Dataset Information

Think globally, measure locally: The MIREN standardized protocol for monitoring plant species distributions along elevation gradients.

ABSTRACT: Climate change and other global change drivers threaten plant diversity in mountains worldwide. A widely documented response to such environmental modifications is for plant species to change their elevational ranges. Range shifts are often idiosyncratic and difficult to generalize, partly due to variation in sampling methods. There is thus a need for a standardized monitoring strategy that can be applied across mountain regions to assess distribution changes and community turnover of native and non-native plant species over space and time. Here, we present a conceptually intuitive and standardized protocol developed by the Mountain Invasion Research Network (MIREN) to systematically quantify global patterns of native and non-native species distributions along elevation gradients and shifts arising from interactive effects of climate change and human disturbance. Usually repeated every five years, surveys consist of 20 sample sites located at equal elevation increments along three replicate roads per sampling region. At each site, three plots extend from the side of a mountain road into surrounding natural vegetation. The protocol has been successfully used in 18 regions worldwide from 2007 to present. Analyses of one point in time already generated some salient results, and revealed region-specific elevational patterns of native plant species richness, but a globally consistent elevational decline in non-native species richness. Non-native plants were also more abundant directly adjacent to road edges, suggesting that disturbed roadsides serve as a vector for invasions into mountains. From the upcoming analyses of time series, even more exciting results can be expected, especially about range shifts. Implementing the protocol in more mountain regions globally would help to generate a more complete picture of how global change alters species distributions. This would inform conservation policy in mountain ecosystems, where some conservation policies remain poorly implemented.

SUBMITTER: Haider S

PROVIDER: S-EPMC8844121 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Think globally, measure locally: The MIREN standardized protocol for monitoring plant species distributions along elevation gradients.

Haider Sylvia S Lembrechts Jonas J JJ McDougall Keith K Pauchard Aníbal A Alexander Jake M JM Barros Agustina A Cavieres Lohengrin A LA Rashid Irfan I Rew Lisa J LJ Aleksanyan Alla A Arévalo José R JR Aschero Valeria V Chisholm Chelsea C Clark V Ralph VR Clavel Jan J Daehler Curtis C Dar Pervaiz A PA Dietz Hansjörg H Dimarco Romina D RD Edwards Peter P Essl Franz F Fuentes-Lillo Eduardo E Guisan Antoine A Gwate Onalenna O Hargreaves Anna L AL Jakobs Gabi G Jiménez Alejandra A Kardol Paul P Kueffer Christoph C Larson Christian C Lenoir Jonathan J Lenzner Bernd B Padrón Mederos Miguel A MA Mihoc Maritza M Milbau Ann A Morgan John W JW Müllerová Jana J Naylor Bridgett J BJ Nijs Ivan I Nuñez Martin A MA Otto Rüdiger R Preuk Niels N Ratier Backes Amanda A Reshi Zafar A ZA Rumpf Sabine B SB Sandoya Verónica V Schroder Mellesa M Speziale Karina L KL Urbach Davnah D Valencia Graciela G Vandvik Vigdis V Vitková Michaela M Vorstenbosch Tom T Walker Tom W N TWN Walsh Neville N Wright Genevieve G Zong Shengwei S Seipel Tim T

Ecology and evolution 20220214 2

Climate change and other global change drivers threaten plant diversity in mountains worldwide. A widely documented response to such environmental modifications is for plant species to change their elevational ranges. Range shifts are often idiosyncratic and difficult to generalize, partly due to variation in sampling methods. There is thus a need for a standardized monitoring strategy that can be applied across mountain regions to assess distribution changes and community turnover of native and ...[more]

PMID: 35222963

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Project description:BackgroundNetwork-based learning algorithms for automated function prediction (AFP) are negatively affected by the limited coverage of experimental data and limited a priori known functional annotations. As a consequence their application to model organisms is often restricted to well characterized biological processes and pathways, and their effectiveness with poorly annotated species is relatively limited. A possible solution to this problem might consist in the construction of big networks including multiple species, but this in turn poses challenging computational problems, due to the scalability limitations of existing algorithms and the main memory requirements induced by the construction of big networks. Distributed computation or the usage of big computers could in principle respond to these issues, but raises further algorithmic problems and require resources not satisfiable with simple off-the-shelf computers.ResultsWe propose a novel framework for scalable network-based learning of multi-species protein functions based on both a local implementation of existing algorithms and the adoption of innovative technologies: we solve "locally" the AFP problem, by designing "vertex-centric" implementations of network-based algorithms, but we do not give up thinking "globally" by exploiting the overall topology of the network. This is made possible by the adoption of secondary memory-based technologies that allow the efficient use of the large memory available on disks, thus overcoming the main memory limitations of modern off-the-shelf computers. This approach has been applied to the analysis of a large multi-species network including more than 300 species of bacteria and to a network with more than 200,000 proteins belonging to 13 Eukaryotic species. To our knowledge this is the first work where secondary-memory based network analysis has been applied to multi-species function prediction using biological networks with hundreds of thousands of proteins.ConclusionsThe combination of these algorithmic and technological approaches makes feasible the analysis of large multi-species networks using ordinary computers with limited speed and primary memory, and in perspective could enable the analysis of huge networks (e.g. the whole proteomes available in SwissProt), using well-equipped stand-alone machines.

Dataset Information

Think globally, measure locally: The MIREN standardized protocol for monitoring plant species distributions along elevation gradients.

Publications

Think globally, measure locally: The MIREN standardized protocol for monitoring plant species distributions along elevation gradients.

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