928 P. C. Pototsky andW. Cresswell
TABLE 3 The relationship between the number of conservation research articles (log transformed) retrieved from Clarivate Analytics (2019) and potential explanatory country-based variables for 41 sub-Saharan African countries. The full model, including all explanatory variables investigated, and the final model are shown. Two non statistically significant variables were also included in the final model because of their AIC weight.
Explanatory variable (Intercept)
Log population size Log area
Population growth rate Log GDP
Median age Urbanization
Education expenditure Literacy rate
Year of independence
Government effectiveness Log international tourism Agricultural land cover
English as an official language
1Adjusted R2 = 0.77, F13,22 = 8.4,P,0.001. 2Adjusted R2 = 0.78, F7,30 = 19.0,P,0.001.
Results
National conservation research publications Overall, 12,701 papers were published during 1987–2017 by authors working in institutions within sub-Saharan African countries (ranging from a total of one in Somalia to 4,986 in South Africa). Authors in only four countries published .500 articles (South Africa, Kenya, Ethiopia and Tanzania) and authors in 38%ofcountries published ,30 articles (Table 1). In 1987, only three articles were published by na- tional authors, from Zimbabwe, South Africa and Ethiopia. In comparison, in 2017, zero papers were recorded for only 15 countries (Burundi, Central African Republic, Chad, Equa- torial Guinea,Eritrea,Guinea-Bissau,Guinea, Lesotho, Liberia, Mauritania, Eswatini, Comoros, Djibouti and Somalia).
Factors predicting national conservation research publications
Log population size, log GDP, and log international tourism (tourism receipts as a % of exports) were significant posi- tive predictors of the number of papers in the full model (Table 3). In addition, the number of research papers mar- ginally significantly decreased with an increase of agri- cultural land cover. No other variables had a statistically significant effect (Table 2). The full model had an adjusted R2 of 0.77. The results were the same for the simplified model except that literacy rate and a log measure of inter- national tourism were also positive significant predictors and agricultural land cover was a significant negative
predictor (Table 2, Fig. 1). The number of papers also de- creased with increasing urbanization; this relationship was not statistically significant but was included in the final model because it appeared in 89% of the top models (Table 4, Fig. 1). The results from the Dredge analysis that considered all possible models confirmed the results of the final model (Table 3).
Distribution of research across institutions
Only four of the top 15 most productive sub-Saharan African institutions were located outside South Africa: Université d’Abomey-Calavi in Abomey-Calavi, Benin; Makerere Uni- versity in Kampala, Uganda; Sokoine University of Agri- culture in Morogoro, Tanzania; and the University of Ghana in Accra, Ghana (Table 5). Authors at all four of these univer- sities produced#20 papers, in the subsample of 2,374 papers, over the 30-year period.
Trends in primary authorship
Overall, total conservation research output with the parti- cipation of national conservation academics is increasing (Fig. 2). Overall, 47% of sampled papers had sub-Saharan African primary authors, but South Africa accounted for 31% of this primary authorship overall, and 67% of pri- mary authorship amongst only sub-Saharan African coun- tries (Table 6). Excluding South Africa, only 16% of papers had sub-Saharan African primary authors. Five countries, including two non-African countries, accounted for over
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
Full model1 Estimate ± SE −24.00 ± 13.60
0.83 ± 0.19
−31.60 ± 27.80 0.56 ± 0.31
−0.03 ± 0.15 0.07 ± 0.06
−1.62 ± 1.19 −2.11 ± 7.43
1.23 ± 0.91 0.01 ± 0.01 0.43 ± 0.46 0.65 ± 0.18
−2.20 ± 1.07 −0.16 ± 0.35
t
−1.77 4.40
−0.23 −1.14
1.81 1.05
−1.36 −0.28
1.36 0.77 0.94 3.52
−2.05 −0.48
P 0.09
,0.001 0.82 0.26 0.09 0.30 0.18 0.77 0.18 0.45 0.35 0.02 0.05 0.63
Final model2 Estimate ± SE −13.90 ± 1.92
t 0.77 ± 0.08
−50.10 ± 22.00 0.78 ± 0.17
−1.36 ± 0.91 1.22 ± 0.71
0.70 ± 0.12 −1.57 ± 0.71
−7.24 9.59
−2.28 4.55
−1.49 1.73
5.86 −2.20 P
,0.001 ,0.001
0.03
,0.001 0.15 0.10
,0.001 0.04
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