Funding begets biodiversity

Abstract

Aim Effective conservation of biodiversity relies on an unbiased knowledge of its distribution. Conservation priority assessments are typically based on the levels of species richness, endemism and threat. Areas identified as important receive the majority of conservation investments, often facilitating further research that results in more species discoveries. Here, we test whether there is circularity between funding and perceived biodiversity, which may reinforce the conservation status of areas already perceived to be important while other areas with less initial funding may remain overlooked.\ud \ud Location Eastern Arc Mountains, Tanzania.\ud \ud Methods We analysed time series data (1980–2007) of funding (n = 134 projects) and plant species records (n = 75,631) from a newly compiled database. Perceived plant diversity, over three decades, is regressed against funding and environmental factors, and variances decomposed in partial regressions. Cross-correlations are used to assess whether perceived biodiversity drives funding or vice versa.\ud \ud Results Funding explained 65% of variation in perceived biodiversity patterns – six times more variation than accounted for by 34 candidate environmental factors. Cross-correlation analysis showed that funding is likely to be driving conservation priorities and not vice versa. It was also apparent that investment itself may trigger further investments as a result of reduced start-up costs for new projects in areas where infrastructure already exists. It is therefore difficult to establish whether funding, perceived biodiversity, or both drive further funding. However, in all cases, the results suggest that regional assessments of biodiversity conservation importance may be biased by investment. Funding effects might also confound studies on mechanisms of species richness patterns.\ud \ud Main conclusions Continued biodiversity loss commands urgent conservation action even if our knowledge of its whereabouts is incomplete; however, by concentrating inventory funds in areas already perceived as important in terms of biodiversity and/or where start-up costs are lower, we risk losing other areas of underestimated or unknown value

Global models of ant diversity suggest regions where new discoveries are most likely are under disproportionate deforestation threat

“…The patterns in modeled diversity reflect the diversity expected but, as the ant biologist Kusnezov wrote in 1957: "different local (ant) faunae have not been investigated equally thoroughly" (13), a truth that persists both for ants and for other insect taxa as well as plants (28)(29)(30). We remain ignorant, but now know enough to begin to map our ignorance and to more systematically make discoveries.…”

Delimiting tropical mountain ecoregions for conservation

“…In providing a spatially explicit definition for this area of endemism, we hope to prompt research and conservation in lesser-studied parts of the Eastern Arc, which could be biologically and politically undervalued due to spatial bias in the data Ahrends et al 2011). The boundaries are also relevant for reforestation strategies, particularly ahead of the United Nations REDD pilot (http://www.un-redd.org/) in Tanzania.…”

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“…Conversely, GBIF participation emerges as a consistently strong factor determining completeness in DAI. Supporting previous suggestions 19,46 , national research funding (gross expenditure on research and development) is strongly positively correlated with completeness (Fig. 3).…”

“…Severe gaps and biases usually exist in DAI 10,[12][13][14] , and require careful consideration in ecological modeling [15][16][17] and conservation research 3 . Data limitations may arise from a multitude of socio-economic and geographic factors, including inadequate financial and institutional resources [18][19][20] , poor international scientific cooperation 20 , lack of access or regional safety concerns [20][21][22][23] , or a focus on regions with certain appeal like endemism-, species-rich or protected areas 12,21,24 . The amount of data required to completely inventory species assemblages is a function of their richness and the spatial grain 13,14,25 .…”