Geometagenomics illuminates the impact of agriculture on the distribution and prevalence of plant viruses at the ecosystem scale.

Disease emergence events regularly result from human activities such as agriculture, which frequently brings large populations of genetically uniform hosts into contact with potential pathogens. Although viruses cause nearly 50% of emerging plant diseases, there is little systematic information about virus distribution across agro-ecological interfaces and large gaps in understanding of virus diversity in nature. Here we applied a novel landscape-scale geometagenomics approach to examine relationships between agricultural land use and distributions of plant-associated viruses in two Mediterranean-climate biodiversity hotspots (Western Cape region of South Africa and Rhône river delta region of France). In total, we analysed 1725 geo-referenced plant samples collected over two years from 4.5 × 4.5 km2 grids spanning farmlands and adjacent uncultivated vegetation. We found substantial virus prevalence (25.8-35.7%) in all ecosystems, but prevalence and identified family-level virus diversity were greatest in cultivated areas, with some virus families displaying strong agricultural associations. Our survey revealed 94 previously unknown virus species, primarily from uncultivated plants. This is the first effort to systematically evaluate plant-associated viromes across broad agro-ecological interfaces. Our findings indicate that agriculture substantially influences plant virus distributions and highlight the extent of current ignorance about the diversity and roles of viruses in nature.

Crowdsourcing the identification of organisms: A case-study of iSpot.

Accurate species identification is fundamental to biodiversity science, but the natural history skills required for this are neglected in formal education at all levels. In this paper we describe how the web application ispotnature.org and its sister site ispot.org.za (collectively, "iSpot") are helping to solve this problem by combining learning technology with crowdsourcing to connect beginners with experts. Over 94% of observations submitted to iSpot receive a determination. External checking of a sample of 3,287 iSpot records verified > 92% of them. To mid 2014, iSpot crowdsourced the identification of 30,000 taxa (>80% at species level) in > 390,000 observations with a global community numbering > 42,000 registered participants. More than half the observations on ispotnature.org were named within an hour of submission. iSpot uses a unique, 9-dimensional reputation system to motivate and reward participants and to verify determinations. Taxon-specific reputation points are earned when a participant proposes an identification that achieves agreement from other participants, weighted by the agreers' own reputation scores for the taxon. This system is able to discriminate effectively between competing determinations when two or more are proposed for the same observation. In 57% of such cases the reputation system improved the accuracy of the determination, while in the remainder it either improved precision (e.g. by adding a species name to a genus) or revealed false precision, for example where a determination to species level was not supported by the available evidence. We propose that the success of iSpot arises from the structure of its social network that efficiently connects beginners and experts, overcoming the social as well as geographic barriers that normally separate the two.

Effect of land cover and ecosystem mapping on ecosystem-risk assessment in the Little Karoo, South Africa.

Extinction-risk assessments aim to identify biological diversity features threatened with extinction. Although largely developed at the species level, these assessments have recently been applied at the ecosystem level. In South Africa, national legislation provides for the listing and protection of threatened ecosystems. We assessed how land-cover mapping and the detail of ecosystem classification affected the results of risk assessments that were based on extent of habitat loss. We tested 3 ecosystem classifications and 4 land-cover data sets of the Little Karoo region, South Africa. Degraded land (in particular, overgrazed areas) was successfully mapped in just one of the land-cover data sets. From <3% to 25% of the Little Karoo was classified as threatened, depending on the land-cover data set and ecosystem classification applied. The full suite of threatened ecosystems on a fine-scale map was never completely represented within the spatial boundaries of a coarse-scale map of threatened ecosystems. Our assessments highlight the importance of land-degradation mapping for the listing of threatened ecosystems. On the basis of our results, we recommend that when budgets are constrained priority be given to generating more-detailed land-cover data sets rather than more-detailed ecosystem classifications for the assessment of threatened ecosystems.

Incorporating geographical and evolutionary rarity into conservation prioritization.

Key goals of conservation are to protect both species and the functional and genetic diversity they represent. A strictly species-based approach may underrepresent rare, threatened, or genetically distinct species and overrepresent widespread species. Although reserves are created for a number of reasons, including economic, cultural, and ecological reasons, their efficacy has been measured primarily in terms of how well species richness is protected, and it is useful to compare how well they protect other measures of diversity. We used Proteaceae species-occurrence data in the Cape Floristic Region of South Africa to illustrate differences in the spatial distribution of species and evolutionary diversity estimated from a new maximum-likelihood molecular phylogeny. We calculated species richness, phylogenetic diversity (i.e., summed phylogenetic branch lengths in a site), and a site-aggregated measure of biogeographically weighted evolutionary distinctiveness (i.e., an abundance weighted measure that captures the unique proportion of the phylogenetic tree a species represents) for sites throughout the Cape Floristic Region. Species richness and phylogenetic diversity values were highly correlated for sites in the region, but species richness was concentrated at a few sites that underrepresented the much more spatially extensive distribution of phylogenetic diversity. Biogeographically weighted evolutionary diversity produced a scheme of prioritization distinct from the other 2 metrics and highlighted southern sites as conservation priorities. In these sites, the high values of biogeographically weighted evolutionary distinctiveness were the result of a nonrandom relation between evolutionary distinctiveness and geographical rarity, where rare species also tended to have high levels of evolutionary distinctiveness. Such distinct and rare species are of particular concern, but are not captured by conservation schemes that focus on species richness or phylogenetic diversity alone.

Causes of plant diversification in the Cape biodiversity hotspot of South Africa.

The Cape region of South Africa is one of the most remarkable hotspots of biodiversity with a flora comprising more than 9000 plant species, almost 70% of which are endemic, within an area of only ± 90,000 km2. Much of the diversity is due to an exceptionally large contribution of just a few clades that radiated substantially within this region, but little is known about the causes of these radiations. Here, we present a comprehensive analysis of plant diversification, using near complete species-level phylogenies of four major Cape clades (more than 470 species): the genus Protea, a tribe of legumes (Podalyrieae) and two speciose genera within the iris family (Babiana and Moraea), representing three of the seven largest plant families in this biodiversity hotspot. Combining these molecular phylogenetic data with ecological and biogeographical information, we tested key hypotheses that have been proposed to explain the radiation of the Cape flora. Our results show that the radiations started throughout the Oligocene and Miocene and that net diversification rates have remained constant through time at globally moderate rates. Furthermore, using sister-species comparisons to assess the impact of different factors on speciation, we identified soil type shifts as the most important cause of speciation in Babiana, Moraea, and Protea, whereas shifts in fire-survival strategy is the most important factor for Podalyrieae. Contrary to previous findings in other groups, such as orchids, pollination syndromes show a high degree of phylogenetic conservatism, including groups with a large number of specialized pollination syndromes like Moraea. We conclude that the combination of complex environmental conditions together with relative climatic stability promoted high speciation and/or low extinction rates as the most likely scenario leading to present-day patterns of hyperdiversity in the Cape.

Diversification of the African genus Protea (Proteaceae) in the Cape biodiversity hotspot and beyond: equal rates in different biomes.

The Cape region of South Africa is a hotspot of flowering plant biodiversity. However, the reasons why levels of diversity and endemism are so high remain obscure. Here, we reconstructed phylogenetic relationships among species in the genus Protea, which has its center of species richness and endemism in the Cape, but also extends through tropical Africa as far as Eritrea and Angola. Contrary to previous views, the Cape is identified as the ancestral area for the radiation of the extant lineages: most species in subtropical and tropical Africa are derived from a single invasion of that region. Moreover, diversification rates have been similar within and outside the Cape region. Migration out of the Cape has opened up vast areas, but those lineages have not diversified as extensively at fine spatial scales as lineages in the Cape. Therefore, higher net rates of diversification do not explain the high diversity and endemism of Protea in the Cape. Instead, understanding why the Cape is so diverse requires an explanation for how Cape species are able to diverge and persist at such small spatial scales.

Delaying conservation actions for improved knowledge: how long should we wait?

Decisions about where conservation actions are implemented are based on incomplete knowledge about biodiversity. The Protea Atlas is a comprehensive database, containing information collated over a decade. Using this data set in a series of retrospective simulations, we compared the outcome from different scenarios of information gain, and habitat protection and loss, over a 20-year period. We assumed that there was no information on proteas at the beginning of the simulation but knowledge improved each year. Our aim was to find out how much time we should spend collecting data before protecting habitat when there is ongoing loss of habitat. We found that, in this case, surveying for more than 2 years rarely increased the effectiveness of conservation decisions in terms of representation of proteas in protected areas and retention within the landscape. If the delay is too long, it can sometimes be more effective just using a readily available habitat map. These results reveal the opportunity costs of delaying conservation action to improve knowledge.

Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models.

Species responses to climate change may be influenced by changes in available habitat, as well as population processes, species interactions and interactions between demographic and landscape dynamics. Current methods for assessing these responses fail to provide an integrated view of these influences because they deal with habitat change or population dynamics, but rarely both. In this study, we linked a time series of habitat suitability models with spatially explicit stochastic population models to explore factors that influence the viability of plant species populations under stable and changing climate scenarios in South African fynbos, a global biodiversity hot spot. Results indicate that complex interactions between life history, disturbance regime and distribution pattern mediate species extinction risks under climate change. Our novel mechanistic approach allows more complete and direct appraisal of future biotic responses than do static bioclimatic habitat modelling approaches, and will ultimately support development of more effective conservation strategies to mitigate biodiversity losses due to climate change.

Biased data reduce efficiency and effectiveness of conservation reserve networks.

Complementarity-based reserve selection algorithms efficiently prioritize sites for biodiversity conservation, but they are data-intensive and most regions lack accurate distribution maps for the majority of species. We explored implications of basing conservation planning decisions on incomplete and biased data using occurrence records of the plant family Proteaceae in South Africa. Treating this high-quality database as 'complete', we introduced three realistic sampling biases characteristic of biodiversity databases: a detectability sampling bias and two forms of roads sampling bias. We then compared reserve networks constructed using complete, biased, and randomly sampled data. All forms of biased sampling performed worse than both the complete data set and equal-effort random sampling. Biased sampling failed to detect a median of 1-5% of species, and resulted in reserve networks that were 9-17% larger than those designed with complete data. Spatial congruence and the correlation of irreplaceability scores between reserve networks selected with biased and complete data were low. Thus, reserve networks based on biased data require more area to protect fewer species and identify different locations than those selected with randomly sampled or complete data.