Biodiversity–disease relationships in wild plant communities differentially affected by land use

This article is a Commentary on Susi & Laine (2021), 230: 2447–2458. How does host biodiversity alter disease dynamics? question has fuelled many empirical, experimental and modelling studies (Keesing Ostfeld, 2015; Liu et al., 2020; Rohr 2020), especially with regard to the current trends of loss emergence infectious diseases. Two opposing hypotheses state that can modify incidence through either an amplification or dilution effect. While these relationships have been investigated for variety host–parasite systems, questions about effects diversity aboveground plant microorganisms are more scarce. These few focused mainly foliar fungal pathogens, even fewer addressed viruses (Pagán 2012; Lacroix 2014; Laine, 2021). The paper presented by in this issue New Phytologist (2021; pp. 2447–2458), describes correlations between infection risk five most common recently described wild Plantago lanceolata Åland islands (Finland), ecosystem characteristics surrounding communities. represents important contribution literature incidence, generally still relatively overlooked field virus dynamics natural ecosystems (Malmstrom 2011), when compared vast ecology epidemiology agricultural settings. ‘This ecosystems...’ showed negative correlation prevalence species examined (Plantago latent (PlLV), caulimovirus, betapartitivirus, enamovirus closterovirus). result line effect found including those involving tree diseases (Liu 2020). originality study resides not only looking at prevalence, but also richness. Theory predicts positive relationship richness, as within-host microbiome assemblage specialist could differ across various species. hypothesis verified empirically some richness decreased increased environments P. populations. Plant per se might be best sole metric assessing disease. abundance competent hosts, its variation gradient matter (Lacroix Johnson 2019). Nonrandom noncompetent poorly hosts shown drive increase pathogen species-poor communities several systems. same mechanism underlie Host competence assessed under lenses both plant–virus, plant–virus–vector interactions. Each from ranked qualitative measure ability infected each species, quantitative evaluation multiplication, which correlated between-host transmission (Froissart 2010). As average varied greatly (0.8–46.8%) populations, it would interesting know whether decrease overall increasing driven single (e.g. caulimovirus) viruses. In addition, insect vectors such aphids known vary their feeding preference different efficiency transmission. A previous revealed Dysaphis plantaginea, aphid apple trees (Malus domestica), was moderately efficient transmitting PlLV plants controlled conditions (Susi Yet, other naturally occurring vectors, unknown. Hence, biodiversity–disease system further survey identification potential assessment spread based plant–virus plant–vectors Another Laine’s work affected level human interference. Indeed, while populations (>200 m crop fields), no were close proximity (within 20-m distance radius) fields. Furthermore, slightly higher agriculture than located away. contrast findings risk, measured crop-dominated landscapes Claflin 2017a; Ingwell 2017), partly due changes vector community composition (Claflin 2017b). hypothesised there spillovers fields nearby explain context-dependent Comparing within cropped informative test hypothetical spillover agro-ecological interface. Biodiversity–disease spatial patterns particular, population connectivity transmission, therefore Contrary expectations, among increased. pattern explained rates gene flow well connected lead levels resistance disease, previously demonstrated case pathogens very environments. Also, diverse; whereas fragmented ones, perhaps disturbed because interference land use, least diverse. Investigating leads back examining gradients, resistant considered non- poor-competent hosts. allow sources introduce improve protection against pathogens. Overall, thoroughly agronomical impacts quantity, sustainability security food production (FAO, http://www.fao.org, 2017). Nevertheless, provides another example how prevalent diverse viruses, plant-associated microorganisms, plants. 57% tested one co-infections observed 17.1% plants, symptoms time sampling. stated processes leading viewed multifactorial network interactions where biological component environment triangle impact activities specified explicitly (Fig. 1). Considering contexts, avenues research highlighted on: (1) noncropped 2011); (2) (Bradley 2008; Paseka 2020); (3) reciprocal influence natural, managed dynamics. More specifically, springboard subsequent investigations as: cultivated plants? Are already found, likely emerge, crops? reservoirs epidemics? What factors emergence? addition co-infection? Last, least, evolved evolution communities? author grateful Benoit Moury Cindy Morris useful comments manuscript.