Increased access to satellite imagery and new developments in remote sensing data analyses can support biodiversity conservation targets by stepping up monitoring processes at various spatial and temporal scales. More satellite imagery is becoming available as open data. Remote sensing based techniques to capitalise on the information contained in spatially-explicit species data, such as Global Biodiversity Information Facility (GBIF), are developing constantly. Current free and open data policy will have a dramatic impact on our ability to understand how biodiversity is being affected by anthropogenic pressures, while improving our ability to predict the consequences of changes at different scales.
Despite how far modelling has taken us in science, the use of models remains controversial. Modelling covers a huge range of common practices, from scaled models of ships to determine the shape that will have the least resistance to water to complex, comprehensive ‘models of everything’. A great example of the latter is the Earth System Model. This model aims to understand the changes in global climate by taking into account the interaction between physical climate, biosphere, the atmosphere and the oceans. Basically, a model of how the Earth works.
The controversy in the use of modelling resides in how accurately the model describes reality and the level of confidence we have in its outputs. The first argument can be a bit counter-intuitive: sometimes, a very simple model can be a great predictor. Actually, the conventional view in ecology is that simple models are more generalisable than complex models, although this view is being challenged. However, the level of confidence, or the level of uncertainty, that we have in the outputs of the model is a crucial point. We need to be able to accurately determine our levels of uncertainty if we want people to trust our models. Continue reading →
Understanding how biodiversity is distributed and its relationship with the environment is crucial for conservation assessment. It also helps us to predict impacts of environmental changes and design appropriate management plans. Biodiversity across a network of local sites is typically described using three components:
alpha (α) diversity, the average number of species in each specific site of the study area
beta (β) diversity, the difference in species composition between sites
gamma (γ) diversity, the total number of species in the study area.
Despite the many insights provided by the combination of alpha, beta and gamma diversity, the ability to describe species turnover has been limited by the fact that they do not consider more than two sites at a time. For more than two sites, the average beta diversity is typically used (multi-site measures have also been developed, but suffer shortcomings, including difficulties of interpretation). This makes it difficult for researchers to determine the likely environmental drivers of species turnover.
We have developed a new method that combines two pre-existing advances, zeta diversity and generalised dissimilarity modelling (both explained below). Our method allows the differences in the contributions of rare versus common species to be modelled to better understand what drives biodiversity responses to environmental gradients. Continue reading →
Today we are welcoming two new Associate Editors to Methods in Ecology and Evolution:Samantha Price (University of California, Davis, USA) and Andrés Baselga (University of Santiago de Compostela, Spain).
“My research seeks to answer the question ‘What regulates biodiversity?’. I use phylogenetic and comparative methods to investigate the abiotic and biotic drivers of global patterns of ecomorphological and lineage diversity over long periods of time and across large clades of vertebrates. To work at this macro-scale I tap the reserves of scientific data in museum collections, published literature, as well as online databases using data and techniques from across ecology, evolution, organismal biology, palaeobiology and data science. ”
“I am broadly interested in biodiversity. My background includes a PhD on beetle taxonomy. Later on I focused on biogeography and macroecology, particularly on beta diversity patterns and their underlying processes. This has led me to develop novel methods to quantify the dissimilarity between assemblages, aiming to improve our ability to infer the driving processes. With this objective, I am also interested in the integration of phylogenetic information to quantify macroecological patterns at multiple hierarchical levels (from genes to species, i.e. multi-hierarchical macroecology).”
Our understanding of how biological diversity works has been advanced by a long history of observing species and linking patterns to ecological processes. However, we generally don’t focus as much on those species that aren’t observed, or in other words ‘absent species’. But, can absent species provide valuable information?
To begin learning about dark diversity, there are two important terms that we need to define: ‘species pool’ and ‘focal community’. A ‘species pool’ is a set of species present in a particular region or landscape that can potentially inhabit a particular observed community because of suitable local ecological conditions.
A ‘focal community’ is the set of species that have been observed in a particular region or landscape (this is the ‘observed community’ and can also be referred to as alpha diversity). For a given focal community to become established, the species within it must have overcome dispersal pressures as well as environmental and biotic filters.
Happy New Year! We hope that you all had a wonderful Winter Break and that you’re ready to start 2016. We’re beginning the year with a look back at some of our highlights of 2015. Here’s how last year looked at Methods in Ecology and Evolution.
We published some amazing articles in 2015, too many to mention them all here. However, we would like to say a massive thank you to all of the authors, reviewers and editors who contributed to the journal last year. Without your hard work, knowledge and generosity, the journal would not be where it is today. We really appreciate all of your time and effort. THANK YOU!
Opportunities at the Interface between Ecology and Statistics
This month’s issue contains one Applications article and two Open Access articles, all of which are freely available.
– POPART: An integrated software package that provides a comprehensive implementation of haplotype network methods, phylogeographic visualisation tools and standard statistical tests, together with publication-ready figure production. The package also provides a platform for the implementation and distribution of new network-based methods.
Michalis Vardakis et al. provide this month’s first Open Access article. In ‘Discrete choice modelling of natal dispersal: ‘Choosing’ where to breed from a finite set of available areas‘ the authors show how the dispersal discrete choice model can be used for analysing natal dispersal data in patchy environments given that the natal and the breeding area of the disperser are observed. This model can be used for any species or system that uses some form of discrete breeding location or a certain degree of discretization can be applied.
A key property of biodiversity is that it is not evenly distributed around the world. In other words, different sites are usually home to different biological communities. Quantifying the differences among biological communities is a major step towards understanding how and why biodiversity is distributed in the way it is.
The term beta diversity was introduced by R.H. Whittaker in 1960. He defined it as “the extent of change in community composition, or degree of community differentiation, in relation to a complex-gradient of environment, or a pattern of environments”. In his original paper, Whittaker proposed several ways to quantify beta diversity. In its simplest form (which we will call strict sense or multiplicative beta diversity), beta diversity is defined as the ratio between gamma (regional) and alpha (local) diversities (Whittaker, 1960; Jost, 2007). Therefore, it is the effective number of distinct compositional units in the region (Tuomisto, 2010). Essentially, beta diversity quantifies the number of different communities in the region. So it’s clear that beta diversity does not only account for the relationship between local and regional diversity, but also informs about the degree of differentiation among biological communities. This is because alpha and gamma diversities are different if (and only if) the biological communities within the region are different.
It’s easy to demonstrate how beta diversity varies from the minimum to the maximum differentiation of local assemblages in a region. For simplicity, we will quantify biological diversity as species richness (number of species), but it’s important to remember that alpha, beta and gamma diversities can also be defined to account for richness and relative abundances (see Jost, 2007 for a detailed explanation). When local assemblages are all identical (minimum differentiation), alpha diversity equals gamma diversity, and beta diversity equals 1 (figure below).
As you may know, today (Friday 22 May) is the United Nations Day for Biodiversity and we are celebrating by highlighting some of the best papers that have been published on biodiversity in Methods in Ecology and Evolution. This is by no means an exhaustive list and you can find many more articles on similar topics on the Wiley Online Library (remember, if you are a member of the BES, you can access all Methods articles free of charge).
If you would like to learn more about the International Day for Biological Diversity, you may wish to visit the Convention on Biological Diversity website, follow them on Twitter or check out today’s hashtag: #IBD2015.
Without further ado though, here are a few of the best Methods papers on Biological Diversity:
We begin with an Open Access article from one of our Associate Editors, Douglas Yu (et al.). This article was published in the August issue of 2012 and focuses on the metabarcoding of arthropods. The authors present protocols for the extraction of ecological, taxonomic and phylogenetic information from bulk samples of arthropods. They also demonstrate that metabarcoding allows for the precise estimation of pairwise community dissimilarity (beta diversity) and within-community phylogenetic diversity (alpha diversity), despite the inevitable loss of taxonomic information.