Conditional Occupancy Design Explained

Occupancy surveys are widely used in ecology to study wildlife and plant habitat use. To account for imperfect detection probability many researchers use occupancy models. But occupancy probability estimates for rare species tend to be biased because we’re unlikely to observe the animals at all and as a result, the data aren’t very informative.

In their new article – ‘Occupancy surveys with conditional replicates: An alternative sampling design for rare species‘ – Specht et al. developed a new “conditional” occupancy survey design to improve occupancy estimates for rare species, They also compare it to standard and removal occupancy study designs. In this video two of the authors, Hannah Specht and Henry Reich, explain how their new conditional occupancy survey design works. 

This video is based on the article ‘Occupancy surveys with conditional replicates: An alternative sampling design for rare species‘ by Specht et al.


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


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

Spatially-explicit Power Analysis: A First Step for Occupancy-Based Monitoring

Post provided by Martha Ellis and Jody Tucker

Where’s Waldo? Trying to find this fisher somewhere in a giant landscape is going to be tricky! ©Mike Schwartz

Where’s Waldo? Trying to find this little guy somewhere in a giant landscape is going to be tricky! © Mike Schwartz

The seemingly basic question of whether a population is increasing, decreasing, or stable can be one of the most difficult to answer. Collecting data on rare and elusive species is hard. Imagine trying to detect a handful of fisher or wolverine across hundreds of thousands of acres – it is physically demanding, time consuming and logistically complicated. And that’s just to do it once! To monitor a population for changes, you have to repeat these surveys regularly over many years. The long-term monitoring that is necessary for conservation requires careful planning and a substantial commitment of resources and funding. So before we spend these valuable resources, it’s critical to know whether the data we are collecting can help us to answer our questions. Continue reading

Issue 7.3

Issue 7.3 is now online!

The March issue of Methods is now online!

This month’s issue contains two Applications articles and two Open Access articles, all of which are freely available.

METAGEAR: A comprehensive, multifunctional toolbox with capabilities aimed to cover much of the research synthesis taxonomy: from applying a systematic review approach to objectively assemble and screen the literature, to extracting data from studies, and to finally summarize and analyse these data with the statistics of meta-analysis.

Universal FQA Calculator: A free, open-source web-based Floristic Quality Assessment (FQA) Calculator. The calculator offers 30 FQA data bases (with more being added regularly) from across the United States and Canada and has been used to calculate thousands of assessments. Its growing repository for site inventory and transect data is accessible via a REST API and represents a valuable resource for data on the occurrence and abundance of plant species. 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

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

From Star Trek to Species Ranks in Space… and Beyond

Post provided by Leonardo Saravia

Algae, Space Travel and Jungles

One of my main areas of study is Periphyton developed in microcosms. For those of you who don’t know, Periphyton is a green biofilm that you may notice in some (not very clean) swimming pools and is composed mainly of algae, bacteria, fungi, meiofauna and detritus. I started studying Periphyton because my Masters thesis involved developing a model in freshwater systems and after that I wanted to look into their spatial distribution.

©Hubble Heritage

©Hubble Heritage

I wanted to find an opportunity to connect my study system with two of my passions: space travel (I used to watch Star Trek and also I thoroughly enjoyed Space: The Final Frontier for Ecological Theory by Peter Kareiva) and tropical rainforests (which I developed a fondness for while watching Tarzan). I thought I could use Periphyton as a model system to test ecological theory, with a complexity similar to tropical forest as suggested by Lowe [1].

The study of the spatial structure of Periphyton was not as easy as space travel in Star Trek (for one thing they have a warp drive and I don’t!). I wanted to compare spatial models and data, but the methods that were available weren’t very well-suited to what I wanted to do, so I was not sure of how to begin. In the end, I decided to launch my first microcosms experiment and in the first photos I took of Periphyton’s spatial structure I saw they were like clouds, algae clouds. Continue reading