Monitoring is a fundamental step in the management of any species. The collection and careful analysis of species data allows us to make informed decisions about management priorities and to critically evaluate our actions. There are many aspects of a natural system that we can measure and, when it comes to monitoring the status of species, occurrence is a commonly used metric.
Ecologists have a long history of collecting species occurrence data from systematic surveys and our ability to gather species data is only going to grow! This is partly enabled by the fact that citizen science programs are starting to gain a prominent role in wildlife monitoring. There’s a growing recognition that well-managed citizen science surveys can produce useful data, while scaling up monitoring effort thanks to the increased human-power from large numbers of committed volunteers. Continue reading →
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.
Last week the Center for Open Science held a meeting with the aim of improving inference in ecology and evolution. The organisers (Tim Parker, Jessica Gurevitch & Shinichi Nakagawa) brought together the Editors-in-chief of many journals to try to build a consensus on how improvements could be made. I was brought in due to my interest in statistics and type I errors – be warned, my summary of the meeting is unlikely to be 100% objective.
True Positives and False Positives
The majority of findings in psychology and cancer biology cannot be replicated in repeat experiments. As evolutionary ecologists we might be tempted to dismiss this because psychology is often seen as a “soft science” that lacks rigour and cancer biologists are competitive and unscrupulous. Luckily, we as evolutionary biologists and ecologists have that perfect blend of intellect and integrity. This argument is wrong for an obvious reason and a not so obvious reason.
We tend to concentrate on significant findings, and with good reason: a true positive is usually more informative than a true negative. However, of all the published positives what fraction are true positives rather than false positives? The knee-jerk response to this question is 95%. However, the probability of a false positive (the significance threshold, alpha) is usually set to 0.05, and the probability of a true positive (the power, beta) in ecological studies is generally less than 0.5 for moderate sized effects. The probability that a published positive is true is therefore 0.5/(0.5+0.05) =91%. Not so bad. But, this assumes that the hypotheses and the null hypothesis are equally likely. If that were true, rejecting the null would give us very little information about the world (a single bit actually) and is unlikely to be published in a widely read journal. A hypothesis that had a plausibility of 1 in 25 prior to testing would, if true, be more informative, but then the true positive rate would be down to (1/25)*0.5/((1/25)*0.5+(24/25)*0.05) =29%. So we can see that high false positive rates aren’t always the result of sloppiness or misplaced ambition, but an inevitable consequence of doing interesting science with a rather lenient significance threshold. Continue reading →