
Geology and climate 13
Biological importance 14
Delineation and classification 15
Forest use and protection 16
Data collection 18
Outline of analytical chapters 19
Chapter 2 – Mountain limits 19
Chapter 3 – Predicting tree distributions 20
Chapter 4 – Distribution models and conservation priority 20
Chapter 5 – Climate change 20
References 22

2. Mountains Limits 27
Article title, authors and abstract 29
Introduction 20
A qualitative definition for the Eastern Arc 31
Mountain limits 32
A global mountain typology 32
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Methods 33
Regional refinements to the global typology 33
Elevational zonation 33
Terrain parameters 34
Matching to mountain vegetation 34
Bounding the chosen regional typology 35
Amalgamating features 35
Mountain selection based on relative relief 36
Results 36
Sensitivity to local elevation range 36
Boundary placement 39
Protected areas 42
Human populations 44
Discussion 45
Acknowledgements 49
Author contributions 49
Appendix 2A: Description of spatial data 50
Appendix 2B: Mountain classes in the regional typology 51
Appendix 2C: Derivation of the population surface 52
References 54

3. Predicting Tree Distributions 57
Article title, authors and abstract 59
Introduction 60
Model selection 61
Data bias 62
Envelope uncertainty 63
Methods 63
Study region 63
Tree data 65
Environmental predictor variables 65
Statistics for evaluation and calibration 68
Modelling experiments 69
Selecting predictors 69
Weighting absences 70
Spatial autocovariates 73
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Results 73
Baseline models 73
Weighted models 74
Spatial models 76
Predictors and envelope uncertainty 78
Discussion 79
Conclusions 83
Acknowledgements 84
Author contributions 84
Appendix 3A: Tree species modelled 85
Appendix 3B: Stratified cross-validation 86
Appendix 3C: Neighbourhood size for spatial autocovariates 87
Appendix 3D: Species-specific results 88
References 97

4. Distribution Models and Conservation Priority 101
Article title, authors and abstract 103
Introduction 104
Methods 106
Study region 106
Plant inventory data 107
Environmental data 107
Model calibration 110
Background data 110
Predictor selection 110
Spatial autocorrelation 111
Testing and validation 111
Richness estimates 112
Results 112
Model performance 112
Sampling bias 114
Richness 115
Confirmed at bloc level 115
Predictive estimates 116
Growth form 116
Discussion 121
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Conclusions 125
Acknowledgements 125
Author contributions 126
Appendix 4A: Details of plant data 127
Appendix 4B: Occurrence thresholds and sensitivity to prevalence 128
Appendix 4C: Analysis of model performance 129
Appendix 4D: Patch occupancy for endemic/threatened taxa 132
Appendix 4E: Drivers of richness and endemism 133
References 135

5. Climate Change 139
Article title, authors and abstract 141
Introduction 142
Methods 145
Study region 145
Plant data 145
Climate data 146
Climates lost and gained 147
Endemism vs. altitude 148
Univariate response 148
Multivariate response 149
Results 149
Spatial variation in change 149
Climates lost and gained 151
Endemism vs. altitude 153
Univariate response 153
Multivariate response 154
Discussion 155
Acknowledgements 162
Author contributions 163
Appendix 5A: Improving normality for statistical regression 164
Appendix 5B: 21st century climate anomalies 167
Appendix 5C: Climates lost and gained 171
Appendix 5D: Multivariate species models 174
References 182
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6. Summary Discussion 185
Mountain limits 187
Endemism criteria 187
Human populations 189
Methods for species distribution modelling 191
Sample selection 191
Data weights 193
Variable selection 193
Environmental relationships 194
Socioeconomic relationships 197
Directions for fieldwork 199
Plants in context 201
References 204

Thesis Appendix 209
Appendix I: Co-authored paper abstracts 209
Appendix II: Population projections for Tanzania 217
Appendix III: Hydrological modelling 220
References 224
Abbreviations 225