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opencc-by-4.0Sep 2024View details →

Exploring the global metaplasmidome: unravelling plasmid landscapes and the spread of antibiotic resistance genes across diverse ecosystems

Plasmid content was predicted from assembled data already publicly available or constructed from reads for this study. The assembled data supplied by Pasolli and colleagues (Pasolli et al., 2019) , metasub consortium (Danko et al., 2020) and TARA ocean (Tully et al., 2018) were used for the human microbiome, the built environment and the marine ecosystem respectively. For assembly in the current study, reads from metagenomes were selected from two main databases. For the soil ecosystem, the metagenomes were selected from the dedicated curated database “TerrestrialMetagenomeDB” (Corrêa et al., 2020).  If the metagenomes were not assembled, reads were assembled by using megahit 1.2.9 with the metalarge option (Li et al., 2015) after cleaning the data with bbduk2 (qtrim=rl trimq=28 minlen=25 maq=20 ktrim=r k=25 mink=11 and a list of adapters to remove) from the bbtools suite (<a href="https://jgi.doe.gov/data-and-tools/software-tools/bbtools/">https://jgi.doe.gov/data-and-tools/software-tools/bbtools/</a>). Plasmids were predicted for each assembly by using both reference-based and reference-free approaches as described in previous works (Hilpert et al., 2021; Hennequin et al., 2022) and available on the github website (https://github.com/meb-team/PlasSuite/). The databases used for the first approach included those for chromosomes (archaea and bacteria) and plasmids from RefSeq, as well as the MOB-suite tool (Robertson and Nash, 2018), SILVA (Quast et al., 2013) and phylogenetic markers hosted by chromosomes (Wu et al., 2013). The database created for this purpose is available at this address <a href="https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-databases">https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-</a><a href="https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-databases">databases</a>. Two reference-free methods were applied to contigs that were not affiliated with chromosomes (discarded) or plasmids (retained in the first step): PlasFlow (Krawczyk et al., 2018) and PlasClass (Pellow et al., 2020). Previously undetected viruses were removed by using ViralVerify (<a href="https://github.com/ablab/viralVerify">https://github.com/ablab/viralVerify</a>)(Antipov et al., 2020) that provides in parallel plasmid/non-plasmid classification. This step would also remove potential plasmid-phage elements as described by Pfeifer et al.  (Pfeifer et al., 2021), but would minimise false positives. Eukaryotic contamination was removed by aligning the sequences against the NT database and human chromosomes (GRCh38) using minimap2 (Li, 2018) with -x asm5 option. Contigs mapping with 95% identity for at least 80% coverage were removed. The predicted plasmids, hereafter referred as plasmid-like sequences (PLSs), were grouped by "scientific names" (i.e. 27) such as defined in the SRA metadata (air, lake, wetland…) and subsequently named ecosystems. These ecosystems were grouped in 9 biomes (Tab Supplementary 4). The data were then dereplicated by ecosystems using cd-hit-est with a threshold of 99%. The dereplicated PLSs were then clustered using MMseqs2 (Steinegger and Söding, 2017) with 80% of coverage an 90% of identity (--min-seq-id 0.90 -c 0.8 --cov-mode 1 --cluster-mode 2 --alignment-mode 3 --kmer-per-seq-scale 0.2) to define plasmid-like clusters (PLCs). <div> The PLC sequences are included in the file "predicted_PLC.fasta" and the main features are dercribed in the file "metadata_PLC.tsv" <ul> <li>fasta_id: fasta identification of the PLC</li> <li>ecosystem: ecosystem from which the PLC originates</li> <li>biome: biome of the ecosystem</li> <li>latitude, longitude: GPS coordinate of the ecosystem</li> <li>length: PLC length</li> <li>map_markers: plasmid marker genes detected by PlasSuite (Hilpert et al., 2021)</li> <li>map_ncbi: PLCs present in the RefSeq plasmid database(Hilpert et al., 2021)</li> <li>nb_genes: Number of genes detected by Prokka implemented in PlasSuite</li> <li>nb_args: ARGs detected by PlasSuite</li> <li>plascad: results from plascad (Che et al., 2021)</li> </ul>     Antipov, D., Raiko, M., Lapidus, A., and Pevzner, P.A. (2020) MetaviralSPAdes: assembly of viruses from metagenomic data. Bioinformatics 36: 4126–4129. Che, Y., Yang, Y., Xu, X., Břinda, K., Polz, M.F., Hanage, W.P., and Zhang, T. (2021) Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genes. Proceedings of the National Academy of Sciences 118: e2008731118. Corrêa, F.B., Saraiva, J.P., Stadler, P.F., and da Rocha, U.N. (2020) TerrestrialMetagenomeDB: a public repository of curated and standardized metadata for terrestrial metagenomes. Nucleic Acids Res 48: D626–D632. Danko, D., Bezdan, D., Afshinnekoo, E., Ahsanuddin, S., Bhattacharya, C., Butler, D.J., et al. (2020) Global Genetic Cartography of Urban Metagenomes and Anti-Microbial Resistance. bioRxiv 724526. Hennequin, C., Forestier, C., Traore, O., Debroas, D., and Bricheux, G. (2022) Plasmidome analysis of a hospital effluent biofilm: Status of antibiotic resistance. Plasmid 122: 102638. Hilpert, C., Bricheux, G., and Debroas, D. (2021) Reconstruction of plasmids by shotgun sequencing from environmental DNA: which bioinformatic workflow? Briefings in Bioinformatics 22: bbaa059. Krawczyk, P.S., Lipinski, L., and Dziembowski, A. (2018) PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Res 46: e35. Li, D., Liu, C.-M., Luo, R., Sadakane, K., and Lam, T.-W. (2015) MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31: 1674–1676. Li, H. (2018) Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34: 3094–3100. Pasolli, E., Asnicar, F., Manara, S., Zolfo, M., Karcher, N., Armanini, F., et al. (2019) Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle. Cell 176: 649-662.e20. Pellow, D., Mizrahi, I., and Shamir, R. (2020) PlasClass improves plasmid sequence classification. PLOS Computational Biology 16: e1007781. Pfeifer, E., Moura de Sousa, J.A., Touchon, M., and Rocha, E.P.C. (2021) Bacteria have numerous distinctive groups of phage–plasmids with conserved phage and variable plasmid gene repertoires. Nucleic Acids Res 49: 2655–2673. Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., et al. (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41: D590–D596. Robertson, J. and Nash, J.H.E. (2018) MOB-suite: software tools for clustering, reconstruction and typing of plasmids from draft assemblies. Microbial Genomics 4:. Steinegger, M. and Söding, J. (2017) MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology. Tully, B.J., Graham, E.D., and Heidelberg, J.F. (2018) The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Scientific Data 5: 170203. Wu, D., Jospin, G., and Eisen, J.A. (2013) Systematic Identification of Gene Families for Use as “Markers” for Phylogenetic and Phylogeny-Driven Ecological Studies of Bacteria and Archaea and Their Major Subgroups. PLoS One 8:. </div>

opencc-by-4.0Apr 2022View details →

Managing for the unexpected: building resilient forest landscapes to cope with global change: Supporting data

Input files and installers of the versions of LANDIS-II, PnET-Succession and other extensions used in the associated paper. They can be used to to reproduce results of the study. The model documentation is freely available at <a href="https://www.landis-ii.org/">https://www.landis-ii.org/</a> The LANDIS-II code is distributed under an open source license at <a href="https://github.com/LANDIS-II-Foundation">https://github.com/LANDIS-II-Foundation</a>. If interested in using this dataset for a research study or project, please contact <a href="https://www.marco-mina.com">Marco Mina</a> --------------------- Mina, M., Messier, C., Duveneck, M., Fortin, M. J., & Aquilué, N. (2022) Managing for the unexpected: building resilient forest landscapes to cope with global change. Global Change Biology 28, 4323– 4341 <a href="https://doi.org/10.1111/gcb.16197">https://doi.org/10.1111/gcb.16197</a> ABSTRACT. Natural disturbances exacerbated by novel climate regimes are increasing worldwide, threatening the ability of forest ecosystems to mitigate global warming through carbon sequestration and to provide other key ecosystem services. One way to cope with unknown disturbance events is to promote the ecological resilience of the forest by increasing both functional trait and structural diversity and by fostering functional connectivity of the landscape to ensure a rapid and efficient self-reorganization of the system. We investigated how expected and unexpected variations in climate and biotic disturbances affect ecological resilience and carbon storage in a forested region in southeastern Canada. Using a process-based forest landscape model (LANDIS-II), we simulated ecosystem responses to climate change and insect outbreaks under different forest policy scenarios – including a novel approach based on functional diversification and network analysis – and tested how the potentially most damaging insect pests interact with changes in forest composition and structure due to changing climate and management. We found that climate warming, lengthening the vegetation season, will increase forest productivity and carbon storage, but unexpected impacts of drought and insect outbreaks will drastically reduce such variables. Generalist, non-native insects feeding on hardwood are the most damaging biotic agents for our region, and their monitoring and early detection should be a priority for forest authorities. Higher forest diversity driven by climate-smart management and fostered by climate change that promotes warm-adapted species, might increase disturbance severity. However, alternative forest policy scenarios led to a higher functional and structural diversity as well as functional connectivity – and thus to higher ecological resilience – than conventional management. Our results demonstrate that adopting a landscape-scale perspective by planning interventions strategically in space and adopting a functional trait approach to diversify forests is promising for enhancing ecological resilience under unexpected global change stressors.

opencc-by-4.0Oct 2022View details →

The Research Software Community Landscape in the Global South [Video]

The Research Software Community Landscape in the Global South Presentation and Video DOI: 10.5281/zenodo.7192692 Watch the Video: <a href="https://youtu.be/pxmYroTxz-A">https://youtu.be/pxmYroTxz-A</a> Description: The Research Software Alliance's (ReSA) mission is to bring research software communities together to collaborate on the advancement of research software. Given the ReSA mission, it is important to understand the landscape of communities involved with research software. In 2020, ReSA completed an initial exercise to scope the international research software community landscape. This work was reported by ReSA's Software Landscape Analysis task force via a blog post. The majority of the communities in the previous analysis represented the global north. To improve the extent of this landscape analysis, ReSA announced a paid opportunity for short-term contractors located in the global south to collect data on communities and funders in their region in early 2022. This document describes how the work was undertaken, a summary of findings, the gaps and opportunities perceived by the data collectors and some highlights. This work identified 126 organisations and communities and 62 funder bodies that support research software in the global south. Their main activities are connecting people, training, and networking, and support through research grants. Blog post: <a href="https://www.researchsoft.org/blog/2022-10/">https://www.researchsoft.org/blog/2022-10/</a> Please cite this work as: Martinez, Paula Andrea. (2022). The Research Software Community Landscape in the Global South. Zenodo. <a href="https://doi.org/10.5281/zenodo.7179892">https://doi.org/10.5281/zenodo.7179892</a> Martinez, Paula Andrea. (2022). Research Software Communities Global South [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7179807">https://doi.org/10.5281/zenodo.7179807</a> Martinez, Paula Andrea. (2022). Research Software Funders Global South [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7179867">https://doi.org/10.5281/zenodo.7179867</a>   To add to the funders list please fill in the following form: <a href="https://forms.gle/CJWo24MUCjhWKh9U8">https://forms.gle/CJWo24MUCjhWKh9U8</a> To add to the communities list please fill in the following form <a href="https://forms.gle/KJE9vkBnM6vhh7cEA">https://forms.gle/KJE9vkBnM6vhh7cEA </a>

opencc-zeroJun 2024View details →

Data for isolation-by-environment and its consequences for range shifts with global change: Landscape genomics of the invasive common tansy

Invasive species are a growing global economic and ecological problem. However, it is not well understood how environmental factors mediate invasive range expansion. In this study, we investigated the recent and rapid range expansion of common tansy across environmental gradients in Minnesota, U.S.A. We densely sampled individuals across the expanding range and performed reduced representation sequencing to generate a dataset of 3071 polymorphic loci for 176 individuals. The dataset includes additional samples from the native range in Finland that were not used in the downstream analysis but are contributed for completeness. The dataset includes the genotype calls for all individuals sampled and sequenced. The genotype file was generated by stacks2.59 running the denovo pipeline and then using the populations function where we kept loci that were in 70% of populations and had a minor allele frequency of at least 1%. We used non-spatial and spatially-explicit analyses to determine the relative influences of geographic distance and environmental variation on patterns of genomic variation. We found no evidence for isolation-by-distance (IBD) but strong evidence for isolation-by-environment (IBE), indicating that environmental factors may have modulated patterns of range expansion.

opencc-zeroDec 2022View details →

Madagascar's fire regimes challenge global assumptions about landscape degradation

Fire and environmental dataset (2003 - 2019) for Phelps et al. (2022, Global Change Biology). Associated manuscript abstract: Narratives of landscape degradation are often linked to unsustainable fire use by local communities. Madagascar is a case in point: the island is considered globally exceptional, with its remarkable endemic biodiversity seen as threatened by unsustainable anthropogenic fire. Yet, fire regimes on Madagascar have not been empirically characterised or globally contextualised. Here, we apply a comparative approach using MODIS remote sensing data (2003-2019), to determine relationships between Madagascar's fire regimes and global patterns and trends. We demonstrate that Madagascar's fire regimes are similar to 88% of tropical burned area, with shared climate and vegetation characteristics. Therefore, rather than a global exception, Madagascar's fire regimes could usefully be understood as a microcosm of most tropical fire regimes, which contribute to global understanding of fire. We found that landscape-scale fire declined in grassy biomes across the tropics, and at a relatively fast rate on Madagascar. The island's high tree loss anomalies (1.25 to 4.77x the tropical average) were not explained by any general expansion of grassy biome burning and were centred in forests rather than at forest-savanna boundaries, demonstrating that high rates of forest degradation were not explained by landscape-scale fire escaping from savannas into forests. Associated with forests, landscape-scale fire trends reflected important differences among tropical regions, indicating a need to better understand regional variation in the anthropogenic drivers of change. Unexpectedly, the highest tree loss anomalies on Madagascar were centred in environments without landscape-scale fire, where the role of small-scale fires (<21ha) is unknown. Madagascar's fire regimes thus contribute two lessons with global implications: first, landscape-scale burning is declining in grassy biomes across the tropics and does not explain high tree loss anomalies on Madagascar. Second, landscape-scale fire is not uniformly associated with forest loss, indicating a need for more socio-ecological context around narratives of tropical fire and ecosystem degradation.

opencc-zeroMar 2023View details →

Effects of global change on bird and beetle populations in boreal forest landscape: an assemblage dissimilarity analysis

Aim Despite an increasing number of studies highlighting the impacts of climate change on boreal species, the main factors that will drive changes in species assemblages remain ambiguous. We study how species community composition would change following anthropogenic and natural disturbances. We determine the main drivers of assemblage dissimilarity for bird and beetle communities. Location Côte-Nord, Québec, Canada. Methods We quantify two climate-induced pathways based on direct and indirect effects on species occurrence under different harvest management scenarios. The direct climate effects illustrate the impact of climate variables while the indirect effects are reflected through habitat-based climate change. We develop empirical models to predict the distribution of more than 100 species over the next century. We analyze the regional and the latitudinal species assemblage dissimilarity by decomposing it into 'balanced variation in species occupancy and occurrence' and 'occupancy and occurrence gradient'. Results Both pathways increased dissimilarity in species assemblage. At the regional scale, both effects have an impact on decreasing the number of winning species. Yet, responses are much larger in magnitude under mixed climate effects (a mixture of direct and indirect effects). Regional assemblage dissimilarity reached 0.77 and 0.69 under mixed effects versus 0.09 and 0.10 under indirect effects for beetles and birds, respectively, between RCP8.5 and baseline climate scenarios when considering harvesting. Latitudinally, assemblage dissimilarity increased following the climate conditions pattern. Main conclusions The two pathways are complementary and alter biodiversity, mainly caused by species turnover. Yet, responses are much larger in magnitude under mixed climate effects. Therefore, the inclusion of climatic variables considers aspects other than just those related to forest landscapes, such as life cycles of animal species. Moreover, we expect differences in occupancy between the two studied taxa. This could indicate the potential range of change in boreal species concerning novel environmental conditions.

opencc-by-4.0Apr 2023View details →

'Projected Landscape-scale Repercussions of Global Action for Climate and Biodiversity Protection' - model outputs

Archive of model outputs produced for the MAgPIE v4.3.5 paper 'Projected Landscape-scale Repercussions of Global Action for Climate and Biodiversity Protection'. The model code of the MAgPIE and SEALS models can be accessed via: MAgPIE model code: <a href="https://doi.org/10.5281/zenodo.5394196">https://doi.org/10.5281/zenodo.5394196</a> and <a href="https://github.com/magpiemodel/magpie">https://github.com/magpiemodel/magpie</a> MAgPIE model documentation: <a href="https://rse.pik-potsdam.de/doc/magpie/4.3.5/">https://rse.pik-potsdam.de/doc/magpie/4.3.5/</a> SEALS model code: <a href="https://doi.org/10.5281/zenodo.7795957">https://doi.org/10.5281/zenodo.7795957</a> Data descriptions: glosem_input.zip contains the spatially-explicit RLSK and C-factor data for each of the modelled scenarios at 10 arcseconds and the R code to estimate C-factor values based on the MAgPIE-SEALS outputs. glosem_output.zip contains the spatially-explicit soil loss estimates for all scenarioso and the R code used to process the input data. The data was used to create Fig. 7. magpie_ouput.zip contains the MAgPIE model outputs of all scenarios. The data is shown in Figs. 2, 3, 4, & 5. pollination_sufficiency.zip contains the spatially-explicit pollination sufficiency estimates for all modelled scenarios and the R code used to derive the pollination sufficiency scores. The data is displayed in Fig. 6.

publicDec 2022View details →

Madagascar's fire regimes challenge global assumptions about landscape degradation

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publicOct 2025View details →

The importance of landscape composition for pest control and crop yield: A global quantitative synthesis

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publicMar 2023View details →

Effects of global change on bird and beetle populations in boreal forest landscape: an assemblage dissimilarity analysis

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publicJun 2024View details →

Data for isolation-by-environment and its consequences for range shifts with global change: Landscape genomics of the invasive common tansy

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opencc-zeroDec 2017View details →

Data from: Forest loss in protected areas and intact forest landscapes: a global analysis

In spite of the high importance of forests, global forest loss has remained alarmingly high during the last decades. Forest loss at a global scale has been unveiled with increasingly finer spatial resolution, but the forest extent and loss in protected areas (PAs) and in large intact forest landscapes (IFLs) have not so far been systematically assessed. Moreover, the impact of protection on preserving the IFLs is not well understood. In this study we conducted a consistent assessment of the global forest loss in PAs and IFLs over the period 2000–2012. We used recently published global remote sensing based spatial forest cover change data, being a uniform and consistent dataset over space and time, together with global datasets on PAs' and IFLs' locations. Our analyses revealed that on a global scale 3% of the protected forest, 2.5% of the intact forest, and 1.5% of the protected intact forest were lost during the study period. These forest loss rates are relatively high compared to global total forest loss of 5% for the same time period. The variation in forest losses and in protection effect was large among geographical regions and countries. In some regions the loss in protected forests exceeded 5% (e.g. in Australia and Oceania, and North America) and the relative forest loss was higher inside protected areas than outside those areas (e.g. in Mongolia and parts of Africa, Central Asia, and Europe). At the same time, protection was found to prevent forest loss in several countries (e.g. in South America and Southeast Asia). Globally, high area-weighted forest loss rates of protected and intact forests were associated with high gross domestic product and in the case of protected forests also with high proportions of agricultural land. Our findings reinforce the need for improved understanding of the reasons for the high forest losses in PAs and IFLs and strategies to prevent further losses.

opencc-zeroDec 2014View details →

Data from: Which landscape size best predicts the influence of forest cover on restoration success? – A global meta-analysis on the scale of effect

Landscape context is a strong predictor of species persistence, abundance and distribution, yet its influence on the success of ecological restoration remains unclear. Thus, a primary question arises: which landscape size best predicts the effects of forest cover on restoration success? To answer this question, we conducted a global meta-analysis for biodiversity (mammals, birds, invertebrates, herpetofauna and plants) and measures of vegetation structure (cover, density, height, biomass and litter). Response ratios were calculated for comparisons between reference (e.g. old-growth forest) and disturbed sites (degraded or restored). Using an information-theoretic approach, mean response ratio (restoration success) and response ratio variance (restoration predictability) within each study landscape were regressed against the percentage of overall (summed forest cover) and contiguous (summed pixels of ≥60% forest cover) forest within eight different buffer sizes of radius 5–200 km (at 1-km resolution). We included 247 studies encompassing 196 study landscapes and 4360 quantitative comparisons. The best buffer (landscape) size varied for the following: (i) overall and contiguous forest cover, (ii) biodiversity and vegetation structure and (iii) mean response ratio and response ratio variance. Only plant biodiversity was influenced by overall forest cover (buffer size of 5, 10 and 200 km radii), while plants (10 and 200 km radii), mammals (5, 10 and 50–200 km radii), invertebrates (5 and 10 km radii), cover (5 km radii), height (5 km radii) and litter (100 km radii) were influenced by contiguous forest cover. Overall, mean response ratio and response ratio variance were positively and negatively nonlinearly related with both overall and contiguous forest cover, respectively. We reveal for the first time a clear pattern of increasing restoration success and decreasing uncertainty as contiguous forest cover increases. We also indicate preliminary recommended buffer sizes for investigating landscape restoration effects on biodiversity and vegetation structure. However, the coarse grain and variability in the data mean the optimal landscape size may not have been detected; thus, further research is needed. Synthesis and applications. When setting targets for ecological restoration, policymakers and restoration practitioners should account for the following: (i) the landscape context, particularly the amount of contiguous habitat up to 10 km around a disturbed site, and (ii) the uncertainty in restoration success, as it increases when contiguous forest cover falls below about 50%.

opencc-zeroDec 2023View details →

Data for: Epidemiological landscape of Batrachochytrium dendrobatidis and its impact on amphibian diversity at global scale

Chytridiomycosis, caused by the fungal pathogen Batrachochytrium dendrobatidis (Bd), is a major driver of amphibian decline worldwide. The global presence of Bd is driven by a synergy of factors, such as climate, species life history, and amphibian host susceptibility. Here, using a Bayesian data-mining approach, we modeled the epidemiological landscape of Bd to evaluate how infection varies across several spatial, ecological, and phylogenetic scales. We compiled global information on Bd occurrence, climate, species ranges, and phylogenetic diversity to infer the potential distribution and prevalence of Bd. By calculating the degree of co-distribution between Bd and our set of environmental and biological variables (e.g. climate and species), we identified the factors that could potentially be related to Bd presence and prevalence using a geographic correlation metric, epsilon (ε). We fitted five ecological models based on 1) amphibian species identity, 2) phylogenetic species variability values for a given species assemblage, 3) temperature, 4) precipitation and 5) all variables together. Our results extend the findings of previous studies by identifying the epidemiological landscape features of Bd. This ecological modeling framework allowed us to generate explicit spatial predictions for Bd prevalence at the global scale and a ranked list of species with high/low probability of Bd presence. Our geographic model identified areas with high potential for Bd prevalence (potential Bd-risk areas) and areas with low potential Bd prevalence as potential refuges (free Bd). At the amphibian assemblage level, we found a non-relationship with amphibian phylogenetic signals, but a significantly negative correlation between observed species richness and Bd prevalence indicated a potential dilution effect at the landscape scale. Our model may identify species and areas potentially susceptible and at risk for Bd presence, which could be used to prioritize regions for amphibian conservation efforts and to assess species and assemblage at risk.