Conservation research capacity 927
TABLE 2 Descriptions and sources of country-level variables used in general linear models. Explanatory variable
Description
Population size Area
Gross Domestic Product (GDP)
Median age Urbanization
Literacy rate
English as an official language
Political stability
Government effectiveness
An estimate of total population size, July 2017
The sum of all land & water areas delimited by international boundaries & coastlines in (km2)
Population growth rate The average annual % change in population size, because of births, deaths & migration, 2017 estimate (or most recent data)
A summary of the age distribution of a population, 2017 estimate (or most recent data)
Data source
Central Intelligence Agency (2018)
Central Intelligence Agency (2018)
Central Intelligence Agency (2018)
GDP on a purchasing power parity basis divided by population, as of 1 July 2017 Central Intelligence Agency (2018)
Central Intelligence Agency (2018)
The % of total population living in urban areas, 2017 estimate (or most recent data) Central Intelligence Agency (2018)
Education expenditure Public expenditure on education, as % of GDP, 2017 estimate (or most recent data) Central Intelligence Agency (2018)
% of population age $15 years that can read & write, 2017 estimate (or most recent data)
Year of independence The year when sovereignty was achieved from another nation or empire (not applicable for Ethiopia)
Whether or not English is listed as an official language
Indicator reflecting perceptions of political instability & politically-motivated violence, mean for 1998–2017
Indicator reflecting perceptions of the quality of public services, independence from political pressures, & government credibility, mean for 1998–2017
International tourism International tourism receipts as % of total exports, in 2016 Agricultural land cover Agricultural land as % of land area, mean for 1987–2016
Next, a simplified final model was constructed through
a process of backwards elimination (Crawley, 2007): non- significant variables, starting with that with the highest probability, were removed and this process was repeated until all variables were significant. All models were com- pared using AIC and the final model was that with the lowest AIC. To ensure that subjectivity had not entered the model simplification process, all possible models were also considered using the Dredge function in the MuMIn pack- age in R (Bartón, 2018). As is required for Dredge analyses, the data were subset to exclude missing values, resulting in sample size differences between the full and final model. Variables were ranked by the proportion of models within ΔAIC = 2 of the top model that they occurred in. Variables thatwere not significant when using the backwards elimination method butwere found in.70%of top modelswere included in the final model. Thus, a final model was identified using backwards elimination, based on the lowest AIC, highest AIC weight, and statistical significance. This model was visually in- spected for any violation of assumptions (Crawley, 2007), and checked by reinserting all of the variables that were removed duringmodel simplification, one at a time, to check their effects remained non-significant.We present both full and finalmod- els, to show that the process of model simplification did not change the overall significance of any of the results.
Central Intelligence Agency (2018)
Central Intelligence Agency (2018)
Central Intelligence Agency (2018)
World Bank (2018a) World Bank (2018a) World Bank (2018b)
World Bank (2018b) Trends in research output were explored using the more
detailed information from the 2,374 articles that were subsampled (to allow the analysis to be completed in a reasonable time). A similar methodology has been used in bibliometric analysis in ecosystem monitoring (Yevide et al., 2016), ornithology (Cresswell, 2018), avian conserva- tion (Brito & Oprea, 2016), insect taxonomy (Deng et al., 2019) and biodiversity research (Liu et al., 2011). Geograph- ical and temporal trends in primary authorship were ana- lysed concurrently to explore whether publication output increased significantly over time and to test for any signifi- cant difference in the ratio of non-African to African pri- mary authors over time. Two general linear models were constructed: one to examine changes in publication output (total number of papers per year, with year scaled to start from zero in 1987) and a second to test for any change in the number of publications with national and non-African primary authors over time (change in the ratio of non- African to national primary author papers, with year scaled to start from zero in 1987).Athird model was constructed to test whether the change in national primary authorship has changed over time at a different rate compared to the change in non-African primary authorship over time, by including the interaction term year × type of authorship (non-African or national).
Oryx, 2021, 55(6), 924–933 © The Author(s), 2020. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605320000046
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