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Mist wafts through the trees at the Monteverde Cloud Forest Biological Preserve in Costa Rica. Cloud forests around the world are threatened by development, wood collection and climate change. Credit: Stefano Paterna/Alamy

Conservation biology
04 May 2021
Forests that float in the clouds are drifting away

Tropical cloud forests are safe havens for a vast range of creatures and plants, but they are under siege around the globe.

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Remote habitats called tropical cloud forests, which cling to misty mountains and tap humid air for water, are in decline. So says a global analysis also reporting that cloud forests, despite occupying just 0.4% of Earth’s land, harbour around 15% of the global biodiversity of birds, mammals, amphibians and tree ferns.Dirk Karger at the Swiss Federal Institute for Forest, Snow and Landscape Research in Birmensdorf, Walter Jetz at Yale University in New Haven, Connecticut, and their colleagues created habitat-prediction models that incorporate remote-sensing data on cloud cover and other conditions to predict the coverage of tropical cloud forests worldwide. They then studied satellite imagery of land cover from 2001 to 2018 to determine the rate of cloud-forest loss and analysed how this loss would affect 3,700 species living in this ecosystem.The team estimates that more than 15,000 square kilometres of tropical cloud forest — 2.4% of the global total — were lost during the 18-year period. Africa and the Americas had the greatest losses. The authors note that the establishment of protected areas did little to halt the loss of habitat and its biodiversity, highlighting the urgent need for other safeguards.

Nature Ecol. Evol. (2021)

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Although Malar et al. “do not exclude the possibility that the genes identified by Mateus et al. are involved in mating,” they qualify the homology inference between genes differentially expressed in the co-inoculation treatment and genes involved in mating in other fungal species as “spurious evolutionary relationships” or “not the best ortholog”. Those statements imply that they attach no importance to the demonstrated sequence homology relationships identified in Mateus et al. Orthology does not necessarily imply conservation of gene function and genes with equivalent functions are not necessarily orthologs [3]. Therefore, it is misleading to assume that two genes have the same function when interpreting the role of a “best candidate ortholog” identified in silico. Moreover, relying only on an in silico search for exploring orthologs can lead to serious problems for inferring function as none of the search algorithms are free from bias if subfunctionalization or neofunctionalization events occurred among the homologs.Malar et al. have not considered, or have misunderstood, the experimental evidence on gene expression in interpreting their homology search. It is not surprising that their “best homologs” were not upregulated, because we already saw that those genes were not upregulated in the original dataset. Our approach comprised performing an experiment to identify genes that were specifically upregulated when two isolates coexisted in planta. We then identified their putative function by homology. We did not look at whether the genes were the closest orthologs. However, we discussed the limitations of an homology approach to identify gene function [2]. To our surprise, a consistent set of 20 genes was upregulated in the co-inoculation treatment in different host plants, and 9 of these 20 (upregulated in more than one host plant) shared the common feature of homology to genes involved in different steps of mating in other fungal species (Figs. 3 and 4 of Mateus et al.).Malar et al. claim the identification of hundreds of hits of the 18 genes differentially expressed in Mateus et al. “against the high-quality protein databases from the JGI Mycocosm Rhiir2” (referring to the protein database “Rhiir2” of R. irregularis). In fact, Malar et al. compared the 18 genes against “all protein gene catalogs of fungal species from the JGI fungal genomic resource” comprising 1318 taxa. The interpretation of the number of hits on a such large dataset is misleading because if a gene is highly conserved across the fungal kingdom, we would expect hundreds of hits in this database. In contrast, if an R. irregularis gene is highly specific to the Glomeromycotina taxa, we would expect very few hits (because there are less Glomeromycotina genomes in the database). Consequently, the number of hits in Table 1 from Malar et al. reflect the size of the database used and how conserved a given gene is, rather than whether a gene is from a large gene family. Malar et al. identified the so-called “closest ortholog” in R. irregularis of fungal mating genes from other fungal species by showing the “best hit” using OrthoMCL. However, differentiating paralogs from orthologs is a complicated task, in very distant species, especially if the organisms are highly paralogous. A more cautious analysis for each gene, including a confirmation that they are located in similar genomic locations, would lend more certitude that a given gene could be an ortholog. Consequently, the evaluation of RNA expression of their “best hit” remains incomplete in terms of the effort to find the best orthologs. More

The underpinning method employed to construct a STR may affect the shape, scaling exponent (w), and fit of the STR power function. Here we used three differing approaches to construct STRs; including, what we term in this study, the ‘every possible window’ (EPW) [10], ‘cumulative moving window’ (CMW) [8], and ‘moving window’ (MW) approaches. The differences in each method are extensively detailed in the Material and Methods. The STRs for the bacterial communities within each of the tree-hole islands were plotted, of which all relationships were significant (Fig. 1 and Table S1). Overall, the resulting STR power law exponents (w) were found to range from 0.048 to 0.350 (Fig. 1) and were typically within the exponent ranges observed from meta-analyses of STRs for a wide range of animals, plants, and microbial communities [9,10,11]. However, these values varied by the approach used to construct STRs (Fig. 2A). The EPW based w values ranged from 0.048 to 0.128, with a mean w of 0.088 ± 0.029 (mean ± SD). The CMW w values ranged from 0.073 to 0.150, with a mean w = 0.111 ± 0.029. Whereas, the MW minimum and maximum w values were 0.223 ± 0.350, with a mean of 0.289 ± 0.044 (Fig. 2A). The EPW and CMW w values were significantly lower than the MW w values (Fig. 2A). However, they were not significantly different from each other, despite that EPW values were uniformly lower (Fig. 2A).Fig. 1: Species-time relationships for the tree-hole bacterial communities.A, B, and C represent species–time relationships (STR) constructed using every possible window, cumulative moving window, and moving window approaches, respectively. Given in each instance is the tree-hole number (TH1–TH10) and the STR power law equation. All STRs were significant (P  More