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    Network metrics guide good control choices

    The management of introduced species, whether kudzu or zebra mussels, is costly and complex. Now, a paper reports a workable, effective solution that harnesses network analyses of ecological phenomena.Invasive species can pose severe economic and environmental problems, costing more than US$1 trillion worldwide since 1970 (ref. 1). Yet managing this human-driven issue is difficult in itself. The regions involved can be vast — entire continents or countries, for instance — while budgets are typically limited. As well, the sites potentially affected and management options can be numerous. Real systems (for example, all the lakes in the United States) can have thousands of locations that could potentially be infested. By contrast, considering just 40 locations means dealing theoretically with over 1 trillion unique combinations (240) where management could be applied (for instance, to reduce the number of invasive species leaving infested areas or entering uninfested ones). Given these constraints, a key problem is how and where to deploy control measures such as invasive-species removal. While sophisticated optimization approaches exist2, which use mathematical rules to exclude most suboptimal combinations and quickly zoom in to which locations should be managed to minimize new invasions, these algorithms are generally unfeasible for very large systems. Now, writing in Nature Sustainability, Ashander et al.3 demonstrate that simpler network metrics revealing linkages between patches can provide solutions that are often comparable to the more complex optimization algorithms. More

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    Gentrified gardens

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    Guiding large-scale management of invasive species using network metrics

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    Climate and land cover dataOur study of tropical dry season dynamics required climatic variables with high temporal resolution (i.e., daily) and full coverage of tropic regions. To reduce uncertainties associated with the choice of precipitation (P) and evapotranspiration (Ep or E) datasets, we used an ensemble of eight precipitation products, three reanalysis-based products for Ep, and one satellite-based land E product. These precipitation datasets were derived four gauge-based or satellite observation (CHIRPS58, GPCC59, CPC-U60 and PERSIANN-CDR61), three reanalyses (ERA-562, MERRA-263, and PGF64) and a multi-source weighted ensemble product (MSWEP v2.865). The potential evapotranspiration (Ep) was calculated using the FAO Penman–Monteith equation66 (Eqs. (1, 2)), which requires meteorological inputs of wind speed, net radiation, air temperature, specific humidity, and surface pressure. We derived these meteorological variables from the three reanalysis products (ERA-5, MERRA-2, and GLDAS-2.067). Since PGF reanalysis lacked upward short- and long-wave radiation output and thus net radiation, we used available meteorological outputs from GLDAS-2.0 instead, which was forced entirely with the PGF input data.$${Ep}=frac{0.408cdot triangle cdot left({R}_{n}-Gright)+gamma cdot frac{900}{T+273}cdot {u}_{2}cdot left({e}_{s}-{e}_{a}right)}{triangle +{{{{{rm{gamma }}}}}}cdot left(1+0.34cdot {u}_{2}right)}$$
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