Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

https://www.cs.princeton.edu/~schapire/papers/ecolmod.pdf

Maximum entropy modeling of species geographic distributions

Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil's well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.

High-Resolution Global Maps of 21st-Century Forest Cover Change

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.

/pdf/novel-methods-improve-prediction-of-species-distributions-3cmioqeu6v.pdf

Novel methods improve prediction of species' distributions from occurrence data

In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory.

Predicting species distribution: offering more than simple habitat models.

. Variation of biodiversity over 72 600 km2 of tropical rain forest was mapped by multiple characteristics using a new combined nominal–numerical classification system. Three hundred and sixty-nine tree genera were grouped into classes by eight taxonomic, geographical, phylogenetic and ecological characteristics. Each grid cell was assigned to a nominal biodiversity type by its dominant classes and classified numerically by its share of total variation in study area biodiversity. Differentiation of 0.5° cells by nominal classification was inferior to numerical classification, but improved at higher resolution. The nominal and numerical classifications were shown to have potential for complementary roles in conservation planning.

Multivariate mapping of spatial variation in biodiversity in Peruvian Amazonia

De la Cruz et al . question the reliability of our results, claiming that we do not refer to the most appropriate spatial extent of drylands. In our response, we explain why we chose an existing and internationally recognized delineation of drylands among several options, and why our findings are due to a difference of remote sensing technique and not to the definition of drylands we have selected.

https://science.sciencemag.org/content/sci/358/6365/881.full.pdf

Response to comment on "the extent of forest in dryland biomes"

The magnitude of net carbon dioxide emissions resulting from global forest carbon change, and hence the contribution of forests to global climate change, is highly uncertain, owing to the lack of direct measurement by Earth observation and ground data collection. This paper uses a new method to evaluate this uncertainty with greater precision than before. Sources of uncertainty are divided into conceptualization and measurement categories and distributed between the spatial, vertical and temporal dimensions of Earth observation. The method is applied to Forest Reference Emission Level (FREL) reports and National Greenhouse Gas Inventories (NGGIs) submitted to the UN Framework Convention on Climate Change (UNFCCC) by 12 countries containing half of tropical forest area. The two sets of estimates are typical of those to be submitted to the Reducing Emissions from Deforestation and Degradation (REDD+) mechanism of the UNFCCC and the 2023 Global Stocktake of its Paris Agreement, respectively. Assembling the Uncertainty Fingerprint of each estimate shows that Uncertainty Scores are between 10 and 14 for the NGGIs and 5 and 10 for the FREL reports, and so both exceed the threshold of 2 when it is advisable to evaluate uncertainty by standard statistical methods. Conceptualization uncertainties account for 60% of all uncertainties in the NGGIs and 47% in the FREL reports, e.g., there is incomplete coverage of forest carbon fluxes, and limited disaggregation of fluxes between different ecosystem types and forest carbon pools. Of the measurement uncertainties, all FREL reports base forest area estimates on at least medium resolution satellite data, compared with only 3 NGGIs; after REDD+ Readiness schemes, mean area mapping frequency has fallen to 2.3 years in Latin America and 3.0 years in Asia, but only 8.3 years in Africa; and carbon density estimates are based on national forest inventory data in all FREL reports but only 4 NGGIs. The effectiveness of the Global Stocktake and REDD+ monitoring will therefore be constrained by considerable uncertainties, and to reduce these requires a new phase of REDD+ Readiness to ensure more frequent national forest inventories and forest carbon mapping.

https://www.mdpi.com/2072-4292/12/15/2369/pdf

Reducing Global Environmental Uncertainties in Reports of Tropical Forest Carbon Fluxes to REDD+ and the Paris Agreement Global Stocktake

Models of global environmental change need to integrate its socio-economic and physical environmental dimensions much better than they do at present. The traditional separation between the social and environmental sciences has hindered development of interdisciplinary methodologies and led to predominantly physical assessments of key problem areas, such as the potential to mitigate global climate change by sequestering more terrestrial carbon (C) in woody biomass.

Integrating the socio-economic and physical dimensions of degraded tropical lands in global climate change mitigation assessments

SUMMARY Trees have not received as much attention as herbaceous plants as far as cultivation is concerned and there are many under-utilised trees with considerable potential for cultivation in fragile tropical and mediterranean ecosystems particularly. Various multiple land use systems are suggested as a means of resolving the traditional conflict between agriculture and forestry and of increasing production of both wood and food in a sustainable manner. Most of these systems come under the heading of agroforestry. Current difficulties in the nomenclature of agroforestry systems are mentioned and a functional classification is given. Trees used in afforestation schemes are increasingly chosen according to the multiplicity of crops and functions obtainable from them, e.g. food, fodder and fuel. Similarly any crop may be utilised at a number of levels according to need, available capital and available technology, e.g. carob pods may be processed into fodder, chocolate, alcohol, or fungal protein. Tropical d...

Alan Grainger

Papers

Multivariate mapping of spatial variation in biodiversity in Peruvian Amazonia

Response to comment on "the extent of forest in dryland biomes"

Reducing Global Environmental Uncertainties in Reports of Tropical Forest Carbon Fluxes to REDD+ and the Paris Agreement Global Stocktake

Integrating the socio-economic and physical dimensions of degraded tropical lands in global climate change mitigation assessments

The development of tree crops and agroforestry systems