Dec | 2020 | ConservationBytes.com

Rank your own sample of journals

29 12 2020

If you follow my blog regularly, you’ll know that around the middle of each year I publish a list of journals in conservation and ecology ranked according to a multi-index algorithm we developed back in 2016. The rank I release coincides with the release of the Web of Knowledge Impact Factors, various Scopus indices, and the Google Scholar journal ranks.

The reasons we developed a multi-index rank are many (summarised here), but they essentially boil down to the following rationale:

(i) No single existing index is without its own faults; (ii) ranks are only really meaningful when expressed on a relative scale; and (iii) different disciplines have wildly different index values, so generally disciplines aren’t easily compared.

That’s why I made the R code available to anyone wishing to reproduce their own ranked sample of journals. However, given that implementing the R code takes a bit of know-how, I decided to apply my new-found addiction to R Shiny to create (yet another) app.

Welcome to the JournalRankShiny app.

This new app takes a pre-defined list of journals and the required indices, and does the resampled ranking for you based on a few input parameters that you can set. It also provides a few nice graphs for the ranks (and their uncertainties), as well as a plot showing the relationship between the resulting ranks and the journal’s Impact Factor (for comparison).

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Tags: bibliometrics, citations, CiteScore, Google Scholar, Immediacy Index, impact factor, journal ranks, SJR, SNIP
Categories : science, scientific publishing

Influential conservation papers of 2020

19 12 2020

Following my late-December tradition, I present — in no particular order — a retrospective list of the ‘top’ 20 influential papers of 2020 as assessed by experts in Faculty Opinions (formerly known as F1000). See previous years’ lists here: 2019, 2018, 2017, 2016, 2015, 2014, and 2013.

Life in fluctuating environments — “… it tackles a fundamental problem of bio-ecology (how living beings cope with the fluctuations of the environment) with a narrative that does not make use of the cumbersome formulas and complicated graphs that so often decorate articles of this kind. Instead, the narrative and the illustrations are user-friendly and easy to understand, while being highly informative.“

Forest carbon sink neutralized by pervasive growth-lifespan trade-offs — “… deals with a key process in the global carbon cycle: whether climate change (CC) is enhancing the natural sink capacity of ecosystems or not.“

Bending the curve of terrestrial biodiversity needs an integrated strategy — “… explores different scenarios about the consequences of habitat conversion on terrestrial biodiversity.“

Rebuilding marine life — “The logic is: leave nature alone, and it will come back. Not necessarily as it was before, but it will come back.“

Towards a taxonomically unbiased European Union biodiversity strategy for 2030 — “… states that the emperor has no clothes, providing an estimate of the money dedicated to biodiversity conservation (a lot of money) and then stating that the bulk of biodiversity remains unstudied and unprotected, while efforts are biased towards just a few “popular” species.“

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Tags: agriculture, ammonium, bioecology, carbon cycle, Climate, climate disruption, CO2, diversification, environmental variation., European Union, farming, food, food security, Forest, genetic rescue, Global warming, governance, MPA, nitrate, outbreeding, Soil, soil biodiversity, synchrony, topoclimates, weather, weathering
Categories : agriculture, anthropocene, biodiversity, birds, cattle, climate change, conservation, conservation biology, deforestation, ecosystem function, ecosystem services, environmental policy, exploitation, extinction, fisheries, habitat loss, marine, marine protected area, protected area, research, science, sustainability

Plan B: COVID-19 challenges for field-based PhD students

8 12 2020

Originally published on the GEL.blog

Blistering heat, pouring rain, finding volunteers, submitting field-trip forms, forgetting equipment, data sheets blowing away in the wind — a field-based research project is hard at the best of times. Add white sharks into the mix and you start to question whether this project is even possible. These were some of my realisations when I started my Honours year studying shark deterrents.

A specific memory from my first field expedition was setting off on a six-day boat trip with the comfortable sight of land getting smaller and smaller, in an already rough ocean, to find one of the most feared fish in the sea, the white shark. I was intimidated, but also excited.

Over the next few days reality set in and I experienced the true challenges of working in the field. When there were no sharks around, I had to concentrate on the bait line for hours in anticipation of a sudden ambush. When there were sharks around, it was all systems go and there was no room for error — not with a fish of this size. It didn’t matter how tired or seasick I was, the data had to be collected.

When I found out that I had been offered a field-based PhD extending my shark-deterrent research from my Honours, other than being over-the-moon, I knew I had a big few years ahead of me. I immediately began preparing mentally for the challenges that came along with my field-based research. Particularly the long periods of time I knew I would spend away from home and my family.

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Tags: academia, career, contingency planning, COVID, COVID-19, fieldwork, PhD, PhDchat, preparedness, student, student life
Categories : ecology, environmental science, fish, marine, research, science, shark, South Australia, water

Need to predict population trends, but can’t code? No problem

2 12 2020

Yes, yes. I know. Another R Shiny app.

However, this time I’ve strayed from my recent bibliometric musings and developed something that’s more compatible with the core of my main research and interests.

Welcome to LeslieMatrixShiny!

Over the years I’ve taught many students the basics of population modelling, with the cohort-based approaches dominating the curriculum. Of these, the simpler ‘Leslie’ (age-classified) matrix models are both the easiest to understand and for which data can often be obtained without too many dramas.

But unless you’re willing to sit down and learn the code, they can be daunting to the novice.

Sure, there are plenty of software alternatives out there, such as Bob Lacy‘s Vortex (a free individual-based model available for PCs only), Resit Akçakaya & co’s RAMAS Metapop ($; PC only), Stéphane Legendre‘s Unified Life Models (ULM; open-source; all platforms), and Charles Todd‘s Essential (open-source; PC only) to name a few. If you’re already an avid R user and already into population modelling, you might be familiar with the population-modelling packages popdemo, OptiPopd, or sPop. I’m sure there are still other good resources out there of which I’m not aware.

But, even to install the relevant software or invoke particular packages in R takes a bit of time and learning. It’s probably safe to assume that many people find the prospect daunting.

It’s for this reason that I turned my newly acquired R Shiny skills to matrix population models so that even complete coding novices can run their own stochastic population models.

I call the app LeslieMatrixShiny.

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