Spatial knowledge deficiencies drive taxonomic and geographic selectivity in data deficiency

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The uncertain threat status of species inevitably influences their focus on conservation. Just as in extinction risk, the non-randomness phenomenon related to uncertainty (also referred to as selectivity), which is a certain character cluster in some groupings, also exists in data deficiency of species' knowledge. In order to illustrate this kind of non-random phenomenon and explain the uncertainties it caused, we performed a hypergeometric test on taxonomic and geographic groupings of China's spermatophyte species and quantified two factors— frequency of collections and spatial accessibility— to indicate the primary causes of spatial knowledge deficiencies. We found that selectivity in data deficiency exists both taxonomically and geographically. Fifteen of the families were more deficient than expected, which included 30.0% of species and 56.3% ranked data deficient (DD). Among these, eight families were statistically highly significant with p < 0.001 and included 25.2% of species and 50.0% ranked DD. Forty-six families were less deficient than expected. With respect to floristic division, four of 29 floristic regions and subregions were more deficient than expected, and seven were less deficient than expected. Spatial autocorrelation analysis on DD species suggested an aggregated pattern of data deficiency in China (Moran's I = 0.58, z-score = 27.0, p < 0.001), and these areas that contained the highest numbers of DD species also contained the highest number of species (Spearman's R2 = 0.879, p < 0.001). However, the largest DD ratio had a low correlation with the richest DD spatial diversity. Moreover, we found the larger the DD ratio was, the lower the frequency of collections and the poorer the spatial accessibility would be. In the research, we showed that the uncertainties associated with DD species would alter the non-randomness in the selectivity of data deficiency and further affect the focus of conservation. Only with a full understanding of the process and mechanisms of data deficiency can we determine where and what kind of actions are necessary to improve the knowledge of plant diversity.