A New Modelling Strategy for Conservation Practice? Ensembles of Small Models (ESMS) for Modelling Rare Species

Post provided by FRANK BREINER, ARIEL BERGAMINI, MICHAEL NOBIS and ANTOINE GUISAN

Rare Species and their Protection

Erythronium dens-canis L. – a rare and threatened species used for modelling in Switzerland. ©Michael Nobis

Erythronium dens-canis L. – a rare and threatened species used for modelling in Switzerland. ©Michael Nobis

Rare species can be important for ecosystem functioning and there is also a high intrinsic interest to protect them as they are often the most original and unique components of local biodiversity. However, rare species are usually those most threatened with extinction.

In order to help prioritizing conservation efforts, the International Union for Conservation of Nature (IUCN) has published criteria to categorize the status of threatened species, which are then published in Red Lists. Changes in a species’ geographical distribution is one of the several criteria used to assign a threat status. For rare species, however, the exact distribution is often inadequately known. In conservation science, Species Distribution Models (SDMs) have recurrently been used to estimate the potential distribution of rare or insufficiently sampled species. Continue reading

What is Dark Diversity?

Post provided by ROB LEWIS & MEELIS PÄRTEL

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?

Dark diversity – a set of species absent from a particular site but which belong to its species pool – has the potential to be as ecologically meaningful as observed diversity. Part of the species pool concept, understanding dark diversity is relatively straightforward.

The Basic Theory of Dark Diversity

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.

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Biggest Library of Bat Sounds Compiled

Below is a press release about the Methods paper ‘Acoustic identification of Mexican bats based on taxonomic and ecological constraints on call design‘ taken from the University College London.

The Funnel-eared bat (Natalus stramineus)

The Funnel-eared bat (Natalus stramineus)

The biggest library of bat sounds has been compiled to detect bats in Mexico – a country which harbours many of the Earth’s species and has one of the highest rates of species extinction and habitat loss.

An international team led by scientists from UCL, University of Cambridge and the Zoological Society of London, developed the reference call library and a new way of classifying calls to accurately and quickly identify and differentiate between bat species.

It is the first time automatic classification for bat calls has been attempted for a large variety of species, most of them previously noted as hard to identify acoustically. Continue reading

A Model Approach to Weed Management

Post provided by VANESSA ADAMS

Vanessa Adams in the field with gamba grass in the Batchelor region, NT. ©Amy Kimber (NERP Northern Australia Hub)

Vanessa Adams in the field with gamba grass in the Batchelor region, NT.
©Amy Kimber (NERP Northern Australia Hub)

Invasive weeds cause environmental and economic harm around the world. Land managers bear a heavy responsibility for the control of infestations in what is often a time-consuming and costly battle.

Fortunately, an increasing number of research-based solutions are giving land managers an advantage. This includes tools to determine the distribution of weeds and also the development of modelling approaches to predict their spread.

Understanding the current and future distribution of an invasive species allows managers to better direct their limited resources. However, the direct and strategic management of weeds is tricky and that’s why population models (in particular spatial dispersal models that can be applied without much data) are needed to inform and facilitate action on the ground. Continue reading

Inferring Extinction: When is a Species as Dead as a Dodo?

Post provided by ELIZABETH BOAKES

The indisputably extinct Dodo (Raphus cucullatus). ©Ballista

The indisputably extinct Dodo (Raphus cucullatus). ©Ballista

A species is either extant or extinct – it exists or it does not exist. Black and white, a binary choice. Surely it should not be difficult to assign species to one of these two categories? Well, in practice it can be extremely challenging and a plethora of methods have been developed to deal with the problem.  This of course leads to a second challenge – which of the plethora should you use?! (More on this later…)

There are a few well-studied cases where we can assert extinction confidently. For example, the chances of the Dodo (Raphus cucullatus) having existed undetected for upwards of 300 years on an island now densely populated by humans are infinitesimally small. However, many extinctions are far harder to diagnose. Species typically become extremely rare before becoming extinct. If taxa are particularly cryptic or are found across a huge geographic range it is quite plausible that the few remaining individuals may exist undetected for decades. An extreme illustration of this is the 1938 discovery of Latimeria chalumnae, a deep-sea member of the Coelacanths, the entire order of which was believed to have become extinct 80 million years earlier! Continue reading

Revealing Biodiversity on Rocky Reefs using Natural Soundscapes

Post Provided by SYDNEY HARRIS

The Biodiversity Struggle

Typical rocky reef habitat in northeast New Zealand, characterized by encrusting red algae and Kelp forest. ©Sydney Harris

Typical rocky reef habitat in north east New Zealand, characterized by encrusting red algae and Kelp forest. ©Sydney Harris

By now we’re all familiar with the global biodiversity crisis: increasing numbers of species extinct or at risk of extinction; widespread habitat loss and a seemingly endless set of political, logistical and financial obstacles hampering swift action for conservation. The international Convention on Biological Diversity (CBD) has set twenty global diversity targets, many of which require participating nations to conduct accurate and efficient monitoring to assess their progress and inform policy decisions. Governing bodies and organizations worldwide have agreed that immediate, efficient action is essential to preserving our planet’s increasingly threatened ecosystems.

But how? Diversity measurement techniques are a tricky business. Accurately recording diversity can be time-consuming, labor-intensive, expensive, invasive and highly susceptible to human error. Often these methods involve the employment of trained specialists to individually identify hundreds or even thousands of species, a process that can take many months to complete.

Marine habitats are particularly difficult to access because of the physical limitations of humans underwater, and are often flawed due to the influence of our presence on marine organisms. However, the oceans contain many of the world’s most diverse systems, and, despite the limitations of current methods, the need to monitor marine diversity is a top priority for the global conservation movement. Continue reading

Demography and Big Data

Post provided by BRITTANY TELLER, KRISTIN HULVEY and ELISE GORNISH

Follow Brittany (@BRITTZINATOR) and Elise (@RESTORECAL) on Twitter

To understand how species survive in nature, demographers pair field-collected life history data on survival, growth and reproduction with statistical inference. Demographic approaches have significantly contributed to our understanding of population biology, invasive species dynamics, community ecology, evolutionary biology and much more.

As ecologists begin to ask questions about demography at broader spatial and temporal scales and collect data at higher resolutions, demographic analyses and new statistical methods are likely to shed even more light on important ecological mechanisms.

Population Processes

Midsummer Opuntia cactus in eastern Idaho, USA. © B. Teller.

Midsummer Opuntia cactus in eastern Idaho, USA. © B. Teller.

Traditionally, demographers collect life history data on species in the field under one or more environmental conditions. This approach has significantly improved our understanding of basic biological processes. For example, rosette size is a significant predictor of survival for plants like wild teasel (Werner 1975 – links to all articles are at the end of the post), and desert annual plants hedge their bets against poor years by optimizing germination strategies (Gremer & Venable 2014).

Demographers also include temporal and spatial variability in their models to help make realistic predictions of population dynamics. We now know that temporal variability in carrying capacity dramatically improves population growth rates for perennial grasses and provides a better fit to data than models with varying growth rates because of this (Fowler & Pease 2010). Moreover, spatial heterogeneity and environmental stochasticity have similar consequences for plant populations (Crone 2016). Continue reading

My Entropy ‘Pearl’: Using Turing’s Insight to Find an Optimal Estimator for Shannon Entropy

Post provided by Anne Chao (National Tsing Hua University, Taiwan)

Shannon Entropy

Not quite as precious as my entropy pearl

Not quite as precious as my entropy pearl ©Amboo Who

Ludwig Boltzmann (1844-1906) introduced the modern formula for entropy in statistical mechanics in 1870s. Since its generalization by Claude E. Shannon in his pioneering 1948 paper A Mathematical Theory of Communication, this entropy became known as ‘Shannon entropy’.

Shannon entropy and its exponential have been extensively used to characterize uncertainty, diversity and information-related quantities in ecology, genetics, information theory, computer science and many other fields. Its mathematical expression is given in the figure below.

In the 1950s Shannon entropy was adopted by ecologists as a diversity measure. It’s interpreted as a measure of the uncertainty in the species identity of an individual randomly selected from a community. A higher degree of uncertainty means greater diversity in the community.

Unlike species richness which gives equal weight to all species, or the Gini-Simpson index that gives more weight to individuals of abundant species, Shannon entropy and its exponential (“the effective number of common species” or diversity of order one) are the only standard frequency-sensitive complexity measures that weigh species in proportion to their population abundances. To put it simply: it treats all individuals equally. This is the most natural weighing for many applications. Continue reading

Issue 7.1

Issue 7.1 is now online!

The January issue of Methods is now online!

As always, the first issue of the year is our sample issue. You can access all of the articles online free of charge. No subscription or membership is required!

We have two Open Access articles and two Applications papers in our January issue.

Recognizing False Positives: Environmental DNA (eDNA) is increasingly used for surveillance and detection of species of interest in aquatic and soil samples. A significant risk associated with eDNA methods is potential false-positive results due to laboratory contamination. To minimize and quantify this risk, Chris Wilson et al. designed and validated a set of synthetic oligonucleotides for use as species-specific positive PCR controls for several high-profile aquatic invasive species.

BiMat: An open-source MATLAB package for the study of the structure of bipartite ecological networks. BiMat enables both multiscale analysis of the structure of a bipartite ecological network – spanning global (i.e. entire network) to local (i.e. module-level) scales – and meta-analyses of many bipartite networks simultaneously. The authors have chosen to make this Applications article Open Access.

Gemma Murray et al. provide this month’s second Open Access article. In ‘The effect of genetic structure on molecular dating and tests for temporal signal‘ the authors use simulated data to investigate the performance of several tests of temporal signal, including some recently suggested modifications. The article shows that all of the standard tests of temporal signal are seriously misleading for data where temporal and genetic structures are confounded (i.e. where closely related sequences are more likely to have been sampled at similar times). This is not an artifact of genetic structure or tree shape per se, and can arise even when sequences have measurably evolved during the sampling period.

Our January issue also features articles on Monitoring, Population Ecology, Genetics, Evolution, Community Ecology, Diversity and more. Continue reading

Introducing Biodiverse: Phylodiversity Made Easy

Post provided by SHAWN LAFFAN and ANDREW THORNHILL

© Shawn Laffan

© Shawn Laffan

Phylodiversity indices are increasingly used in spatial analyses of biodiversity, driven largely by the increased availability of phylogenetic trees and the tools to analyse them. Such analyses are integral to understanding evolutionary history and deciding where to allocate conservation resources.

Phylogenetic Indices: The Current Favourites

The most commonly used phylogenetic index is Faith’s Phylogenetic Diversity (PD; Faith 1992). PD is the phylogenetic analogue of taxon richness and is expressed as the number of tree units which are found in a sample.

More recently developed phylodiversity indices adapt the calculation of PD by adjusting the branch lengths of a sample using the local lineage range sizes and abundances, for example Phylogenetic Endemism (PE) and Abundance weighted Evolutionary Diversity (AEDt). In PE the length of each branch in a sample is multiplied by the fraction of its total geographic range found in that sample. The AEDt index uses the same general approach, but weights each branch by the fraction of total abundances found in the sample. The weighting process is generic, so one can scale the branch lengths by any relevant factor, for example the threat status (Faith 2015). Continue reading