More stories

  • in

    MiDAS 4: A global catalogue of full-length 16S rRNA gene sequences and taxonomy for studies of bacterial communities in wastewater treatment plants

    The MiDAS global consortium was established in 2018 to coordinate the sampling and collection of metadata from WWTPs across the globe (Supplementary Data 1). Samples were obtained in duplicates from 740 WWTPs in 425 cities, 31 countries on six continents (Fig. 1a). The majority of the WWTPs were configured with the activated sludge process (69.7%) (Fig. 1b), and these were the main focus of the subsequent analyses. Nevertheless, WWTPs based on biofilters, moving bed bioreactors (MBBR), membrane bioreactors (MBR), and granular sludge were also sampled to cover the microbial diversity in other types of WWTPs. The activated sludge plants were designed for carbon removal only (C; 22.1%), carbon removal with nitrification (C,N; 9.5%), carbon removal with nitrification and denitrification (C,N,DN; 40.9%), and carbon removal with nitrogen removal and enhanced biological phosphorus removal, EBPR (C,N,DN,P; 21.7%) (Fig. 1c). The first type represents the simplest design whereas the latter represents the most advanced process type with varying oxic and anoxic stages or compartments.Fig. 1: Sampling of WWTPs across the world.a Geographical distribution of WWTPs included in the study and their process configuration. b Distribution of plant types. MBBR moving bed bioreactor, MBR membrane bioreactor. c Distribution of process types for the activated sludge plants. C carbon removal, C,N carbon removal with nitrification, C,N,DN carbon removal with nitrification and denitrification, C,N,DN,P carbon removal with nitrogen removal and enhanced biological phosphorus removal (EBPR). The values next to the bars are the number of WWTPs in each group.Full size imageMiDAS 4: a global 16S rRNA gene catalogue and taxonomy for WWTPsMicrobial community profiling at high taxonomic resolution (genus- and species-level) using 16S rRNA gene amplicon sequencing requires a reference database with high-identity reference sequences (≥99% sequence identity) for the majority of the bacteria in the samples and a complete seven-rank taxonomy (domain to species) for all reference sequences16,20. To create such a database for bacteria in WWTPs globally, we applied synthetic long-read full-length 16S rRNA gene sequencing20,21 on samples from all WWTPs included in this study.More than 5.2 million full-length 16S rRNA gene sequences were obtained after quality filtering and primer trimming. The sequences were processed with AutoTax20 to yield 80,557 full-length 16S rRNA gene amplicon sequence variant (FL-ASVs). These reference sequences were added to our previous MiDAS 3 database16, providing a combined database (MiDAS 4) with a total of 90,164 unique, chimera-free FL-ASV reference sequences. The absence of detectable chimeric sequences is a unique feature of the database and is achieved due to the attachment of unique molecular identifiers (UMIs) to each end of the original template molecules before any PCR amplification steps21. This allows filtering of true biological sequences from chimera already in the synthetic long-read assembly20,21. The novelty of the FL-ASVs were determined based on the percent identity shared with their closest relatives in the SILVA 138 SSURef NR99 database and the threshold for each taxonomic rank proposed by Yarza et al.22. Out of all FL-ASVs, 88% had relatives above the genus-level threshold (≥94.5% identity) and 56% above the species-level threshold (≥98.7% identity) (Fig. 2 and Table 1).Fig. 2: Novel sequences and de novo taxa defined in the MiDAS 4 reference database.The phylogenetic trees are based on a multiple alignment of all MiDAS 4 reference sequences, which were first aligned against the global SILVA 138 alignment using the SINA aligner, and subsequently pruned according to the ssuref:bacteria positional variability by parsimony filter in ARB to remove hypervariable regions. The eight phyla with most FL-ASVs are highlighted in different colours. Sequence novelty was determined by the percent identity between each FL-ASV and their closest relative in the SILVA_138_SSURef_Nr99 database according to Usearch mapping and the taxonomic thresholds proposed by Yarza et al.22 shown in Table 1. Taxonomy novelty was defined based on the assignment of de novo taxa by AutoTax20.Full size imageTable 1 Novel sequences and de novo taxa observed in the MiDAS 4 reference database.Full size tableMiDAS 4 provides placeholder names for many environmental taxaAlthough only a small percentage of the reference sequences in MiDAS 4 represented new putative taxa at higher ranks (phylum, class, or order) according to the sequence identity thresholds proposed by Yarza et al.22, a large number of sequences lacked lower-rank taxonomic classifications and was assigned de novo placeholder names by AutoTax20 (Fig. 2 and Table 1). In total, de novo taxonomic names were generated by AutoTax for 26 phyla (30.6% of observed), 83 classes (37.2% of observed), 297 orders (46.8% of observed), and more than 8000 genera (86.3% of observed). Without the de novo taxonomy we would not be able to discuss these taxa across studies to unveil their potential role in wastewater treatment systems.Phylum-specific phylogenetic trees were created to determine if the FL-ASV reference sequences that were assigned to de novo phyla were actual phyla or simply artifacts related to the naive sequence identity-based assignment of de novo placeholder taxonomies (Supplementary Fig. 1a). The majority (65 FL-ASVs) created deep branches from within the Alphaproteobacteria together with 16S rRNA gene sequences from mitochondria, suggesting they represented divergent mitochondrial genes rather than true novel phyla. We also observed several FL-ASVs assigned to de novo phyla that branched from the classes Parcubacteria (3 FL-ASVs) and Microgenomatis (22 FL-ASVs) within the Patescibacteria phylum. These two classes were originally proposed as superphyla due to an unusually high rate of evolution of their 16S rRNA genes23,24. It is, therefore, likely that these de novo phyla are also artefacts due to the simple taxonomy assignment approach, which does not take different evolutionary rates into account20. Most of the class- and order-level novelty was found within the Patescibacteria, Proteobacteria, Firmicutes, Planctomycetota and Verrucomicrobiota. (Supplementary Fig. 1b). At the family- and genus-level, we also observed many de novo taxa affiliated to Bacteroidota, Bdellovibrionota and Chloroflexi.MiDAS 4 provides a common taxonomy for the fieldThe performance of the MiDAS 4 database was evaluated based on an independent amplicon dataset from the Global Water Microbiome Consortium (GWMC) project2, which covers ~1200 samples from 269 WWTPs. The raw GWMC amplicon data of the 16S rRNA gene V4 region was resolved into ASVs, and the percent identity to their best hits in MiDAS 4 and other reference databases was calculated (Fig. 3). The MiDAS 4 database had high-identity hits (≥99% identity) for 72.0 ± 9.5% (mean ± SD) of GWMC ASVs with ≥0.01% relative abundance, compared to 57.9 ± 8.5% for the SILVA 138 SSURef NR99 database, which was the best of the universal reference databases (Fig. 3). The relative abundance cutoff selects taxa that likely have a quantitative impact on the ecosystem while filtering out the rare biosphere which includes many bacteria introduced with the influent wastewaters25. Similar analyses of ASVs obtained from the samples included in this study showed, not surprisingly, even better performance with high-identity hits for 90.7 ± 7.9% of V1–V3 ASVs and 90.0 ± 6.6% of V4 ASVs with ≥0.01% relative abundance, compared to 60.6 ± 11.9% and 73.9 ± 10.3% for SILVA (Supplementary Fig. 2a). Although the sampling of WWTPs was focused towards activated sludge plants, the MiDAS 4 database also includes high-identity references for most ASVs in other plant types (granules, biofilters, etc.) (Supplementary Fig. 2b). This suggests that most taxa were shared across plant types, although often present in other relative abundances.Fig. 3: Database evaluation based on amplicon data from the Global Water Microbiome Consortium project.Raw amplicon data from the Global Water Microbiome Consortium project2 was processed to resolve ASVs of the 16S rRNA gene V4 region. The ASVs for each of the samples were filtered based on their relative abundance (only ASVs with ≥0.01% relative abundance were kept) before the analyses. The percentage of the microbial community represented by the remaining ASVs after the filtering was 88.35 ± 2.98% (mean ± SD) across samples. High-identity (≥99%) hits were determined by the stringent mapping of ASVs to each reference database. Classification of ASVs was done using the SINTAX classifier. The violin and box plots represent the distribution of percent of ASVs with high-identity hits or genus/species-level classifications for each database across n = 1165 biologically independent samples. Box plots indicate median (middle line), 25th, 75th percentile (box) and the min and max values after removing outliers based on 1.5x interquartile range (whiskers). Outliers have been removed from the box plots to ease visualisation. Different colours are used to distinguish the different databases.Full size imageUsing MiDAS 4 with the SINTAX classifier, it was possible to obtain genus-level classifications for 75.0 ± 6.9% of the GWMC ASVs with ≥0.01% relative abundance (Fig. 3). In comparison, SILVA 138 SSURef NR99, which was the best of the universal reference databases, could only classify 31.4 ± 4.2% of the ASVs to genus-level. When MiDAS 4 was used to classify amplicons from this study, we obtained genus-level classification for 92.0 ± 4.0% of V1–V3 ASVs and 84.8 ± 3.6% of V4 ASVs (Supplementary Fig. 2a). This is close to the theoretical limit set by the phylogenetic signal provided by each amplicon region analyzed20. Improved classifications were also observed for archaeal V4 ASVs (93.3 ± 10.6% for MiDAS 4 vs 69.3 ± 21.3% for SILVA), although no additional archaeal reference sequences were added to the MiDAS database in this study.MiDAS 4 was also able to assign species-level classifications to 40.8 ± 7.1% of the GWMC ASVs. In contrast, the 16S rRNA gene reference database obtained from GTDB SSU r89, which is the only universal reference database that contains a comprehensive species-level taxonomy, only classified 9.9 ± 2.0% of the ASVs (Fig. 3). For the ASVs created in this study, MiDAS 4 provided a species-level classification for 68.4 ± 6.1% of the V1–V3 and 48.5 ± 6.0% of the V4 ASVs (Supplementary Fig. 2a).Based on the large number of WWTPs sampled, their diversity, and the independent evaluation based on the GWMC dataset2, we expect that the MiDAS 4 reference database essentially covers the large majority of bacteria in WWTPs worldwide. Therefore, the MiDAS 4 taxonomy should act as a shared vocabulary for wastewater treatment microbiologists, providing opportunities for cross-study comparisons and ecological studies at high taxonomic resolution.Comparison of the V1–V3 and V4 primer sets for community profiling of WWTPsBefore investigating what factors shape the activated sludge microbiota, we compared short-read amplicon data created for all activated sludge samples belonging to the four main process types (C; C,N; C,N,DN and C,N,DN,P) collected in the Global MiDAS project using two commonly used primer sets that target the V1–V3 or V4 variable region of the 16S rRNA gene. The V1–V3 primers were chosen because the corresponding region of the 16S rRNA gene provides the highest taxonomic resolution of common short-read amplicons20,26, and these primers have previously shown great correspondence with metagenomic data and quantitative fluorescence in situ hybridisation (FISH) results for wastewater treatment systems17. The V4 region has a lower phylogenetic signal, but the primers used for amplification have better theoretical coverage of the bacterial diversity in the SILVA database20,26.The majority of genera (62%) showed less than twofold difference in relative abundances between the two primer sets, and the rest were preferentially detected with either the V1–V3 or the V4 primer (19% for both) (Fig. 4). We observed that several genera of known importance detected in high abundance by V1–V3 were hardly observed by V4, including Acidovorax, Rhodoferax, Ca. Villigracilis, Sphaerotilus and Leptothrix. Similarly, we observed genera abundant with V4 but strongly underestimated by V1–V3, such as Acinetobacter and Prosthecobacter. A complete list of differentially detected genera (Supplementary Data 2) serves as a valuable tool in combination with in silico primer evaluation for deciding which primer pair to use for targeted studies of specific taxa.Fig. 4: Comparison of relative genus abundance based on V1–V3 and V4 region 16S rRNA gene amplicon data.a Mean relative abundance was calculated based on 709 activated sludge samples. Genera present at ≥0.001% relative abundance in V1–V3 and/or V4 datasets are considered. Genera with less than twofold difference in relative abundance between the two primer sets are shown with gray circles, and those that are overrepresented by at least twofold with one of the primer sets are shown in red (V4) and blue (V1–V3). The twofold difference is an arbitrary choice; however, it relates to the uncertainty we usually encounter in amplicon data. Genus names are shown for all taxa present at a minimum of 0.1% mean relative abundance (excluding those with de novo names). b Heatmaps of the most abundant genera with more than twofold relative abundance difference between the two primer sets.Full size imageBecause the V1–V3 primers provide better classification rates at the genus- and species-level (Supplementary Fig. 2a), we primarily focused on this dataset for the following analyses. It should be noted that the V1–V3 primer set performs poorly on anammox bacteria27,28 and does not target archaea at all. To determine the importance of these groups, we estimated their relative read abundance using the V4 amplicon data. Ca. Brocadia and Ca. Anammoximicrobium were the only anammox genera detected, and the latter was never more than 0.6% abundant. Ca. Brocadia was observed in MBBR reactors and granular sludge in anammox reactors with relative read abundances reaching 29%, but it was below 0.1% relative abundance in all but two of the activated sludge samples investigated. For archaea, the relative read abundance was generally low (median = 0.18%), but for a few WWTPs high (up to 11.7%), so archaea should not be neglected in these cases.Process and environmental factors affecting the activated sludge microbiotaAlpha diversity analysis revealed that the rarefied (10,000 read per sample) richness and diversity in activated sludge plants were most strongly affected by process type, industrial load and continent (Supplementary Fig. 3 and Supplementary Note 1). The richness and diversity increased with the complexity of the treatment process, as found in other studies, reflecting the increased number of niches29. In contrast, it decreased with high industrial loads, presumably because industrial wastewater often is less complex and therefore promotes the growth of fewer specialised species7. The effect of continents is presumably caused by the necessary unbalanced sampling of WWTPs and confounded by the effects of plant types and industrial loads.Distance decay relationship (DDR) analyses were used to determine the effect of geographic distance on the microbial community similarity of activated sludge plants with the four main process types (Supplementary Fig. 4 and Supplementary Note 2). We found that distance decay was only effective within shorter geographical distances (2500 km) at the ASV-level, but higher similarities with OTUs clustered at 97% and even more at the genus-level. This suggests that many ASVs are geographically restricted and functionally redundant in the activated sludge microbiota, so different strains or species from the same genus across the world may provide similar functions.To gain a deeper understanding of the factors that shape the activated sludge microbiota, we examined the genus-level taxonomic beta-diversity using principal coordinate analysis (PCoA) and permutational multivariate analysis of variance (PERMANOVA) analyses (Fig. 5 and Supplementary Note 3). We have chosen taxonomic diversity instead of phylogenetic diversity (UniFrac) because many of the important traits are categorical (yes/no) and only conserved at lower taxonomic ranks (genus/species). The analysis was made at the genus-level due to the high classification rate achieved with MiDAS 4 and because genera were less affected by DDR compared to ASVs. We found that the overall microbial community was most strongly affected by continent and temperature in the WWTPs. However, process type, industrial load and the climate zone also had significant impacts. The percentage of total variation explained by each parameter was generally low, indicating that the global WWTPs microbiota represents a continuous distribution rather than distinct states, as observed for the human gut microbiota30.Fig. 5: Effects of process and environmental factors on the activated sludge microbial community structure. Principal coordinate analyses of Bray–Curtis and Soerensen beta-diversity for genera based on V1–V3 amplicon data. Samples are coloured based on metadata.The fraction of variation in the microbial community explained by each variable in isolation was determined by PERMANOVA (Adonis R2-values). Exact P values 0.1% relative abundance in 80% (strict core), 50% (general core) and 20% (loose core) of all activated sludge plants (Fig. 6a).Fig. 6: Identification of core and conditionally rare or abundant taxa based on V1–V3 amplicon data.a Identification of strict, general and loose core genera based on how often a given genus was observed at a relative abundance above 0.1% in WWTPs. b Identification of conditionally rare or abundant (CRAT) genera based on whether a given genus was observed at a relative abundance above 1% in at least one WWTP. The cumulative genus abundance is based on all ASVs classified at the genus-level. All core genera were removed before identification of the CRAT genera. c, d Number of genera and species, respectively, and their abundance in different process types across the global WWTPs. Values for genera and species are divided into strict core, general core, loose core, CRAT, other taxa and unclassified ASVs. The relative abundance of different groups was calculated based on the mean relative abundance of individual genera or species across samples. C carbon removal, C,N carbon removal with nitrification, C,N,DN carbon removal with nitrification and denitrification, C,N,DN,P carbon removal with nitrogen removal and enhanced biological phosphorus removal (EBPR).Full size imageIn addition to the core taxa, we also identified conditionally rare or abundant taxa (CRAT)32 (Fig. 6b). These are taxa typically present in low abundance but occasionally become prevalent, including taxa related to process disturbances, such as bacteria causing activated sludge foaming or those associated with the degradation of specific residues in industrial wastewater. CRAT have only been studied in a single WWTP treating brewery wastewater, despite their potential effect on performance32,33. CRAT are here defined as taxa which are not part of the core, but present in at least one WWTP with a relative abundance above 1%.Core taxa and CRAT were identified for both the V1–V3 and V4 amplicon data to ensure that critical taxa were not missed due to primer bias. We identified 250 core genera (15 strict, 65 general and 170 loose) and 715 CRAT genera (Supplementary Data 4). The strict core genera (Fig. 7) mainly contained genera with versatile metabolisms found in several environments, including Flavobacterium, Novosphingobium and Haliangium. The general core (Fig. 7) included many known bacteria associated with nitrification (Nitrosomonas and Nitrospira), polyphosphate accumulation (Tetrasphaera, Ca. Accumulibacter) and glycogen accumulation (Ca. Competibacter). The loose core contained well-known filamentous bacteria (Ca. Microthrix, Ca. Promineofilum, Ca. Sarcinithrix, Gordonia, Kouleothrix and Thiothrix), but also Nitrotoga, a less common nitrifier in WWTPs.Fig. 7: Percent relative abundance of strict and general core taxa across process types.The taxonomy for the core genera indicates phylum and genus. For general core species, genus names are also provided. De novo taxa in the core are highlighted in red. C carbon removal, C,N carbon removal with nitrification, C,N,DN carbon removal with nitrification and denitrification, C,N,DN,P carbon removal with nitrogen removal and enhanced biological phosphorus removal (EBPR).Full size imageBecause MiDAS 4 allowed for species-level classification, we also identified core and CRAT species based on the same criteria as for genera (Supplementary Fig. 7 and Supplementary Data 4). This revealed 113 core species (0 strict, 9 general and 104 loose). The general core species (Fig. 7) included Nitrospira defluvii and Tetrasphaera midas_s_5, a common nitrifier and PAO, respectively. Arcobacter midas_s_2255, a potential pathogen commonly abundant in the influent wastewater, was also part of the general core34. The loose core contained additional species associated with nitrification (Nitrosomonas midas_s_139 and Nitrospira nitrosa), polyphosphate accumulation (Ca. Accumulibacter phosphatis, Dechloromonas midas_s_173, Tetrasphaera midas_s_45), as well as known filamentous species (Ca. Microthrix parvicella and midas_s_2 (recently named Ca. M. subdominans35), Ca. Villigracilis midas_s_471 and midas_s_9223, Leptothrix midas_s_884). In addition to the core species, we identified 1417 CRAT species. As CRAT taxa are generally found in low abundance and the current study does not include time series or influent data, we cannot say anything conclusive about their general implications for the ecosystem. However, they may be present due to short-term mass immigration25 or specific operational conditions36 and in both cases, potentially affect the plant operation. They should therefore be considered important target for further investigations together with the core taxa.Many core taxa and CRAT can only be identified with MiDAS 4The core taxa and CRAT included a large proportion of MiDAS 4 de novo taxa. At the genus-level, 106/250 (42%) of the core genera and 500/715 (70%) of the CRAT genera had MiDAS placeholder names. At the species-level, the proportion was even higher. Here placeholder names were assigned to 101/113 (89%) of the core species and 1352/1417 (95%) CRAT species. This highlights the importance of a comprehensive taxonomy that includes the uncultured environmental taxa.The core and CRAT taxa cover the majority of the global activated sludge microbiotaAlthough the core taxa and CRAT represent a small fraction of the total diversity observed in the MiDAS 4 reference database, they accounted for the majority of the observed global activated sludge microbiota (Fig. 6c, d). Accumulated read abundance estimates ranged from 57–68% for the core genera and 11–13% for the CRAT, and combined they accounted for 68–79% of total read abundance in the WWTPs depending on process types. The core taxa represented a larger proportion of the activated sludge microbiota for the more advanced process types, which likely reflects the requirement of more versatile bacteria associated with the alternating redox conditions in these types of WWTPs. The remaining fraction, 21–32%, consisted of 6–8% unclassified genera and genera present in very low abundance, presumably with minor importance for the plant performance. The species-level core taxa and CRAT represented 11–24% and 24–33% accumulated read abundance, respectively. Combined, they accounted for almost 50% of the observed microbiota.Global diversity within important functional guildsThe general change from simple to advanced WWTPs with nutrient removal and the transition to water resource recovery facilities (WRRFs) requires increased knowledge about the bacteria responsible for the removal and recovery of nutrients, so we examined the global diversity of well-described nitrifiers, denitrifiers, PAOs and GAOs (Fig. 8). GAOs were included because they may compete with the PAOs for nutrients and thereby interfere with the biological recovery of phosphorus37. Because MiDAS 4 provided species-level resolution for a large proportion of activated sludge microbiota, we also investigated the species-level diversity within genera affiliated with the functional guilds. A complete overview of species in all genera detected in this global study is provided in the MiDAS field guide (https://www.midasfieldguide.org/guide).Fig. 8: Global diversity of genera belonging to major functional groups.The percent relative abundance represents the mean abundance for each country considering only WWTPs with the relevant process types. Countries are grouped based on continent (shifting colour).Full size imageNitrosomonas and potential comammox Nitrospira were the only abundant (≥0.1% average relative abundance) genera found among ammonia-oxidising bacteria (AOBs), whereas both Nitrospira and Nitrotoga were abundant among the nitrite oxidisers (NOBs), with Nitrospira being the most abundant across all countries (Fig. 8). Nitrobacter was not detected, and Nitrosospira was detected in only a few plants in very low abundance (≤0.01% average relative abundance). At the species-level, each genus had 2–5 abundant species (Supplementary Fig. 8). The most abundant and widespread Nitrosomonas species was midas_s_139. However, midas_s_11707 and midas_s_11733 were dominating in a few countries. For Nitrospira, the most abundant species in nearly all countries was N. defluvii. ASVs classified as the comammox N. nitrosa38,39 was also common in many countries across the world. However, because the comammox trait is not phylogenetically conserved at the 16S rRNA gene level38,39, we cannot conclude that these ASVs represent true comammox bacteria. For Nitrotoga, only two species were detected with notable abundance, midas_s_181 and midas_s_9575. Ammonia-oxidising archaea (AOAs) were not detected with MiDAS 4 due to the lack of reference sequences, and because AOAs are not targeted by the V1–V3 primer pair. However, analyses of our V4 amplicon dataset classified with the SILVA database revealed a considerable relative read abundance of AOAs in Malaysia and the Philippines, but absence or low abundance of AOAs in other countries (Supplementary Fig. 9). Other studies have occasionally found AOAs across the world, but generally in lower abundance than AOBs40,41,42. To ensure detection of AOAs with MiDAS 4, we anticipate adding external reference sequences for AOAs in a future release of the database.Denitrifying bacteria are very common in advanced activated sludge plants, but are generally poorly described. Among the known genera, Rhodoferax, Zoogloea and Thauera were most abundant (Fig. 8). Zoogloea and Thauera are well-known floc formers, sometimes causing unwanted slime formation43. Rhodoferax was the most common denitrifier in Europe, whereas Thauera dominated in Asia. Many denitrifiers could not be classified at the species-level (Supplementary Fig. 10), likely due to highly conserved 16S rRNA genes. An exception was Zoogloea, where midas_s_1080 and Z. caeni and were the most abundant species worldwide.EBPR is performed by PAOs, with three genera recognised as important in full-scale WWTPs: Tetrasphaera, Dechloromonas and Ca. Accumulibacter13. According to relative read abundance, all three were found in EBPR plants globally, with Tetrasphaera as the most prevalent (Fig. 8). Dechloromonas was also abundant in nitrifying and denitrifying plants without EBPR, indicating a more diverse ecology. Four recognised GAOs were found globally: Ca. Competibacter, Defluviicoccus, Propionivibrio and Micropruina, with Ca. Competibacter being the most abundant (Fig. 8). Only a few species (2–6 species) in each genus were dominant across the world for both PAOs (Supplementary Fig. 11) and GAOs (Supplementary Fig. 12), except for Ca. Competibacter, which covered ~20 abundant but country-specific species. Among PAOs, the abundant species were Tetrasphaera midas_s_5, Dechloromonas midas_s_173, (recently named D. phosphorivorans) Ca. Accumulibacter midas_s_315, Ca. A. phosphatis and Ca. A. aalborgensis. Interestingly, some of the most abundant PAOs and GAOs were also abundant in the simple process design with C-removal, indicating more versatile metabolisms.Global diversity of filamentous bacteriaFilamentous bacteria are essential for creating strong activated sludge flocs. However, in large numbers, they can also lead to loose flocs and poor settling properties. This is known as bulking, a major operational problem in many WWTPs. Many can also form foam on top of process tanks due to hydrophobic surfaces. Presently, approximately 20 genera are known to contain filamentous species44, and among those, the most abundant are Ca. Microthrix, Leptothrix, Ca. Villigracilis, Trichococcus and Sphaerotilus (Fig. 9). They are all well-known from studies on mitigation of poor settling properties in WWTPs. Interestingly, Leptothrix, Sphaerotilus and Ca. Villigracilis belong to the genera where abundance-estimation depended strongly on primers, with V4 underestimating their abundance (Fig. 3). Ca. Microthrix and Leptothrix were strongly associated with continents, most common in Europe and less in Asia and North America (Fig. 9).Fig. 9: Global diversity of known filamentous organisms.The percent relative abundance represents the mean abundance for each country across all process types. Countries are grouped based on the continent (shifting colour).Full size imageMany of the filamentous bacteria were linked to specific process types (Supplementary Fig. 13), e.g. Ca. Microthrix were not observed in WWTPs with carbon removal only, and Ca. Amarolinea were only abundant in plants with nutrient removal. The number of abundant species within the genera were generally low, with one species in Trichococcus, two in Ca. Microthrix and approximately five in Leptothrix and Ca. Villigracilis (Supplementary Fig. 14). Only five abundant species were observed for Sphaerotilus. However, a substantial fraction of unclassified ASVs was also observed, demonstrating that certain species within this genus are poorly resolved based on the 16S rRNA gene. Ca. Promineofilum was also poorly resolved at the species-level (Supplementary Fig. 15).Conclusion and perspectivesWe present a worldwide collaborative effort to produce MiDAS 4, an ASV-resolved full-length 16S rRNA gene reference database, which covers more than 31,000 species and enables genus- to species-level resolution in microbial community profiling studies. MiDAS 4 covers the vast majority of WWTP bacteria globally and provides a strongly needed common taxonomy for the field, which provides the foundation for comprehensive linking of microbial taxa in the ecosystem with their functional traits. Presently, hundreds of studies are undertaken to combine engineering and microbial aspects of full-scale WWTPs. However, most ASVs or OTUs in these studies are classified at poor taxonomic resolution (family-level or above) due to the use of incomplete universal reference databases. Because many important functional traits are only conserved at high taxonomic resolution (genus- or species-level), this strongly hampers our ability to transfer taxa-specific knowledge from one study to another. This will change with MiDAS 4, and we expect that reprocessing of data from earlier studies may reveal new perspectives into wastewater treatment microbiology. Our online Global MiDAS Field Guide presents the data generated in this study and summarises present knowledge about all taxa. We encourage researchers within the field to contribute new knowledge to MiDAS using the contact link in the MiDAS website (https://www.midasfieldguide.org/guide/contact).The global microbiota of activated sludge plants has been predicted to harbour a massive diversity with up to one billion species2. However, most of these occur at very low abundance and are of little importance for the treatment process. By focusing only on the abundant taxa, we can see that this number is much smaller, i.e., ~1000 genera and 1500 species. We consider these taxa functionally the most important globally, representing a “most wanted list” for future studies. Some taxa are abundant in most WWTPs (core taxa), and others are occasionally abundant in fewer plants (CRAT). The CRAT have received little attention in the field of wastewater treatment, but they can be of profound importance for WWTP performance. Both groups have a high fraction of poorly characterised species. The high taxonomic resolution provided by MiDAS 4 enables us to identify samples where these important core taxa occur in high abundance. This provides an ideal starting point for obtaining high-quality metagenome-assembled genomes (MAGs), isolation of pure cultures, in addition to targeted culture-independent studies to uncover their physiological and ecological roles.Among the known functional guilds, such as nitrifiers or polyphosphate-accumulating organisms, the same genera were found worldwide, with only a few abundant species in each genus. There were differences in the community structure, and the abundance of dominant species was mainly shaped by process type, temperature, and in some cases, continent. This discovery sends an important message to the field: relatively few species are abundant worldwide, so research or operational results can reliably be transferred from one geographical region to another, stimulating the transition from WWTPs to more sustainable WRRFs.The relatively low number of uncharacterised abundant species also shows that it is within our reach to describe them all in terms of identity, physiology, ecology and dynamics, providing the necessary knowledge for informed process optimisation and management. The number of poorly described genera (i.e. those with only a MiDAS placeholder genus name) was 88 among the 250 core genera (35%) and more than 89% at the species-level, so there is still some work to do to link their identities and function. An important step in this direction is the visualisation of the populations. With the comprehensive set of FL-ASVs, it is possible to design highly specific FISH probes, and to critically evaluate the old probes. In the Danish WWTPs, we have successfully done this for groups in the Acidobacteriota42 based on the MiDAS 3 database18. Our recent retrieval of more than 1000 high-quality MAGs from Danish WWTPs with advanced process design is also an important step to link identity to function43. The HQ-MAGs can be linked directly to MiDAS 4 as they contain complete 16S rRNA genes. They cover 62% (156/250) of the core genera and 61% (69/113) of the core species identified in this study. These MAGs may also form the basis for further studies to link identity and function, e.g. by applying metatranscriptomics44 and other in situ techniques such as FISH combined with Raman45,46, guided by the “most wanted” list provided in this study. We expect that MiDAS 4 will have significant implications for future microbial ecology studies in wastewater treatment systems. More

  • in

    Fusarium species isolated from post-hatchling loggerhead sea turtles (Caretta caretta) in South Africa

    Zhang, N. et al. Members of the Fusarium solani species complex that cause infections in both humans and plants are common in the environment. J. Clin. Microbiol. 44, 2186–2190 (2006).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    O’Donnell, K. et al. Molecular Phylogenetic Diversity, Multilocus Haplotype Nomenclature, and In Vitro antifungal resistance within the Fusarium solani species complex. J. Clin. Microbiol. 46, 2477–2490 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    Schroers, H. J. et al. Epitypification of Fusisporium (Fusarium) solani and its assignment to a common phylogenetic species in the Fusarium solani species complex. Mycologia 108, 806–819 (2016).CAS 
    PubMed 

    Google Scholar 
    O’Donnell, K. Molecular phylogeny of the Nectria haematococca-Fusarium solani species complex. Mycologia 92, 919–938 (2000).
    Google Scholar 
    Gleason, F., Allerstorfer, M. & Lilje, O. Newly emerging diseases of marine turtles, especially sea turtle egg fusariosis (SEFT), caused by species in the Fusarium solani complex (FSSC). Mycology 11, 184–194 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fernando, N. et al. Fatal Fusarium solani species complex infections in elasmobranchs: the first case report for black spotted stingray (Taeniura melanopsila) and a literature review. Mycoses 58, 422–431 (2015).PubMed 

    Google Scholar 
    Sarmiento-Ramírez, J. M. et al. Global distribution of two fungal pathogens threatening endangered Sea Turtles. PLoS ONE 9, e85853 (2014).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mayayo, E., Pujol, I. & Guarro, J. Experimental pathogenicity of four opportunist Fusarium species in a murine model. J. Med. Microbiol. 48, 363–366 (1999).CAS 
    PubMed 

    Google Scholar 
    Muhvich, A. G., Reimschuessel, R., Lipsky, M. M. & Bennett, R. O. Fusarium solani isolated from newborn bonnethead sharks, Sphyrna tiburo (L.). J. Fish Dis. 12, 57–62 (1989).
    Google Scholar 
    Crow, G. L., Brock, J. A. & Kaiser, S. Fusarium solani fungal infection of the lateral line canal system in captive scalloped hammerhead sharks (Sphyrna lewini) in Hawaii. J. Wildl. Dis. 31, 562–565 (1995).CAS 
    PubMed 

    Google Scholar 
    Cabañes, F. J. et al. Cutaneous hyalohyphomycosis caused by Fusarium solani in a loggerhead sea turtle (Caretta caretta L.). J. Clin. Microbiol. 35, 3343–3345 (1997).PubMed 
    PubMed Central 

    Google Scholar 
    Cafarchia, C. et al. Fusarium spp. in Loggerhead Sea Turtles (Caretta caretta): From Colonization to Infection. Vet. Pathol. 57, 139–146 (2019).PubMed 

    Google Scholar 
    Garcia-Hartmann, M., Hennequin, C., Catteau, S., Béatini, C. & Blanc, V. Cas groupés d’infection à Fusarium solani chez de jeunes tortues marines Caretta caretta nées en captivité. J. Mycol. Med. 28, 113–118 (2017).
    Google Scholar 
    Orós, J., Delgado, C., Fernández, L. & Jensen, H. E. Pulmonary hyalohyphomycosis caused by Fusarium spp in a Kemp’s ridley sea turtle (Lepidochelys kempi): An immunohistochemical study. N. Z. Vet. J. 52, 150–152 (2004).PubMed 

    Google Scholar 
    Candan, A. Y., Katılmış, Y. & Ergin, Ç. First report of Fusarium species occurrence in loggerhead sea turtle (Caretta caretta) nests and hatchling success in Iztuzu Beach, Turkey. Biologia (Bratisl). https://doi.org/10.2478/s11756-020-00553-4 (2020).Article 

    Google Scholar 
    Sarmiento-Ramirez, J. M., van der Voort, M., Raaijmakers, J. M. & Diéguez-Uribeondo, J. Unravelling the Microbiome of eggs of the endangered Sea Turtle Eretmochelys imbricata identifies bacteria with activity against the emerging pathogen Fusarium falciforme. PLoS ONE 9, e95206 (2014).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sarmiento-Ramírez, J. M. et al. Fusarium solani is responsible for mass mortalities in nests of loggerhead sea turtle, Caretta caretta, in Boavista, Cape Verde. FEMS Microbiol. Lett. 312, 192–200 (2010).PubMed 

    Google Scholar 
    Sarmiento-Ramirez, J. M., Sim, J., Van West, P. & Dieguez-Uribeondo, J. Isolation of fungal pathogens from eggs of the endangered sea turtle species Chelonia mydas in Ascension Island. J. Mar. Biol. Assoc. United Kingdom 97, 661–667 (2017).CAS 

    Google Scholar 
    Hoh, D., Lin, Y., Liu, W., Sidique, S. & Tsai, I. Nest microbiota and pathogen abundance in sea turtle hatcheries. Fungal Ecol. 47, 100964 (2020).
    Google Scholar 
    Güçlü, Ö., Bıyık, H. & Şahiner, A. Mycoflora identified from loggerhead turtle (Caretta caretta) egg shells and nest sand at Fethiye beach, Turkey. Afr. J. Microbiol. Res. 4, 408–413 (2010).
    Google Scholar 
    Gambino, D. et al. First data on microflora of loggerhead sea turtle (Caretta caretta) nests from the coastlines of Sicily. Biol. Open 9, bio045252 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Bailey, J. B., Lamb, M., Walker, M., Weed, C. & Craven, K. S. Detection of potential fungal pathogens Fusarium falciforme and F. keratoplasticum in unhatched loggerhead turtle eggs using a molecular approach. Endanger. Species Res. 36, 111–119 (2018).
    Google Scholar 
    Summerbell, R. C. & Schroers, H.-J. Analysis of Phylogenetic Relationship of Cylindrocarpon lichenicola and Acremonium falciforme to the Fusarium solani Species Complex and a Review of similarities in the spectrum of opportunistic infections caused by these fungi. J. Clin. Microbiol. 40, 2866–2875 (2002).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nel, R., Punt, A. E. & Hughes, G. R. Are coastal protected areas always effective in achieving population recovery for nesting sea turtles?. PLoS ONE 8, e63525 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Branch, G. & Branch, M. Living Shores. (Pippa Parker, 2018).Fuller, M. S., Fowles, B. E. & Mclaughlin, D. J. Isolation and pure culture study of marine phycomycetes. Mycologia 56, 745–756 (1964).
    Google Scholar 
    Greeff, M. R., Christison, K. W. & Macey, B. M. Development and preliminary evaluation of a real-time PCR assay for Halioticida noduliformans in abalone tissues. Dis. Aquat. Organ. 99, 103–117 (2012).CAS 
    PubMed 

    Google Scholar 
    Sandoval-Denis, M., Lombard, L. & Crous, P. W. Back to the roots: a reappraisal of Neocosmospora. Persoonia Mol. Phylogeny Evol. Fungi 43, 90–185 (2019).CAS 

    Google Scholar 
    O’Donnell, K., Cigelnik, E. & Nirenberg, H. I. Molecular systematics and phylogeography of the Gibberella fujikuroi species complex. Mycologia 90, 465–493 (1998).
    Google Scholar 
    Geiser, D. M. et al. FUSARIUM-ID v. 1. 0: A DNA sequence database for identifying Fusarium. Eur. J. Plant Pathol. 110, 473–479 (2004).ADS 
    CAS 

    Google Scholar 
    O’Donnell, K. et al. Phylogenetic diversity of insecticolous fusaria inferred from multilocus DNA sequence data and their molecular identification via FUSARIUM-ID and FUSARIUM MLST. Mycologia 104, 427–445 (2012).PubMed 

    Google Scholar 
    Chehri, K., Salleh, B. & Zakaria, L. Morphological and phylogenetic analysis of Fusarium solani species complex in Malaysia. Microb. Ecol. 69, 457–471 (2015).PubMed 

    Google Scholar 
    Lanfear, R., Frandsen, P., Wright, A., Senfeld, T. & Calcott, B. PartionFinder 2: new methods for selecting partioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. https://doi.org/10.1093/molbev/msw260 (2016).Article 

    Google Scholar 
    Ronquist, F. et al. Efficient Bayesian phylogenetic inference and model selection across a large model space. Syst. Biol. 61, 539–542 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Leslie, J. F. & Summerell, B. A. The Fusarium Laboratory manual (Blackwell Publishing, Hoboken, 2006).
    Google Scholar 
    Fisher, N. L., Burgess, L. W., Toussoun, T. A. & Nelson, P. E. Carnation leaves as a substrate and for preserving cultures of Fusarium species. Phytopathology 72, 151 (1982).
    Google Scholar 
    Smyth, C. W. et al. Unraveling the ecology and epidemiology of an emerging fungal disease, sea turtle egg fusariosis (STEF). PLOS Pathog. 15, e1007682 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rachowicz, L. J. et al. The novel and endemic pathogen hypotheses: Competing explanations for the origin of emerging infectious diseases of wildlife. Conserv. Biol. 19, 1441–1448 (2005).
    Google Scholar 
    Lombard, L., Sandoval-Denis, M., Cai, L. & Crous, P. W. Changing the game: resolving systematic issues in key Fusarium species complexes. Persoonia Mol. Phylogeny Evol. Fungi 43, i–ii (2019).CAS 

    Google Scholar 
    Short, D. P. G., Donnell, K. O., Zhang, N., Juba, J. H. & Geiser, D. M. Widespread occurrence of diverse human pathogenic types of the fungus Fusarium detected in plumbing drains. J. Clin. Microbiol. 49, 4264–4272 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    White, T. J., Burns, T., Lee, S. & Taylor, J. Amplification and direct identification of fungal ribosomal RNA genes for phylogenetics. In PCR Protocols: a guide to methods and applications (eds Innis, M. A. et al.) 315–322 (Academic Press, San Diego, 1990).
    Google Scholar 
    Sekimoto, S., Hatai, K. & Honda, D. Molecular phylogeny of an unidentified Haliphthoros-like marine oomycete and Haliphthoros milfordensis inferred from nuclear-encoded small- and large-subunit rRNA genes and mitochondrial-encoded cox2 gene. Mycoscience 48, 212–221 (2007).CAS 

    Google Scholar 
    Petersen, A. B. & Rosendahl, S. Ø. Phylogeny of the Peronosporomycetes (Oomycota) based on partial sequences of the large ribosomal subunit (LSU rDNA). Mycol. Res. 104, 1295–1303 (2000).CAS 

    Google Scholar 
    O’Donnell, K. et al. Phylogenetic diversity and microsphere array-based genotyping of human pathogenic fusaria, including isolates from the multistate contact lens-associated U.S. keratitis outbreaks of 2005 and 2006. J. Clin. Microbiol. 45, 2235–2248 (2007).PubMed 
    PubMed Central 

    Google Scholar 
    Migheli, Q. et al. Molecular Phylogenetic diversity of dermatologic and other human pathogenic fusarial isolates from hospitals in Northern and Central Italy. J. Clin. Microbiol. 48, 1076–1084 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Rare species disproportionally contribute to functional diversity in managed forests

    Hooper, D. U. et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75, 3–35 (2005).Article 

    Google Scholar 
    Schleuter, D., Daufresne, M., Massol, F. & Argillier, C. A user’s guide to functional diversity indices. Ecol. Monogr. 80, 469–484 (2010).Article 

    Google Scholar 
    Petchey, O. L. & Gaston, K. J. Extinction and the loss of functional diversity. Proc. R. Soc. B Biol. Sci. 269, 1721–1727 (2002).Article 

    Google Scholar 
    Tilman, D. et al. The influence of functional diversity and composition on ecosystem processes. Science (80-. ). 277, 1300–1302 (1997).Díaz, S. & Cabido, M. Vive la différence: Plant functional diversity matters to ecosystem processes. Trends Ecol. Evol. 16, 646–655 (2001).Article 

    Google Scholar 
    Tilman, D. Functional diversity. in Encyclopedia of Biodiversity, Volume 3 (ed. Levin, S. A.) 109–120 (Academic Press, 2001).McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).PubMed 
    Article 

    Google Scholar 
    Cadotte, M. W., Carscadden, K. & Mirotchnick, N. Beyond species: Functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48, 1079–1087 (2011).Article 

    Google Scholar 
    Petchey, O. L., Hector, A. & Gaston, K. J. How do different measures of functional diversity perform?. Ecology 85, 847–857 (2004).Article 

    Google Scholar 
    Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).CAS 
    Article 
    ADS 

    Google Scholar 
    Petchey, O. L. & Gaston, K. J. Functional diversity (FD), species richness and community composition. Ecol. Lett. 5, 402–411 (2002).Article 

    Google Scholar 
    Halpern, B. S. & Floeter, S. R. Functional diversity responses to changing species richness in reef fish communities. Mar. Ecol. Prog. Ser. 364, 147–156 (2008).Article 
    ADS 

    Google Scholar 
    Seymour, C. L., Simmons, R. E., Joseph, G. S. & Slingsby, J. A. On bird functional diversity: Species richness and functional differentiation show contrasting responses to rainfall and vegetation structure in an arid landscape. Ecosystems 18, 971–984 (2015).Article 

    Google Scholar 
    Müller, J., Jarzabek-Müller, A., Bussler, H. & Gossner, M. M. Hollow beech trees identified as keystone structures for saproxylic beetles by analyses of functional and phylogenetic diversity. Anim. Conserv. 17, 154–162 (2014).Article 

    Google Scholar 
    Ulrich, W. et al. Species assortment or habitat filtering: A case study of spider communities on lake islands. Ecol. Res. 25, 375–381 (2010).Article 

    Google Scholar 
    Mouillot, D., Dumay, O. & Tomasini, J. A. Limiting similarity, niche filtering and functional diversity in coastal lagoon fish communities. Estuar. Coast. Shelf Sci. 71, 443–456 (2007).Article 
    ADS 

    Google Scholar 
    Cadotte, M. W. & Tucker, C. M. Should environmental filtering be abandoned?. Trends Ecol. Evol. 32, 429–437 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Flynn, D. F. B. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Rader, R., Bartomeus, I., Tylianakis, J. M. & Laliberté, E. The winners and losers of land use intensification: Pollinator community disassembly is non-random and alters functional diversity. Divers. Distrib. 20, 908–917 (2014).Article 

    Google Scholar 
    Sol, D. et al. The worldwide impact of urbanisation on avian functional diversity. Ecol. Lett. 23, 962–972 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Bihn, J. H., Gebauer, G. & Brandl, R. Loss of functional diversity of ant assemblages in secondary tropical forests. Ecology 91, 782–792 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Balestrieri, R. et al. A guild-based approach to assessing the influence of beech forest structure on bird communities. For. Ecol. Manage. 356, 216–223 (2015).Article 

    Google Scholar 
    Basile, M., Mikusiński, G. & Storch, I. Bird guilds show different responses to tree retention levels: A meta-analysis. Glob. Ecol. Conserv. 18, e00615 (2019).Article 

    Google Scholar 
    Czeszczewik, D. et al. Effects of forest management on bird assemblages in the Bialowieza Forest, Poland. iForest – Biogeosciences For. 8, 377–385 (2015).Article 

    Google Scholar 
    Wesołowski, T. Primeval conditions—What can we learn from them? Ibis (Lond. 1859). 149, 64–77 (2007).Paillet, Y. et al. Biodiversity differences between managed and unmanaged forests: meta-analysis of species richness in europe. Conserv. Biol. 24, 101–112 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Götzenberger, L. et al. Ecological assembly rules in plant communities-approaches, patterns and prospects. Biol. Rev. 87, 111–127 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Fox, J. W. & Kerr, B. Analyzing the effects of species gain and loss on ecosystem function using the extended Price equation partition. Oikos 121, 290–298 (2012).Article 

    Google Scholar 
    Fox, J. W. Using the Price Equations to partition the effects of biodiversity loss on ecosystem function. Ecology 87, 2687–2696 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Winfree, R. W., Fox, J., Williams, N. M., Reilly, J. R. & Cariveau, D. P. Abundance of common species, not species richness, drives delivery of a real-world ecosystem service. Ecol. Lett. 18, 626–635 (2015).PubMed 
    Article 

    Google Scholar 
    Storch, I. et al. Evaluating the effectiveness of retention forestry to enhance biodiversity in production forests of Central Europe using an interdisciplinary, multi‐scale approach. Ecol. Evol. ece3.6003 (2020) https://doi.org/10.1002/ece3.6003.Pommerening, A. & Murphy, S. T. A review of the history, definitions and methods of continuous cover forestry with special attention to afforestation and restocking. Forestry 77, 27–44 (2004).Article 

    Google Scholar 
    Bauhus, J., Puettmannn, K. J. & Kühne, C. Close-to-nature forest management in Europe: does it support complexity and adaptability of forest ecosystems? in Managing Forests as Complex Adaptive Systems: Building Resilience to the Challenge of Global Change 187–213 (Routledge/The Earthscan Forest Library, 2013). https://doi.org/10.4324/9780203122808.Bauhus, J., Puettmannn, K. J. & Kühne, C. Is Close-to-Nature Forest Management in Europe Compatible with Managing Forests as Complex Adaptive Forest Ecosystems? in Managing Forests as Complex Adaptive Systems: Building Resilience to the Challenge of Global Change (eds. Messier, C., Puettmannn, K. J. & Coates, K. D.) 187–213 (Routledge/The Earthscan Forest Library, 2013).Balestrieri, R., Basile, M., Posillico, M., Altea, T. & Matteucci, G. Survey effort requirements for bird community assessment in forest habitats. Acta Ornithol. 52, 1–9 (2017).Article 

    Google Scholar 
    Wilman, H. et al. EltonTraits 1.0: Species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).Article 

    Google Scholar 
    Laliberte, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).PubMed 
    Article 

    Google Scholar 
    Gower, J. C. A general coefficient of similarity and some of its properties. Biometrics 27, 857 (1971).Article 

    Google Scholar 
    Kahl, T. & Bauhus, J. An index of forest management intensity based on assessment of harvested tree volume, tree species composition and dead wood origin. Nat. Conserv. 7, 15–27 (2014).Article 

    Google Scholar 
    Paillet, Y. et al. Quantifying the recovery of old-growth attributes in forest reserves: A first reference for France. For. Ecol. Manage. 346, 51–64 (2015).Article 

    Google Scholar 
    Burrascano, S., Lombardi, F. & Marchetti, M. Old-growth forest structure and deadwood: Are they indicators of plant species composition? A case study from central Italy. Plant Biosyst. 142, 313–323 (2008).Article 

    Google Scholar 
    Van Wagner, C. E. Practical aspects of the line intersect method. (Minister of Supply and Services Canada, 1982).Larrieu, L. et al. Tree related microhabitats in temperate and Mediterranean European forests: A hierarchical typology for inventory standardization. Ecol. Indic. 84, 194–207 (2018).Article 

    Google Scholar 
    Asbeck, T., Pyttel, P., Frey, J. & Bauhus, J. Predicting abundance and diversity of tree-related microhabitats in Central European montane forests from common forest attributes. For. Ecol. Manage. 432, 400–408 (2019).Article 

    Google Scholar 
    Paillet, Y. et al. The indicator side of tree microhabitats: A multi-taxon approach based on bats, birds and saproxylic beetles. J. Appl. Ecol. https://doi.org/10.1111/1365-2664.13181 (2018).Article 

    Google Scholar 
    Basile, M. et al. What do tree-related microhabitats tell us about the abundance of forest-dwelling bats, birds, and insects?. J. Environ. Manage. 264, 110401 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Wang, Q., Adiku, S., Tenhunen, J. & Granier, A. On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sens. Environ. 94, 244–255 (2005).Article 
    ADS 

    Google Scholar 
    Rafique, R., Zhao, F., De Jong, R., Zeng, N. & Asrar, G. R. Global and regional variability and change in terrestrial ecosystems net primary production and NDVI: A model-data comparison. Remote Sens. 8, 1–16 (2016).Article 

    Google Scholar 
    Bates, D. et al. Package ‘lme4’. R Found. Stat. Comput. Vienna 12, (2014).Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed effects models and extensions in ecology with R. (Springer, New York, 2009). https://doi.org/10.1007/978-0-387-87458-6.Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. (2019).R Core Team. R: A language and environment for statistical computing. (2021).Mayfield, M. M. et al. What does species richness tell us about functional trait diversity? Predictions and evidence for responses of species and functional trait diversity to land-use change. Glob. Ecol. Biogeogr. 19, 423–431 (2010).
    Google Scholar 
    Pavoine, S. & Bonsall, M. B. Measuring biodiversity to explain community assembly: a unified approach. Biol. Rev. 86, 792–812 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Mayfield, M. M., Boni, M. F., Daily, G. C. & Ackerly, D. Species and functional diversity of natie and human-dominated plant communities. Ecology 86, 2365–2372 (2005).Article 

    Google Scholar 
    Holdaway, R. J. & Sparrow, A. D. Assembly rules operating along a primary riverbed-grassland successional sequence. J. Ecol. 94, 1092–1102 (2006).Article 

    Google Scholar 
    Matuoka, M. A., Benchimol, M., de Almeida-Rocha, J. M. & Morante-Filho, J. C. Effects of anthropogenic disturbances on bird functional diversity: A global meta-analysis. Ecol. Indic. 116, 106471 (2020).Article 

    Google Scholar 
    Leaver, J., Mulvaney, J., Ehlers-Smith, D. A., Ehlers-Smith, Y. C. & Cherry, M. I. Response of bird functional diversity to forest product harvesting in the Eastern Cape, South Africa. For. Ecol. Manage. 445, 82–95 (2019).Article 

    Google Scholar 
    Poos, M. S., Walker, S. C. & Jackson, D. A. Functional-diversity indices can be driven by methodological choices and species richness. Ecology 90, 341–347 (2009).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Mayfield, M. M., Boni, M. F., Daily, G. C. & Ackerly, D. Species and functional diversity of native and human-dominated plant communities. Ecology 86, 2365–2372 (2005).Article 

    Google Scholar 
    Tsianou, M. A. & Kallimanis, A. S. Different species traits produce diverse spatial functional diversity patterns of amphibians. Biodivers. Conserv. 25, 117–132 (2016).Article 

    Google Scholar 
    Gregory, R. D., Skorpilova, J., Vorisek, P. & Butler, S. An analysis of trends, uncertainty and species selection shows contrasting trends of widespread forest and farmland birds in Europe. Ecol. Indic. 103, 676–687 (2019).Article 

    Google Scholar 
    Peña, R. et al. Biodiversity components mediate the response to forest loss and the effect on ecological processes of plant–frugivore assemblages. Funct. Ecol. 34, 1257–1267 (2020).Article 

    Google Scholar 
    Chase, J. M., Blowes, S. A., Knight, T. M., Gerstner, K. & May, F. Ecosystem decay exacerbates biodiversity loss with habitat loss. Nature 584, 238–243 (2020).CAS 
    PubMed 
    Article 
    ADS 
    PubMed Central 

    Google Scholar 
    Fedrowitz, K. et al. Can retention forestry help conserve biodiversity? A meta-analysis. J. Appl. Ecol. 51, 1669–1679 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Horák, J. et al. Green desert?: Biodiversity patterns in forest plantations. For. Ecol. Manage. 433, 343–348 (2019).Article 

    Google Scholar 
    Ameztegui, A. et al. Bird community response in mountain pine forests of the Pyrenees managed under a shelterwood system. For. Ecol. Manage. 407, 95–105 (2017).Article 

    Google Scholar 
    Basile, M., Balestrieri, R., de Groot, M., Flajšman, K. & Posillico, M. Conservation of birds as a function of forestry. Ital. J. Agron. 11, 42–48 (2016).
    Google Scholar 
    Uezu, A. & Metzger, J. P. Vanishing bird species in the Atlantic Forest: Relative importance of landscape configuration, forest structure and species characteristics. Biodivers. Conserv. 20, 3627–3643 (2011).Article 

    Google Scholar 
    Endenburg, S. et al. The homogenizing influence of agriculture on forest bird communities at landscape scales. Landsc. Ecol. 34, 1–15 (2019).Article 

    Google Scholar 
    Reif, J. et al. Changes in bird community composition in the Czech Republic from 1982 to 2004: Increasing biotic homogenization, impacts of warming climate, but no trend in species richness. J. Ornithol. 154, 359–370 (2013).Article 

    Google Scholar 
    Morelli, F. et al. Evidence of evolutionary homogenization of bird communities in urban environments across Europe. Glob. Ecol. Biogeogr. 25, 1284–1293 (2016).Article 

    Google Scholar 
    Devictor, V., Julliard, R., Couvet, D., Lee, A. & Jiguet, F. Functional homogenization effect of urbanization on bird communities. Conserv. Biol. 21, 741–751 (2007).PubMed 
    Article 

    Google Scholar 
    Doxa, A., Paracchini, M. L., Pointereau, P., Devictor, V. & Jiguet, F. Preventing biotic homogenization of farmland bird communities: The role of High Nature Value farmland. Agric. Ecosyst. Environ. 148, 83–88 (2012).Article 

    Google Scholar 
    Van Turnhout, C. A. M., Foppen, R. P. B., Leuven, R. S. E. W., Siepel, H. & Esselink, H. Scale-dependent homogenization: Changes in breeding bird diversity in the Netherlands over a 25-year period. Biol. Conserv. 134, 505–516 (2007).Article 

    Google Scholar 
    Clavero, M. & Brotons, L. Functional homogenization of bird communities along habitat gradients: Accounting for niche multidimensionality. Glob. Ecol. Biogeogr. 19, 684–696 (2010).
    Google Scholar 
    Gustafsson, L. et al. Retention as an integrated biodiversity conservation approach for continuous-cover forestry in Europe. Ambio 49, 85–97 (2020).PubMed 
    Article 

    Google Scholar 
    Lelli, C. et al. Biodiversity response to forest structure and management: Comparing species richness, conservation relevant species and functional diversity as metrics in forest conservation. For. Ecol. Manage. 432, 707–717 (2019).Article 

    Google Scholar 
    Aquilué, N., Messier, C., Martins, K. T., Dumais-Lalonde, V. & Mina, M. A simple-to-use management approach to boost adaptive capacity of forests to global uncertainty. For. Ecol. Manage. 481, (2021).Manes, F., Ricotta, C., Salvatori, E., Bajocco, S. & Blasi, C. A multiscale analysis of canopy structure in Fagus sylvatica L. and Quercus cerris L. old-growth forests in the Cilento and Vallo di Diano National Park. Plant Biosyst. 144, 202–210 (2010).Article 

    Google Scholar 
    Tscharntke, T. et al. Landscape moderation of biodiversity patterns and processes – eight hypotheses. Biol. Rev. 87, 661–685 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    Kirsch, J.-J. et al. The use of water-filled tree holes by vertebrates in temperate forests. Wildlife Biol. 2021, wlb.00786
    (2021). More

  • in

    Artificial shelters provide suitable thermal habitat for a cold-blooded animal

    Ellis, E. C., Beusen, A. H. W. & Goldewijk, K. K. Anthropogenic Biomes: 10,000 BCE to 2015 CE. Land. 9(5), 129 (2020).Article 

    Google Scholar 
    Seto, K. C., Guneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. PNAS. 109, 16083–8 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Doherty, T. S., Hays, G. C. & Driscoll, D. A. Human disturbance causes widespread disruption of animal movement. Nat. Ecol. Evol. 5, 513–519 (2021).PubMed 
    Article 

    Google Scholar 
    Frid, A. & Dill, L. Human-caused disturbance stimuli as a form of predation risk. Conserv. Ecol. 6, 11 (2002).
    Google Scholar 
    Rodgers, J. A. & Schwikert, S. T. Buffer-zone distances to protect foraging and loafing waterbirds from disturbance by personal watercraft and outboard-powered boats. Conserv. Bio. 16, 216–224 (2002).Article 

    Google Scholar 
    Constantine, R., Brunton, D. H. & Dennis, T. Dolphin-watching tour boats change bottlenose dolphin (Tursiops truncates) behaviour. Biol. Conserv. 117, 299–307 (2004).Article 

    Google Scholar 
    Gill, J. A., Sutherland, W. J. & Watkinson, A. R. A method to quantify the effects of human disturbance on animal populations. J. Appl. Ecol. 33, 786–792 (1996).Article 

    Google Scholar 
    King, J. M. & Heinen, J. T. An assessment of the behaviors of overwintering manatees as influenced by interactions with tourists at two sites in central Florida. Biol. Conserv 117, 227–234 (2004).Article 

    Google Scholar 
    Stockwell, C. A., Bateman, G. C. & Berger, J. Conflicts in national parks: A case study of helicopters and bighorn sheep time budgets at the Grand Canyon. Biol. Conserv 56, 317–328 (1991).Article 

    Google Scholar 
    Diamond, J. M. The design of a nature reserve system for Indone-Asian New Guinea. In Conservation Biology: The Science of Scarcity and Cliversity (ed. Soule, M.) 485–503 (Sinauer, Sunderland, Massachusetts, 1986).Ceballos, G., García, A. & Ehrlich, P. R. The sixth extinction crisis loss of animal populations and species. J. Cosmol. 8, 1821–1831 (2010).
    Google Scholar 
    Kerr, J. T. & Deguise, I. Habitat loss and the limits to endangered species recovery. Ecol. Lett. 7, 1163–1169 (2004).Article 

    Google Scholar 
    Mbora, D. N. M. & McPeek, M. A. Host density and human activities mediate increased parasite prevalence and richness in primates threatened by habitat loss and fragmentation. J. Anim. Ecol. 78, 210–218 (2009).PubMed 
    Article 

    Google Scholar 
    Low, T. The New Nature (Penguin Books Limited, 2003).
    Google Scholar 
    Baxter-Gilbert, J., Riley, J. L. & Measey, J. Fortune favors the bold toad: Urban-derived behavioral traits may provide advantages for invasive amphibian populations. Behav. Ecol. Sociobiol. 75, 130 (2021).Article 

    Google Scholar 
    Coleman, J. L. & Barclay, R. M. R. Prey availability and foraging activity of grassland bats in relation to urbanization. J. Mammal. 94, 1111–1122 (2013).Article 

    Google Scholar 
    Castellano, M. J. & Valone, T. J. Effects of livestock removal and perennial grass recovery on the lizards of a desertified arid grassland. J. Arid Environ. 66, 87e95 (2006).Article 

    Google Scholar 
    Huey, R. B. Temperature, physiology, and the ecology of reptiles. In Biology of the Reptilia (eds. Gans, C., & Pough, F.H.) Vol. 12. (Academic Press, London, 1982).White, D. et al. Assessing risks to biodiversity from future landscape change. Conserv. Biol. 11, 349360 (1997).Article 

    Google Scholar 
    Carpio, A. J., Oteros, J., Tortosa, F. S. & Guerrero-Casado, J. Land use and biodiversity patterns of the herpetofauna: The role of olive groves. Acta Oecol. 70, 103–111 (2016).Article 

    Google Scholar 
    Geyle, H. M., Tingley, R., Amey, A. P. & Chapple, D. G. Reptiles on the brink: Identifying the Australian terrestrial snake and lizard species most at risk of extinction. Pac. Conserv. Biol. 27, 3–12 (2021).Article 

    Google Scholar 
    Doherty, T. S. et al. Reptile responses to anthropogenic habitat modification: A global meta-analysis. Glob. Ecol. Biogeogr. 29(7), 1265–1279 (2020).Article 

    Google Scholar 
    Hu, Y., Doherty, T. S. & Jessop, T. S. How influential are squamate reptile traits in explaining population responses to environmental disturbances?. Wildl. Res. 47(3), 249–259 (2020).Article 

    Google Scholar 
    Poole, G. & Berman, C. An ecological perspective on in-stream temperature: natural heat dynamics and mechanisms of human-caused thermal degradation. Environ. Manag. 27, 787–802 (2001).CAS 
    Article 

    Google Scholar 
    Tang, X. et al. Human activities enhance radiation forcing through surface albedo associated with vegetation in beijing. Remote Sens. 12(5), 837 (2020).Article 

    Google Scholar 
    Barna, A., Masum, A. K. M., Hossain, M. E., Bahadur, E.H. & Alam, M. S. A study on human activity recognition using gyroscope, accelerometer, temperature and humidity data. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–6 (2019).Moore, M. & Seigel, R. A. No place to nest or bask: Effects of human disturbance on the nesting and basking habits of yellow-blotched map turtles (Graptemys flavimaculata). J. Biol. Conserv. 130(3), 386–393 (2006).Article 

    Google Scholar 
    Bonnet, X., Naulleau, G. & Shine, R. The dangers of leaving home: Dispersal and mortality in snakes. Biol. Conserv. 89(1), 39–50 (1999).Article 

    Google Scholar 
    Haxton, T. Road mortality of Snapping Turtles, Chelydra serpentina, in central Ontario during their nesting period. Can. Field-Nat. 114(1), 106–110 (2000).
    Google Scholar 
    Koenig, J., Shine, R. & Shea, G. L. The ecology of an Australian reptile icon: How do blue-tongued lizards (Tiliqua scincoides) survive in suburbia?. Wildl. Res. 28(3), 214–227 (2001).Article 

    Google Scholar 
    Uetz, P. How many Reptile species?. Herpetol. Rev. 31, 13–15 (2000).
    Google Scholar 
    Todd, R. L., Steven, P., Rowland, G. & Oldham, G. Herpetological observations from field expeditions to North Karnataka and Southwest Maharashtra, India. Herpetol. Bull. 112, 17–37 (2010).
    Google Scholar 
    Sathish Kumar, V. M. The conservation of Indian Reptiles: An approach with molecular aspects. Reptile Rap. 14, 2–8 (2012).
    Google Scholar 
    Berryman, A. A. & Hawkins, B. A. The refuge as an integrating concept in ecology and evolution. Oikos. 115, 92–196 (2006).Article 

    Google Scholar 
    Webb, J. K., Pringle, R. M. & Shine, R. How do nocturnal snakes select diurnal retreat sites?. Copeia 2004, 919–925 (2004).Article 

    Google Scholar 
    Skinner, M. & Miller, N. Aggregation and social interaction in garter snakes (Thamnophis sirtalis sirtalis). Behav. Ecol. Sociobiol. 74, 51 (2020).Article 

    Google Scholar 
    Aubret, F. & Shine, R. Causes and consequences of aggregation by neonatal tiger snakes (Notechis scutatus, Elapidae). Austral Ecol. 34(2), 210–217 (2009).Article 

    Google Scholar 
    Myres, B. & Eells, M. Thermal aggregation in Boa constrictor. Herpetologica 24(1), 61–66 (1968).
    Google Scholar 
    Parrish, J. K. & Edelstein-keshet, L. Coinplexity, pattern, and evolutionary trade-offs in animal aggregation. Science 284, 99–101 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    Trevesa, A. Theory and method in studies of vigilance and aggregation. Anim. Behav. 60, 711–722 (2000).Article 

    Google Scholar 
    Greene, H. W. Snakes (University of California Press, 1997).Book 

    Google Scholar 
    Huey, R. B., Peterson, C. R., Arnold, S. J. & Porter, W. P. Hot rocks and not-so-hot rocks: Retreat-site selection by garter snakes and its thermal consequences. Ecology 70, 931–944 (1989).Article 

    Google Scholar 
    Christian, K. & Weavers, B. Analysis of activity and energetics of the lizard Varanus rosenbergi. Copeia 1994, 289–295 (1994).Article 

    Google Scholar 
    Autumn, K. & de Nardo, D. F. Behavioural thermoregulation increases growth rate in nocturnal lizard. J. Herpetol. 29, 157–162 (1995).Article 

    Google Scholar 
    Milne, T., Bull, C. M. & Hutchinson, M. N. Use of burrows by the endangered pygmy blue-tongue lizard, Tiliqua adelaidensis (Scincidae). Wildl. Res. 30, 523–528 (2003).Article 

    Google Scholar 
    Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. PNAS. 111, 5610–5615 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kearney, M., Shine, R. & Porter, W. P. The potential for behavioral thermoregulation to buffer ‘“cold-blooded”’ animals against climate warming. PNAS 106, 3835–3840 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stevenson, D. J., Dyer, K. J. & Willis-Stevenson, B. A. Survey and monitoring of the eastern indigo snake in georgia. Southeast. Nat. 2(3), 393–408 (2003).Article 

    Google Scholar 
    Zappalorti, R. T. & Reinert, H. K. Artificial refugia as a habitat-improvement strategy for snake conservation. Contrib. Herpetol. 11, 369–375 (1994).
    Google Scholar 
    Griffith, B., Scott, J. M., Carpenter, J. W. & Reed, C. Translocation as a species conservation tool: Status and strategy. Science 245, 477–480 (1989).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mullin, S. J. Snakes Ecology and Conservation (eds. Stephen, J. M. & Richard, A. S.). (Cornell University Press, 2011).Lei, J., Booth, D. T. & Dwyer, R. G. Spatial ecology of yellow-spotted goannas adjacent to a sea turtle nesting beach. Aust. J. Zool. 65, 77–86 (2017).Article 

    Google Scholar 
    Ermi, Z. Snakes of China. (Anhui Science and Technology Press, 2006).Schulz, K. D. A Monograph of the Colubrid Snakes of the Genus Elaphe Fitzinger (Czech Republic, Koeltz Scientific Books, 1996).
    Google Scholar 
    Pallas, P. S. Reise durch verschiedene Provinzen des Russischen Reiches, Vol. 2. 744 (Kaiserl. Akad. Wiss., St. Petersburg, 1773).Auffenberg, W., Arian, Q. N. & Kurshid, N. Preferred habitat, home range and movement patterns of Varanus bengalensis in southern Pakistan. Mertensiella 2, 7–28 (1991).
    Google Scholar 
    McDiarmid, R. W. Reptile Biodiversity: Standard Methods for Inventory and Monitoring. (University of California Press, 2002).Riley, J. L., Baxter-gilbert, J. H. & Litzgus, J. D. A comparison of three external transmitter attachment methods for snakes. Wildl. Soc. Bull. 41(1), 132–139 (2017).Article 

    Google Scholar 
    Meine, C., & Archibald, G. The Cranes: Status Survey and Conservation Action Plan (IUCN, 1996).Mori, A. & Toda, M. Body temperature of subtropical snakes at night: How cold is their blood?. Curr. Herpetol. 37(2), 151–157 (2018).Article 

    Google Scholar 
    Crane, M., Silva, I., Marshall, B. M. & Strine, C. T. Lots of movement, little progress: A review of reptile home range literature. PeerJ 9, e11742 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Calabrese, J. M., Fleming, C. H. & Gurarie, E. ctmm: An R package for analyzing animal relocation data as a continuous-time stochastic process. Methods Ecol. Evol. 7, 1124–1132 (2016).Article 

    Google Scholar 
    Fleming, C. H. & Calabrese, J. M. A new kernel density estimator for accurate home-range and species-range area estimation. Methods Ecol. Evol. 8, 571–579 (2017).Article 

    Google Scholar 
    Fleming, C. H. et al. From fine-scale foraging to home ranges: A semivariance approach to identifying movement modes across spatiotemporal scales. Am. Nat. 183, 154–167 (2014).Article 

    Google Scholar 
    Fleming, C. H., Noonan, M. J., Medici, E. P. & Calabrese, J. M. Overcoming the challenge of small effective sample sizes in home-range estimation. Methods Ecol. Evol. 10, 1679–1689 (2019).Article 

    Google Scholar 
    Uhlenbeck, G. E. & Ornstein, L. S. On the theory of the Brownian motion. Phys. Rev. 36, 823 (1930).CAS 
    MATH 
    Article 

    Google Scholar 
    Bürkner, P. C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).Article 

    Google Scholar 
    Bhattacharyya, A. On a measure of divergence between two statistical populations defined by their probability distributions. News Bull. Calcutta Math. Soc. 35, 99–109 (1943).MathSciNet 
    MATH 

    Google Scholar 
    Winner, K. et al. Statistical inference for home range overlap. Methods Ecol. Evol. 9, 1679–1691 (2018).Article 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2016). http://www.R-project.org. Accessed September 2022.Calenge, A. The package ‘“adehabitat”’ for the R software: Tool for the analysis of space and habitat use by animals. Ecol. Modell. 197, 516–519 (2006).Article 

    Google Scholar 
    Manley, B. F. J., McDonald, L. L. & Thomas, D. L. Resource Selection by Animals: Statistical Design and Analysis for Field Studies (Chapman and Hall, 1993).Book 

    Google Scholar  More

  • in

    Cophylogeny and convergence shape holobiont evolution in sponge–microbe symbioses

    Hyman, L. H. The Invertebrates: Protozoa Through Ctenophora Vol. 1 (McGraw-Hill, 1940).Taylor, M. W., Radax, R., Steger, D. & Wagner, M. Sponge-associated microorganisms: evolution, ecology, and biotechnological potential. Microbiol. Mol. Biol. Rev. 71, 295–347 (2007).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Giles, E. C. et al. Bacterial community profiles in low microbial abundance sponges. FEMS Microbiol. Ecol. 83, 232–241 (2013).CAS 
    PubMed 

    Google Scholar 
    Gloeckner, V. et al. The HMA–LMA dichotomy revisited: an electron microscopical survey of 56 sponge species. Biol. Bull. 227, 78–88 (2014).PubMed 

    Google Scholar 
    Moitinho-Silva, L. et al. Predicting the HMA–LMA status in marine sponges by machine learning. Front. Microbiol. 8, 752 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Cárdenas, C. A. et al. High similarity in the microbiota of cold-water sponges of the genus Mycale from two different geographical areas. PeerJ 6, e4935 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Webster, N. S. & Taylor, M. W. Marine sponges and their microbial symbionts: love and other relationships. Environ. Microbiol. 14, 335–346 (2012).CAS 
    PubMed 

    Google Scholar 
    Freeman, C. J. et al. Microbial symbionts and ecological divergence of Caribbean sponges: a new perspective on an ancient association. ISME J. 14, 1571–1583 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Bell, J. J. et al. Climate change alterations to ecosystem dominance: how might sponge-dominated reefs function? Ecology 99, 1920–1931 (2018).PubMed 

    Google Scholar 
    Gardner, T. A., Côté, I. M., Gill, J. A., Grant, A. & Watkinson, A. R. Long-term region-wide declines in Caribbean corals. Science 301, 958–960 (2003).CAS 
    PubMed 

    Google Scholar 
    Lesser, M. P. Benthic–pelagic coupling on coral reefs: feeding and growth of Caribbean sponges. J. Exp. Mar. Biol. Ecol. 328, 277–288 (2006).
    Google Scholar 
    de Goeij, J. M., Lesser, M. P. & Pawlik, J. R. in Climate Change, Ocean Acidification and Sponges (eds Carballo, J. L. & Bell, J. J.) 373–410 (Springer, 2017); https://doi.org/10.1007/978-3-319-59008-0_8Pita, L., Rix, L., Slaby, B. M., Franke, A. & Hentschel, U. The sponge holobiont in a changing ocean: from microbes to ecosystems. Microbiome 6, 46 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Slaby, B. M., Hackl, T., Horn, H., Bayer, K. & Hentschel, U. Metagenomic binning of a marine sponge microbiome reveals unity in defense but metabolic specialization. ISME J. 11, 2465–2478 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Moitinho-Silva, L. et al. Revealing microbial functional activities in the Red Sea sponge Stylissa carteri by metatranscriptomics. Environ. Microbiol. 16, 3683–3698 (2014).CAS 
    PubMed 

    Google Scholar 
    Weisz, J. B., Lindquist, N. & Martens, C. S. Do associated microbial abundances impact marine demosponge pumping rates and tissue densities? Oecologia 155, 367–376 (2008).PubMed 

    Google Scholar 
    Poppell, E. et al. Sponge heterotrophic capacity and bacterial community structure in high- and low-microbial abundance sponges. Mar. Ecol. 35, 414–424 (2014).
    Google Scholar 
    McFall-Ngai, M. J. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl Acad. Sci. USA 110, 3229–3236 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Douglas, A. E. Symbiosis as a general principle in eukaryotic evolution. Cold Spring Harb. Perspect. Biol. 6, a016113 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Moran, N. A. & Sloan, D. B. The hologenome concept: helpful or hollow? PLoS Biol. 13, e1002311 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Brooks, A. W., Kohl, K. D., Brucker, R. M., van Opstal, E. J. & Bordenstein, S. R. Phylosymbiosis: relationships and functional effects of microbial communities across host evolutionary history. PLoS Biol. 14, e2000225–e2000229 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    O’Brien, P. A. et al. Diverse coral reef invertebrates exhibit patterns of phylosymbiosis. ISME J. 14, 2211–2222 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Houwenhuyse, S., Stoks, R., Mukherjee, S. & Decaestecker, E. Locally adapted gut microbiomes mediate host stress tolerance. ISME J. 15, 2401–2414 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Moeller, A. H. et al. Experimental evidence for adaptation to species-specific gut microbiota in house mice. mSphere 4, e00387-19 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    van Opstal, E. J. & Bordenstein, S. R. Phylosymbiosis impacts adaptive traits in Nasonia wasps. mBio https://doi.org/10.1128/mBio.00887-19 (2019).Lim, S. J. & Bordenstein, S. R. An introduction to phylosymbiosis. Proc. R. Soc. B https://doi.org/10.1098/rspb.2019.2900 (2020).Pollock, F. J. et al. Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny. Nat. Commun. https://doi.org/10.1038/s41467-018-07275-x (2018).Douglas, A. E. & Werren, J. H. Holes in the hologenome: why host–microbe symbioses are not holobionts. mBio 7, e02099 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hadfield, J. D., Krasnov, B. R., Poulin, R. & Nakagawa, S. A tale of two phylogenies: comparative analyses of ecological interactions. Am. Nat. 183, 174–187 (2014).PubMed 

    Google Scholar 
    Hill, M. S. et al. Reconstruction of family-level phylogenetic relationships within Demospongiae (Porifera) using nuclear encoded housekeeping genes. PLoS ONE 8, e50437 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Redmond, N. E. et al. Phylogeny and systematics of Demospongiae in light of new small-subunit ribosomal DNA (18S) sequences. Int. Comp. Biol. 53, 388–415 (2013).CAS 

    Google Scholar 
    Worheide, G. et al. in Advances in Marine Biology: Advances in Sponge Science Vol. 61 (eds Becerro, M. A. et al.) 1–78 (Elsevier, 2012).Schuster, A. et al. Divergence times in demosponges (Porifera): first insights from new mitogenomes and the inclusion of fossils in a birth–death clock model. BMC Evol. Biol. 18, 114 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Stanley, G. D. & Fautin, D. G. Paleontology and evolution. Orig. Mod. Corals Sci. 291, 1913–1914 (2001).CAS 

    Google Scholar 
    Brinkmann, C. M., Marker, A. & Kurtböke, D. I. An overview on marine sponge-symbiotic bacteria as unexhausted sources for natural product discovery. Diversity 9, 40 (2017).
    Google Scholar 
    Rust, M. et al. A multiproducer microbiome generates chemical diversity in the marine sponge Mycale hentscheli. Proc. Natl Acad. Sci. USA 117, 9508–9518 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Faulkner, D. J., Harper, M. K., Haygood, M. G., Salomon, C. E. & Schmidt, E. W. in Drugs from the Sea (ed. Fusetani, N.) 107–119 (Karger, 2000).Loh, T.-L. & Pawlik, J. R. Chemical defenses and resource trade-offs structure sponge communities on Caribbean coral reefs. Proc. Natl Acad. Sci. USA 111, 4151–4156 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pagel, M. Detecting correlated evolution on phylogenies—a general method for the comparative analysis of discrete characters. Proc. R. Soc. Lond. B 255, 37–45 (1994).
    Google Scholar 
    Easson, C. G. & Thacker, R. W. Phylogenetic signal in the community structure of host-specific microbiomes of tropical marine sponges. Front. Microbiol. 5, 532 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Thomas, T. et al. Diversity, structure and convergent evolution of the global sponge microbiome. Nat. Commun. 7, 11870 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Schöttner, S. et al. Relationships between host phylogeny, host type and bacterial community diversity in cold-water coral reef sponges. PLoS ONE 8, e55505 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Robinson, D. R. & Foulds, L. R. Comparison of phylogenetic trees. Math. Biosci. 53, 131–147 (1981).
    Google Scholar 
    Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Apprill, A. The role of symbioses in the adaptation and stress responses of marine organisms. Annu. Rev. Mar. Sci. 12, 291–314 (2020).
    Google Scholar 
    Lesser, M. P., Slattery, M. & Mobley, C. Biodiversity and functional ecology of mesophotic coral reefs. Annu. Rev. Ecol. Evol. Syst. 49, 49–71 (2018).
    Google Scholar 
    Lipps, J. H. & Stanley, G. D. in Coral Reefs at the Crossroads (eds Hubbard, D. K. et al.) 175–196 (Springer, 2016); https://doi.org/10.1007/978-94-017-7567-0_8Macartney, K. J., Slattery, M. & Lesser, M. P. Trophic ecology of Caribbean sponges in the mesophotic zone. Limnol. Oceanogr. 66, 1113–1124 (2021).CAS 

    Google Scholar 
    McMurray, S. E., Stubler, A. D., Erwin, P. M., Finelli, C. M. & Pawlik, J. R. A test of the sponge-loop hypothesis for emergent Caribbean reef sponges. Mar. Ecol. Prog. Ser. 588, 1–14 (2018).CAS 

    Google Scholar 
    Olinger, L. K., Strangman, W. K., McMurray, S. E. & Pawlik, J. R. Sponges with microbial symbionts transform dissolved organic matter and take up organohalides. Front. Mar. Sci. 8, 665789 (2021).
    Google Scholar 
    Haas, A. F. et al. Effects of coral reef benthic primary producers on dissolved organic carbon and microbial activity. PLoS ONE 6, e27973 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sánchez-Baracaldo, P. Origin of marine planktonic cyanobacteria. Sci. Rep. 5, 17418 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Sanchez-Bracaldo, P., Ridgwell, A. & Raven, J. A. A neoproterozoic transition in the marine nitrogen cycle. Curr. Biol. 24, 652–657 (2014).
    Google Scholar 
    Falkowski, P. G. et al. The evolution of modern eukaryotic phytoplankton. Science 305, 354–360 (2004).CAS 
    PubMed 

    Google Scholar 
    Wang, D. et al. Coupling of ocean redox and animal evolution during the Ediacaran–Cambrian transition. Nat. Commun. 9, 2575 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Bellwood, D. R., Goatley, C. H. R. & Bellwood, O. The evolution of fishes and corals on reefs: form, function and interdependence. Biol. Rev. 92, 878–901 (2017).PubMed 

    Google Scholar 
    Ehrlich, P. R. & Raven, P. H. Butterflies and plants: a study in coevolution. Evolution 18, 586–608 (1964).
    Google Scholar 
    Després, L., David, J.-P. & Gallet, C. The evolutionary ecology of insect resistance to plant chemicals. Trends Ecol. Evol. 22, 298–307 (2007).PubMed 

    Google Scholar 
    Richardson, K. L., Gold-Bouchot, G. & Schlenk, D. The characterization of cytosolic glutathione transferase from four species of sea turtles: loggerhead (Caretta caretta), green (Chelonia mydas), olive ridley (Lepidochelys olivacea), and hawksbill (Eretmochelys imbricata). Comp. Biochem. Physiol. C 150, 279–284 (2009).
    Google Scholar 
    Bayer, K., Jahn, M. T., Slaby, B. M., Moitinho-Silva, L. & Hentschel, U. Marine sponges as Chloroflexi hot spots: genomic insights and high-resolution visualization of an abundant and diverse symbiotic clade. mSystems 3, e00150-18 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Sachs, J. L., Skophammer, R. G., Bansal, N. & Stajich, J. E. Evolutionary origins and diversification of proteobacterial mutualists. Proc. R Soc. B https://doi.org/10.1098/rspb.2013.2146 (2014).Sachs, J. L., Skophammer, R. G. & Regus, J. U. Evolutionary transitions in bacterial symbiosis. Proc. Natl Acad. Sci. USA 108, 10800–10807 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Seutin, G., White, B. N. & Boag, P. T. Preservation of avian blood and tissue samples for DNA analyses. Can. J. Zool. https://doi.org/10.1139/z91-013 (2011).Sunagawa, S. et al. Generation and analysis of transcriptomic resources for a model system on the rise: the sea anemone Aiptasia pallida and its dinoflagellate endosymbiont. BMC Genomics 10, 258 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    Song, L. & Florea, L. Rcorrector: efficient and accurate error correction for Illumina RNA-seq reads. GigaScience 4, 48 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chevreux, B., Wetter, T. & Suhai, S. Genome sequence assembly using trace signals and additional sequence information. Comput. Sci. Biol. 99, 45–56 (1999).
    Google Scholar 
    Li, W. & Godzik, A. CD-HIT: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).CAS 

    Google Scholar 
    Francis, W. R. et al. The genome of the contractile demosponge Tethya wilhelma and the evolution of metazoan neural signalling pathways. Preprint at bioRxiv https://doi.org/10.1101/120998 (2017).Altschul, S. F. A protein alignment scoring system sensitive at all evolutionary distances. J. Mol. Evol. 36, 290–300 (1993).CAS 
    PubMed 

    Google Scholar 
    Srivastava, M. et al. The Amphimedon queenslandica genome and the evolution of animal complexity. Nature 466, 720–726 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Simion, P. et al. A large and consistent phylogenomic dataset supports sponges as the sister group to all other animals. Curr. Biol. https://doi.org/10.1016/j.cub.2017.02.031 (2017).Katoh, K., Misawa, K., Kuma, K.-I. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol. 17, 540–552 (2000).CAS 

    Google Scholar 
    Kalyaanamoorthy, S. et al. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stamatakis, A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22, 2688–2690 (2006).CAS 

    Google Scholar 
    Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 

    Google Scholar 
    Dohrmann, M. & Wörheide, G. Dating early animal evolution using phylogenomic data. Sci. Rep. 7, 3599 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Smith, S. A. & O’Meara, B. C. treePL: divergence time estimation using penalized likelihood for large phylogenies. Bioinformatics 28, 2689–2690 (2012).CAS 
    PubMed 

    Google Scholar 
    Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
    PubMed 

    Google Scholar 
    Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).
    Google Scholar 
    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
    PubMed 

    Google Scholar 
    McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-5 (2019).Lahti, L. et al. Tools for Microbiome Analysis in R. Microbiome package version 1.17.2 https://github.com/microbiome/microbiome (2017).Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E. & Challenger, W. GEIGER: investigating evolutionary radiations. Bioinformatics 24, 129–131 (2008).CAS 
    PubMed 

    Google Scholar 
    Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).CAS 

    Google Scholar 
    Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
    Google Scholar 
    Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).
    Google Scholar 
    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Westbrook, A. et al. PALADIN: protein alignment for functional profiling whole metagenome shotgun data. Bioinformatics 33, 1473–1478 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Waddell, B. & Pawlik, J. R. Defenses of Caribbean sponges against invertebrate predators. I. Assays with hermit crabs. Mar. Ecol. Prog. Ser. 195, 125–132 (2000).
    Google Scholar 
    Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. FEMS Microbiol. Ecol. 20, 289–290 (2004).CAS 

    Google Scholar 
    Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
    Google Scholar 
    Nakagawa, S., Johnson, P. C. D. & Schielzeth, H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R. Soc. Interface 14, 20170213 (2017).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    The impact of Tamarix invasion on the soil physicochemical properties

    Mack, R. N. et al. Biotic invasions: Causes, epidemiology, global consequences, and control. Ecol. Appl. 10(3), 689–710 (2000).
    Google Scholar 
    Pimentel, D. Biological invasionseconomic and environmental costs of alien plant, animal, and microbe species. No. 577.18 B5/2011. 2011.Jackson, T. Addressing the economic costs of invasive alien species: Some methodological and empirical issues. Int. J. Sustain. Soc. 7(3), 221–240 (2015).
    Google Scholar 
    Walker, B. H. & Steffen, W. L. Interactive and integrated effects of global change on terrestrial ecosystems. In The Terrestrial Biosphere and Global Change. Implications for Natural and Managed Ecosystems, Synthesis Volume. International Geosphere-Biosphere Program Book Series 4 (eds Walker, B. et al.) 329–375 (Cambridge University Press, 1999).
    Google Scholar 
    Wilcove, D. S., Rothstein, D., Dubow, J., Phillips, A. & Losos, E. Quantifying threats to imperiled species in the United States. Bioscience 48(8), 607–615 (1998).
    Google Scholar 
    Robinson, T. W. Introduction, Spread and Areal Extent of Saltcedar [Tamarix] in the Western States (No. 491) (US Government Printing Office, 1965).
    Google Scholar 
    Marlin, D., Newete, S. W., Mayonde, S. G., Smit, E. R. & Byrne, M. J. Invasive Tamarix (Tamaricaceae) in South Africa: Current research and the potential for biological control. Biol. Invasions 19(10), 2971–2992 (2017).
    Google Scholar 
    Pearce, C. M. & Smith, D. G. Saltcedar: Distribution, abundance, and dispersal mechanisms, northern Montana, USA. Wetlands 23(2), 215–228 (2003).
    Google Scholar 
    Newete, S. W., Mayonde, S. & Byrne, M. J. Distribution and abundance of invasive Tamarix genotypes in South Africa. Weed Res. 59(3), 191–200 (2019).CAS 

    Google Scholar 
    Chew, M. K. The monstering of tamarisk: How scientists made a plant into a problem. J. Hist. Biol. 42(2), 231–266 (2009).PubMed 

    Google Scholar 
    Richardson, D. M., Macdonald, I. A. W., Hoffmann, J. H. & Henderson, L. Alienplantinvasions. In The Vegetation of Southern Africa (eds Cowling, R. M. et al.) 535–570 (Cambridge University Press, 1997).
    Google Scholar 
    Ehrenfeld, J. G. Effects of exotic plant invasions on soil nutrient cycling processes. Ecosystems 6(6), 503–523 (2003).CAS 

    Google Scholar 
    Haubensak, K. A., D’Antonio, C. M. & Alexander, J. Effects of nitrogen-fixing shrubs in Washington and Coastal California1. Weed Technol. 18(sp1), 1475–1479 (2004).
    Google Scholar 
    Hawkes, C. V., Wren, I. F., Herman, D. J. & Firestone, M. K. Plant invasion alters nitrogen cycling by modifying the soil nitrifying community. Ecol. Lett. 8(9), 976–985 (2005).PubMed 

    Google Scholar 
    Kourtev, P. S., Ehrenfeld, J. G. & Häggblom, M. Exotic plant species alter the microbial community structure and function in the soil. Ecology 83(11), 3152–3166 (2002).
    Google Scholar 
    Saggar, S., McIntosh, P. D., Hedley, C. B. & Knicker, H. Changes in soil microbial biomass, metabolic quotient, and organic matter turnover under Hieracium (H. pilosella L.). Biol. Fertility Soils 30(3), 232–238 (1999).CAS 

    Google Scholar 
    Dudley, T. L., DeLoach, C. J., Levich, J. E. & Carruthers, R. I. Saltcedar invasion of western riparian areas: Impacts and new prospects for control. Trans. N. Am. Wildlife Nat. Resources Conf. 65, 345–381 (2000).
    Google Scholar 
    Algotsson, E. Biological diversity. In Environmental Management in South Africa 2nd edn (eds Strydom, H. A. & King, N. D.) 97–125 (Juta Cape Town, 2009).
    Google Scholar 
    Mayonde, S. G., Cron, G. V., Gaskin, J. F. & Byrne, M. J. Tamarix (Tamaricaceae) hybrids: The dominant invasive genotype in Southern Africa. Biol. Invasions 18(12), 3575–3594 (2016).
    Google Scholar 
    Corbin, J. D. & D’Antonio, C. M. Effects of exotic species on soil nitrogen cycling: Implications for restoration1. Weed Technol. 18(sp1), 1464–1468 (2004).CAS 

    Google Scholar 
    Marchante, E., Kjøller, A., Struwe, S. & Freitas, H. Soil recovery after removal of the N 2-fixing invasive Acacia longifolia: Consequences for ecosystem restoration. Biol. Invasions 11(4), 813–823 (2009).
    Google Scholar 
    Magadlela, D. & Mdzeke, N. Social benefits in the Working for Water programme as a public works initiative: Working for water. S. Afr. J. Sci. 100(1–2), 94–96 (2004).
    Google Scholar 
    Yelenik, S. G., Stock, W. D. & Richardson, D. M. Ecosystem level impacts of invasive Acacia saligna in the South African fynbos. Restor. Ecol. 12(1), 44–51 (2004).
    Google Scholar 
    Malcolm, G. M., Bush, D. S. & Rice, S. K. Soil nitrogen conditions approach preinvasion levels following restoration of nitrogen-fixing black locust (Robinia pseudoacacia) stands in a Pine-Oak Ecosystem. Restor. Ecol. 16(1), 70–78 (2008).
    Google Scholar 
    Maron, J. L. & Jefferies, R. L. Bush lupine mortality, altered resource availability, and alternative vegetation states. Ecology 80(2), 443–454 (1999).
    Google Scholar 
    AgriLASA (Agri Laboratory Association of Southern Africa). 2004. Soil handbook.Okalebo, J.R., Gathua, K.W. & Woomer, P.L. (2002). Laboratory methods of soil and plant analysis: A working manual second edition. Sacred Africa, Nairobi, 21.LECO. 2003. Truspec CN Carbon/Nitrogen Determinator Instructions Manual. LECO Corporation, St Joseph, USA.Suarez, D. L., Wood, J. D. & Lesch, S. M. Effect of SAR on water infiltration under a sequential rain–irrigation management system. Agric. Water Manag. 86(1–2), 150–164 (2006).
    Google Scholar 
    Dane, J.H., and Hopmans, JW. (2002). Water retention and storage. GC Method of soil analysis. SSSA book series. Madison, Wisconsin, USA. 1692, 671–720.Blakemore, L.C., Searle, P.L. and Daly, B.K. (1987). Methods for chemical analysis of soils. New Zealand Soil Bureau Scientific, Report 80. New Zealand, Lower Hutt: New Zealand Society of Soil Science, 103.Buckham, L.E. (2011). Contrasting growth traits and insect interactions of two Tamarix species and a hybrid (Tamaricaceae) used for mine rehabilitation in South Africa (Doctoral dissertation).Ladenburger, C. G., Hild, A. L., Kazmer, D. J. & Munn, L. C. Soil salinity patterns in Tamarix invasions in the Bighorn Basin, Wyoming, USA. J. Arid Environ. 65(1), 111–128 (2006).ADS 

    Google Scholar 
    Beukes, P. C. & Ellis, F. Soil and vegetation changes across a Succulent Karoo grazing gradient. Afr. J. Range Forage Sci. 20(1), 11–19 (2003).
    Google Scholar 
    Liu, M. et al. Monitoring the invasion of Spartina alterniflora using multi-source high-resolution imagery in the Zhangjiang Estuary, China. Remote Sensing 9(6), 539 (2017).ADS 

    Google Scholar 
    Newete, S. W., Abd Elbasit, M. A. & Araya, T. Soil salinity and moisture content under non-native Tamarix species. Int. J. Phytorem. 22(9), 931–938. https://doi.org/10.1080/15226514.2020.1774503 (2020).CAS 
    Article 

    Google Scholar 
    Whitford, W. G., Anderson, J. & Rice, P. M. Stemflow contribution to the ’fertile island’effect in creosotebush, Larrea tridentata. J. Arid Environ. 35(3), 451–457 (1997).ADS 

    Google Scholar 
    Li, C., Li, Y. & Ma, J. Spatial heterogeneity of soil chemical properties at fine scales induced by Haloxylon ammodendron (Chenopodiaceae) plants in a sandy desert. Ecol. Res. 26(2), 385–394 (2011).MathSciNet 
    CAS 

    Google Scholar 
    Sookbirsingh, R., Karina, C., Thomas, E.G. & Rusell, RC. (2010). Salt separation processes in the saltcedar Tamarix ramosissima (Lebed.). Commun Soil Sci Plant Anal. 41(10), 1271–1281.Newete, S.W., Allem, S.M., Venter, N. and Byrne, M.J. Tamarix efficiency in salt excretion and physiological tolerance to salt-induced stress in South Africa. Int. J. Phytoremediat. 1–7 (2019).Di Tomaso, J. M. Impact, biology, and ecology of saltcedar (Tamarix spp.) in the southwestern United States. Weed Technol. 12(2), 326–336 (1998).
    Google Scholar 
    Smith, S. D., Devitt, D. A., Sala, A., Cleverly, J. R. & Busch, D. E. Water relations of riparian plants from warm desert regions. Wetlands 18(4), 687–696 (1998).
    Google Scholar 
    Lesica, P. & DeLuca, T. H. Is tamarisk allelopathic?. Plant Soil 267(1–2), 357–365 (2004).CAS 

    Google Scholar 
    Bagstad, K. J., Lite, S. J. & Stromberg, J. C. Vegetation, soils, and hydrogeomorphology of riparian patch types of a dryland river. Western N. Am. Naturalist 66(1), 23–45 (2006).
    Google Scholar 
    Lehnhoff, E. A., Rew, L. J., Zabinski, C. A. & Menalled, F. D. Reduced impacts or a longer lag phase? Tamarix in the northwestern USA. Wetlands 32(3), 497–508 (2012).
    Google Scholar 
    Ye, W., Wang, H. X., Gao, J., Liu, H. J. & Yan, L. Simulation of salt ion migration in soil under reclaimed water irrigation. J. Agro-Environ. Sci. 33(5), 1007–1015 (2014).CAS 

    Google Scholar 
    Yang, S. C. et al. Characterization of soil salinization based on canonical correspondence analysis method in Gansu Yellow River irrigation district of Northwest China. Scientia Agricultura Sinica 47(1), 100–110 (2014).CAS 

    Google Scholar 
    Zhang, L. H., Chen, P. H., Li, J., Chen, X. B. & Feng, Y. Distribution of soil salt ions around Tamarix chinensis individuals in the Yellow River Delta. Acta Ecol. Sin. 36(18), 5741–5749 (2016).CAS 

    Google Scholar 
    Zhang, T., Zhan, X., He, J., Feng, H. & Kang, Y. Salt characteristics and soluble cations redistribution in an impermeable calcareous saline-sodic soil reclaimed with an improved drip irrigation. Agric. Water Manag. 197, 91–99 (2018).
    Google Scholar 
    Yin, C. H., Feng, G. U., Zhang, F., Tian, C. Y. & Tang, C. Enrichment of soil fertility and salinity by tamarisk in saline soils on the northern edge of the Taklamakan Desert. Agric. Water Manag. 97(12), 1978–1986 (2010).
    Google Scholar 
    Chaudhari, P. R., Ahire, D. V., Ahire, V. D., Chkravarty, M. & Maity, S. Soil bulk density as related to soil texture, organic matter content and available total nutrients of Coimbatore soil. Int. J. Sci. Res. Publ. 3(2), 1–8 (2013).CAS 

    Google Scholar 
    Tanveera, A., Kanth, T. A., Tali, P. A. & Naikoo, M. Relation of soil bulk density with texture, total organic matter content and porosity in the soils of Kandi Area of Kashmir valley, India. Int. Res. J. Earth Sci. 4(1), 1–6 (2016).
    Google Scholar 
    Sharma, B. & Bhattacharya, S. Soil bulk density as related to soil texture, moisture content, Ph, electrical conductivity, organic carbon, organic matter content and available macro nutrients of Pandoga sub watershed, Una District of HP (India). Int. J. Eng. Res. Dev. 13(12), 72–76 (2017).
    Google Scholar  More

  • in

    The citizens who chart changing climate

    Jean Combes’s love of nature as a child led her to note the signs of starting spring. Her long-term records are now part of a vital growing citizen science dataset that starkly shows how climate change is shifting the timing of the natural world.For people living in colder parts of the world, watching for the first signs of spring — from the opening of snowdrops and daffodils, to birds building their nests, to the return of bees and butterflies — is a common winter pastime. Jean Combes has not just been watching out for these signs, but also recording them, ever since she was a child. Taking note of the earliest emergence of leaves in springtime — first as a child of 11 years, and then continuously from the age of 20 years — Jean has now collected one of the longest continuous datasets of spring leaf-out time in the UK (see also Correspondence by Vitasse et al.). These almost 75 years of data show a clear shift that corroborates shifts now acknowledged for diverse species around the world: springtime is coming earlier, and the patterns of advance match the global trends in the changing climate. Jean’s naturalist endeavours have already earned her high honours in the form of an OBE (Order of the British Empire), and recognition of her own work is mirrored in a growing recognition of the vital role of citizen scientists in tracking the signs of our rapidly changing world.
    This is a preview of subscription content More

  • in

    Understanding urban plant phenology for sustainable cities and planet

    Meng, L. et al. Proc. Natl Acad. Sci. USA 117, 4228 (2020).CAS 
    Article 

    Google Scholar 
    Wohlfahrt, G., Tomelleri, E. & Hammerle, A. Nat. Ecol. Evol. 3, 1668–1674 (2019).Article 

    Google Scholar 
    Wortman, S. E. & Lovell, S. T. J. Environ. Qual. 42, 1283–1294 (2013).CAS 
    Article 

    Google Scholar 
    Su, Y. et al. Agri. For. Meterol. 280, 107765 (2020).Article 

    Google Scholar 
    Smith, I. A., Dearborn, V. K. & Hutyra, L. R. PLoS ONE 14, e0215846 (2019).Article 

    Google Scholar 
    Richardson, A. D. et al. Nature 560, 368–371 (2018).CAS 
    Article 

    Google Scholar 
    Meineke, E. K., Dunn, R. R. & Frank, S. D. Biol. Lett. 10, 20140586 (2014).Article 

    Google Scholar 
    Liu, J. et al. Tour. Manag. 70, 262–272 (2019).Article 

    Google Scholar 
    Li, X. et al. Remote Sens. Environ. 222, 267–274 (2019).CAS 
    Article 

    Google Scholar 
    Wang, S. et al. Nat. Ecol. Evol. 3, 1076–1085 (2019).Article 

    Google Scholar 
    Feeley, K. J. et al. Nat. Clim. Change 10, 965–970 (2020).CAS 
    Article 

    Google Scholar 
    Li, D. et al. Nat. Ecol. Evol. 3, 1661–1667 (2019).Article 

    Google Scholar 
    Li, X. et al. Earth Syst. Sci. Data 11, 881–894 (2019).Article 

    Google Scholar 
    Román, M. O. et al. Remote Sens. Environ. 210, 113–143 (2018).Article 

    Google Scholar 
    Li, X. et al. Remote Sens. Environ. 215, 74–84 (2018).Article 

    Google Scholar  More