The title says it all. I wrote this piece about Publication Viability Analysis pondering about a pattern that I observed while looking at Hungarian ecologists publication output through time using the Web of Science database (the original post is in Hungarian).
I fit Ricker growth model to observed publication numbers and a non-stationary 2-phase model was the best fit. An intrinsic growth rate of 0.39 with carrying capacity K = 14 publications per year was characteristic to the pre-democracy (Soviet occupation era) phase (1978–1997). The democratic era (1998–2012) showed growth rate of 0.21 with a much higher K = 100 (variance went from 0.44 to 0.03).
The motivation for the analysis and post was that the number of publications has been persistently around K = 100 for 5 years. So I looked at the correlation between the number of PhD students and the number of publications, using different lag times between 0 and 10 years. The correlation was highest for a 7-year lag. This more or less indicates a cohort of PhD students (myself included) who started PhD around 2000.
This cohort represents the grandchildren of the post-WWII boom, births have declined after this cohort – therefore less PhD students to produce papers. If this is true, it also means that it takes considerable time for PhD students to reach peak publication productivity. So I proposed to shorten the lag to 3–5 years to boost publication numbers. Win for the students and win for Hungary.
Why am I bringing this old post up 3 years later? Because I wanted to
see how well my estimates have held up from a short-term forecasting
standpoint. Well, here are the results of a recent query with identical
ADDRESS=HUNGARY; CATEGORIES=ECOLOGY) for the years 2013–2015:
The figure shows the two phases used in modeling (grey and gold), and the forecast (tomato) with horizontal lines for carrying capacity. I wish these numbers have improved, but it is what it is. You can’t argue with science. In case you want to argue, just leave a comment!
In a recent paper entitled “Lessons learned from comparing spatially explicit models and the Partners in Flight approach to estimate population sizes of boreal birds in Alberta, Canada” we developed improved, spatially explicit models for 81 land bird species in northern Alberta, Canada. We then compared these estimates of bird abundance to a commonly-used but non-spatially explicit estimate by Partners in Flight (PIF v 3.0) that’s based on the North American Breeding Bird Survey (BBS) data set. The publication is a result of years of collaboration between the ABMI, Boreal Avian Modelling (BAM) project, Canadian Wildlife Service (Environment and Climate Change Canada), and United States Geological Survey.
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