Philippa Kaur

CORRESPONDENCE
02 November 2021

For NGOs, article-processing charges sap conservation funds

Kevin A. Wood

 ORCID: http://orcid.org/0000-0001-9170-6129

0
,

Julia L. Newth

 ORCID: http://orcid.org/0000-0003-3744-1443

1
&

Geoff M. Hilton

 ORCID: http://orcid.org/0000-0001-9062-3030

2

Kevin A. Wood

Wildfowl & Wetlands Trust, Slimbridge, UK.

View author publications

You can also search for this author in PubMed
 Google Scholar

Julia L. Newth

Wildfowl & Wetlands Trust, Slimbridge, UK.

View author publications

You can also search for this author in PubMed
 Google Scholar

Geoff M. Hilton

Wildfowl & Wetlands Trust, Slimbridge, UK.

View author publications

You can also search for this author in PubMed
 Google Scholar

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

The shift from a ‘reader pays’ to an ‘author pays’ model of scientific publishing presents a financial threat to environmental non-governmental organizations (eNGOs). Many of these support, conduct and publish applied research on real-world solutions to the planet’s most pressing challenges. Funded mainly by donations, eNGOs must now choose between taking conservation action and publishing more research papers.

Access options

Access through your institution

Change institution

Buy or subscribe

/* style specs start */
style{display:none!important}.LiveAreaSection-193358632 *{align-content:stretch;align-items:stretch;align-self:auto;animation-delay:0s;animation-direction:normal;animation-duration:0s;animation-fill-mode:none;animation-iteration-count:1;animation-name:none;animation-play-state:running;animation-timing-function:ease;azimuth:center;backface-visibility:visible;background-attachment:scroll;background-blend-mode:normal;background-clip:borderBox;background-color:transparent;background-image:none;background-origin:paddingBox;background-position:0 0;background-repeat:repeat;background-size:auto auto;block-size:auto;border-block-end-color:currentcolor;border-block-end-style:none;border-block-end-width:medium;border-block-start-color:currentcolor;border-block-start-style:none;border-block-start-width:medium;border-bottom-color:currentcolor;border-bottom-left-radius:0;border-bottom-right-radius:0;border-bottom-style:none;border-bottom-width:medium;border-collapse:separate;border-image-outset:0s;border-image-repeat:stretch;border-image-slice:100%;border-image-source:none;border-image-width:1;border-inline-end-color:currentcolor;border-inline-end-style:none;border-inline-end-width:medium;border-inline-start-color:currentcolor;border-inline-start-style:none;border-inline-start-width:medium;border-left-color:currentcolor;border-left-style:none;border-left-width:medium;border-right-color:currentcolor;border-right-style:none;border-right-width:medium;border-spacing:0;border-top-color:currentcolor;border-top-left-radius:0;border-top-right-radius:0;border-top-style:none;border-top-width:medium;bottom:auto;box-decoration-break:slice;box-shadow:none;box-sizing:border-box;break-after:auto;break-before:auto;break-inside:auto;caption-side:top;caret-color:auto;clear:none;clip:auto;clip-path:none;color:initial;column-count:auto;column-fill:balance;column-gap:normal;column-rule-color:currentcolor;column-rule-style:none;column-rule-width:medium;column-span:none;column-width:auto;content:normal;counter-increment:none;counter-reset:none;cursor:auto;display:inline;empty-cells:show;filter:none;flex-basis:auto;flex-direction:row;flex-grow:0;flex-shrink:1;flex-wrap:nowrap;float:none;font-family:initial;font-feature-settings:normal;font-kerning:auto;font-language-override:normal;font-size:medium;font-size-adjust:none;font-stretch:normal;font-style:normal;font-synthesis:weight style;font-variant:normal;font-variant-alternates:normal;font-variant-caps:normal;font-variant-east-asian:normal;font-variant-ligatures:normal;font-variant-numeric:normal;font-variant-position:normal;font-weight:400;grid-auto-columns:auto;grid-auto-flow:row;grid-auto-rows:auto;grid-column-end:auto;grid-column-gap:0;grid-column-start:auto;grid-row-end:auto;grid-row-gap:0;grid-row-start:auto;grid-template-areas:none;grid-template-columns:none;grid-template-rows:none;height:auto;hyphens:manual;image-orientation:0deg;image-rendering:auto;image-resolution:1dppx;ime-mode:auto;inline-size:auto;isolation:auto;justify-content:flexStart;left:auto;letter-spacing:normal;line-break:auto;line-height:normal;list-style-image:none;list-style-position:outside;list-style-type:disc;margin-block-end:0;margin-block-start:0;margin-bottom:0;margin-inline-end:0;margin-inline-start:0;margin-left:0;margin-right:0;margin-top:0;mask-clip:borderBox;mask-composite:add;mask-image:none;mask-mode:matchSource;mask-origin:borderBox;mask-position:0% 0%;mask-repeat:repeat;mask-size:auto;mask-type:luminance;max-height:none;max-width:none;min-block-size:0;min-height:0;min-inline-size:0;min-width:0;mix-blend-mode:normal;object-fit:fill;object-position:50% 50%;offset-block-end:auto;offset-block-start:auto;offset-inline-end:auto;offset-inline-start:auto;opacity:1;order:0;orphans:2;outline-color:initial;outline-offset:0;outline-style:none;outline-width:medium;overflow:visible;overflow-wrap:normal;overflow-x:visible;overflow-y:visible;padding-block-end:0;padding-block-start:0;padding-bottom:0;padding-inline-end:0;padding-inline-start:0;padding-left:0;padding-right:0;padding-top:0;page-break-after:auto;page-break-before:auto;page-break-inside:auto;perspective:none;perspective-origin:50% 50%;pointer-events:auto;position:static;quotes:initial;resize:none;right:auto;ruby-align:spaceAround;ruby-merge:separate;ruby-position:over;scroll-behavior:auto;scroll-snap-coordinate:none;scroll-snap-destination:0 0;scroll-snap-points-x:none;scroll-snap-points-y:none;scroll-snap-type:none;shape-image-threshold:0;shape-margin:0;shape-outside:none;tab-size:8;table-layout:auto;text-align:initial;text-align-last:auto;text-combine-upright:none;text-decoration-color:currentcolor;text-decoration-line:none;text-decoration-style:solid;text-emphasis-color:currentcolor;text-emphasis-position:over right;text-emphasis-style:none;text-indent:0;text-justify:auto;text-orientation:mixed;text-overflow:clip;text-rendering:auto;text-shadow:none;text-transform:none;text-underline-position:auto;top:auto;touch-action:auto;transform:none;transform-box:borderBox;transform-origin:50% 50% 0;transform-style:flat;transition-delay:0s;transition-duration:0s;transition-property:all;transition-timing-function:ease;vertical-align:baseline;visibility:visible;white-space:normal;widows:2;width:auto;will-change:auto;word-break:normal;word-spacing:normal;word-wrap:normal;writing-mode:horizontalTb;z-index:auto;-webkit-appearance:none;-moz-appearance:none;-ms-appearance:none;appearance:none;margin:0}.LiveAreaSection-193358632{width:100%}.LiveAreaSection-193358632 .login-option-buybox{display:block;width:100%;font-size:17px;line-height:30px;color:#222;padding-top:30px;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-access-options{display:block;font-weight:700;font-size:17px;line-height:30px;color:#222;font-family:Harding,Palatino,serif}.LiveAreaSection-193358632 .additional-login >li:not(:first-child)::before{transform:translateY(-50%);content:”;height:1rem;position:absolute;top:50%;left:0;border-left:2px solid #999}.LiveAreaSection-193358632 .additional-login >li:not(:first-child){padding-left:10px}.LiveAreaSection-193358632 .additional-login >li{display:inline-block;position:relative;vertical-align:middle;padding-right:10px}.BuyBoxSection-683559780{display:flex;flex-wrap:wrap;flex:1;flex-direction:row-reverse;margin:-30px -15px 0}.BuyBoxSection-683559780 .box-inner{width:100%;height:100%}.BuyBoxSection-683559780 .readcube-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:1;flex-basis:255px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .subscribe-buybox{background-color:#f3f3f3;flex-shrink:1;flex-grow:4;flex-basis:300px;background-clip:content-box;padding:0 15px;margin-top:30px}.BuyBoxSection-683559780 .title-readcube{display:block;margin:0;margin-right:20%;margin-left:20%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-buybox{display:block;margin:0;margin-right:29%;margin-left:29%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .title-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:24px;line-height:32px;color:#222;padding-top:30px;text-align:center;font-family:Harding,Palatino,serif}.BuyBoxSection-683559780 .asia-link{color:#069;cursor:pointer;text-decoration:none;font-size:1.05em;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:1.05em6}.BuyBoxSection-683559780 .access-readcube{display:block;margin:0;margin-right:10%;margin-left:10%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-asia-buybox{display:block;margin:0;margin-right:5%;margin-left:5%;font-size:14px;color:#222;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .access-buybox{display:block;margin:0;margin-right:30%;margin-left:30%;font-size:14px;color:#222;opacity:.8px;padding-top:10px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .price-buybox{display:block;font-size:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;padding-top:30px;text-align:center}.BuyBoxSection-683559780 .price-from{font-size:14px;padding-right:10px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:20px}.BuyBoxSection-683559780 .issue-buybox{display:block;font-size:13px;text-align:center;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:19px}.BuyBoxSection-683559780 .no-price-buybox{display:block;font-size:13px;line-height:18px;text-align:center;padding-right:10%;padding-left:10%;padding-bottom:20px;padding-top:30px;color:#222;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif}.BuyBoxSection-683559780 .vat-buybox{display:block;margin-top:5px;margin-right:20%;margin-left:20%;font-size:11px;color:#222;padding-top:10px;padding-bottom:15px;text-align:center;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;line-height:17px}.BuyBoxSection-683559780 .button-container{display:block;padding-right:20px;padding-left:20px}.BuyBoxSection-683559780 .button-container >a:hover,.Button-505204839:hover,.Button-1078489254:hover{text-decoration:none}.BuyBoxSection-683559780 .readcube-button{background:#fff;margin-top:30px}.BuyBoxSection-683559780 .button-asia{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:75px}.BuyBoxSection-683559780 .button-label-asia,.ButtonLabel-3869432492,.ButtonLabel-3296148077{display:block;color:#fff;font-size:17px;line-height:20px;font-family:-apple-system,BlinkMacSystemFont,”Segoe UI”,Roboto,Oxygen-Sans,Ubuntu,Cantarell,”Helvetica Neue”,sans-serif;text-align:center;text-decoration:none;cursor:pointer}.Button-505204839,.Button-1078489254{background:#069;border:1px solid #069;border-radius:0;cursor:pointer;display:block;padding:9px;outline:0;text-align:center;text-decoration:none;min-width:80px;margin-top:10px}.Button-505204839 .readcube-label,.Button-1078489254 .readcube-label{color:#069}
/* style specs end */Subscribe to JournalGet full journal access for 1 year$199.00only $3.90 per issueSubscribeAll prices are NET prices. VAT will be added later in the checkout.Tax calculation will be finalised during checkout.Rent or Buy articleGet time limited or full article access on ReadCube.from$8.99Rent or BuyAll prices are NET prices.

Additional access options:

Log in

Learn about institutional subscriptions

Nature 599, 32 (2021)
doi: https://doi.org/10.1038/d41586-021-02979-5

Competing Interests
All three authors are current employees of the Wildfowl & Wetlands Trust, an environmental non-governmental organization that is actively involved in undertaking and publishing research.

Related Articles

See more letters to the editor

Subjects

Environmental sciences

Conservation biology

Publishing

Latest on:

Environmental sciences

Embrace open-source sensors for local climate studies
Correspondence 02 NOV 21

Scientists say Australian plan to cull up to 10,000 wild horses doesn’t go far enough
News 01 NOV 21

Machine learning enables global solar-panel detection
News & Views 27 OCT 21

Publishing

Colour blindness: journals should enable image redisplay
Correspondence 02 NOV 21

Water bear fossil and grizzly bear selfie — October’s best science images
News 02 NOV 21

Cassyni aims to make online seminars more findable and citable
Career News 28 OCT 21

Jobs

Researcher/Senior Researcher in cancer progression and metastasis

St. Jude Children’s Research Hospital (St. Jude)
Memphis, TN, United States

Assistant or Associate Professor of Industrial and Physical Pharmacy

Purdue University
West Lafayette, United States

Assistant/Associate Professor in Cancer Research

The University of Texas at El Paso (UTEP)
El Paso, TX, United States

Research Coordinator II

Baylor College of Medicine (BCM)
Houston, TX, United States More

Data setsMonthly climate data of maximum and minimum temperature, dewpoint temperature, and precipitation at a 1/24th degree horizontal resolution from 1950 to 2020 was acquired from the Parameterized Regression on Independent Slopes Model (PRISM)44. Monthly surface downward shortwave radiation and 10-m wind speeds at a 0.25-degree horizontal resolution were acquired from ERA-545 for the same period and bilinearly interpolated to the PRISM grid. Monthly data for the same variables from a single ensemble member from each of 30 climate models participating in the Sixth Coupled Model Intercomparison Project (CMIP6) were acquired from the historical climate experiment for 1950–2014 and from the SSP2-45 experiment for 2015–2050 and interpolated to a common 1.0-degree horizontal resolution grid (Supplementary Table 4).Following Abatzoglou and Williams, we calculated three proxies of aridity using monthly climate data: mean vapor pressure deficit (VPD), Penman-Monteith reference evapotranspiration (ETo), and climatic water deficit (CWD46, defined as ETo minus actual evapotranspiration3). We modified ETo to account for potential reduced stomatal conductance due to increasing atmospheric carbon dioxide, which reduces surface resistance to evapotranspiration. We made this modification following the method of Yang et al.47. Importantly, the effect of CO2 on surface resistance at the scale of the western US is highly uncertain and this method derives the strength of this effect from earth system models. Each index was calculated as follows. At each grid cell, we calculated mean Mar–Sep VPD, the sum of Mar–Sep ETo, and Jan-Dec CWD; each of these time series was standardized to the 1991–2020 baseline using z-score transformations to create a fuel aridity index f for each grid cell. The regionally averaged fuel aridity index F was calculated by first taking the average of f over grid cells that have a majority of land classified as forest or woodland in the LANDFIRE environmental site potential product48. We then re-standardized F relative to the 1991–2020 reference period and applied equidistant quantile mapping49 to each model. The latter ensures that the distributions of modeled Z match those of observed Z for the 1991–2020 period while preserving changes in Z from this reference period. Herein we used CWD for F because it presents a more balanced view of precipitation and atmospheric demand than VPD or ETo alone, exhibits strong links to the forest-fire area over the observational record, and has more conservative increases in fire under future climate (Supplementary Fig. 2). The variance explained in forest-fire area when defining F as VPD, ETo, and detrended CWD is presented in Supplementary Table 1. We note that our approach does not explicitly incorporate daily meteorology such as the number of dry days or critical fire-weather patterns10 beyond that already included in F.Burned area data from wildland fires were acquired from Monitoring Trends in Burn Severity (MTBS) during 1984–201850 and from the version 6 MODIS burned area dataset during 2001–202051. The forested burned area was aggregated by lands classified as forest or woodland48. MTBS includes primarily fires ≥404 ha that comprises >95% of burned area in the region52. We further excluded areas in the unburned-to-low burn severity class53 as well as fires classified as prescribed burns in MTBS. Further, we did not include forested area treated by prescribed fire as a contemporary area for prescribed fire is more than an order of magnitude less than that of forest-fire area41. Forest-fire area estimates for 2019–2020 were obtained using adjusted burned areas from MODIS based on a linear model that relates MODIS and to the MTBS forest-fire area time series during the overlapping 2001–2018 period26.Experimental designWe focus on macroscale climate–fire models operating at the scale of the entire western US forested area. While there is value in spatially refined models, efforts to parameterize empirical relationships at localized scales can be limited by the stochastic nature of ignitions and fire weather—particularly in locations with long fire return intervals with zero-inflated distributions of annual burned area. Strong interannual relationships between fuel aridity and strain on national fire suppression resources shared across the region highlight the implicit value in considering larger spatial scales54. The macroscale approach is further justified because the leading mode of variability in fuel aridity across forested land is a commonly signed regionwide pattern that is strongly correlated (r2 = 0.79) to the logarithm of forest-fire area (Supplementary Fig. 3).Static modelFollowing previous empirical models of annual forest-fire area3,25, we first consider a static model of western US annual forest-fire area (FFA) based on F (fuel aridity) of the form:$${{{{{rm{log }}}}}}left({{{{{{mathrm{FFA}}}}}}}(t)right)={alpha }_{{{{{{mathrm{s}}}}}}}+{beta }_{{{{{{mathrm{s}}}}}}}Fleft(tright)+{{{{{rm{varepsilon }}}}}},$$
(1)
where t is the year, αs and βs, are regression coefficients, and ε represents an error term. We use annual CWD for F as it accounts for precipitation and atmospheric demand, exhibits strong interannual relationships with FFA, and provide more conservative estimates of projected changes in aridity and thus area burned than other aridity metrics such as VPD3,7,12. The error term ε is drawn from the population of the log-residual of observed minus modeled FFA. This error term represents variability not captured in the FFA–F relationship (e.g., extreme fire-weather conditions, human ignitions) that is important for the full distribution of FFA.Dynamic modelsThe contemporary climate–fire relationship in Eq. 1 should persist with increased F until increased burned area and severity cause fuel limitations15. Fire-fuel feedbacks that alter the climate–fire relationship primarily occur through temporary reduction of fine fuels; such feedbacks can reduce the burning potential for approximately three decades post-fire38,55. Further, longer-lived reductions in the forest-fire area can occur when forests do not recover from fire and instead transition to non-forest vegetation that can still carry fire. However, constraints on the area burned imposed by fire-fuel feedbacks are weakened by concurrent drought, which allows the fire to propagate across sparser fuels, and can markedly shorten the window of reduced burning18.We incorporate these effects through a term L, which represents the fraction of contemporary forested land that is incapable of carrying fire in a predominately forested environment in a given year, in a dynamic model of the form:$${log }left(frac{{{{{{{mathrm{FFA}}}}}}}}{1-Lleft(tright)}right)={alpha }_{{{{{{mathrm{d}}}}}}}+{beta }_{{{{{{mathrm{d}}}}}}}Fleft(tright)+{{{{{rm{varepsilon }}}}}},$$
(2)
where the response of log(FFA) to fuel aridity reduces as a function of L. We present various potential forms and strengths of fire-fuel feedbacks in L that are guided by the ecological literature and account for post-fire tree regeneration failure, fuel limitations imposed by recent fire history, and waning of fuel limitations during drought18,22,23,24. L is influenced by semi-permanent limitations due to failure of post-fire forest regeneration (Lrf), and temporary limitations due to recent fire history (Lf):$$Lleft(tright)={L}_{{{{{{{mathrm{rf}}}}}}}}left(tright)+{L}_{{{{{{mathrm{f}}}}}}}(t).$$
(3)
Importantly, L is poorly constrained and likely varies in geographically and temporally complex ways18,34. For example, L can differ for a fixed fraction of recently burned forest. A relatively small L implies weak feedbacks allowing forests to more easily reburn. A relatively large L implies strong feedbacks, for example, where heterogeneous fire effects create patch mosaics that constrain fire spread even though there is ample fuel. Finally, the age threshold for L may decrease with continued climate change, with some indications that recent fires burned through forests , 2end{array}right.,$$
(4)
where μ is set at 0.1 (Eq. 4 is plotted in Supplementary Fig. 4a). Hence, the fraction of forested land that is semi-permanently ineligible to carry forest fire because previously burned forest did not regenerate as forest (Lrf) is the cumulative sum of the product of annual FFA and ρ since 1984:$${L}_{{{{{{{mathrm{rf}}}}}}}}left(tright)=mathop{sum }limits_{i=1984}^{t}frac{rho left(tright){{{{{{mathrm{FFA}}}}}}}(t)}{T},$$
(5)
where T refers to the contemporary area of forested land48. Note that Eq. 4 and μ can be modified to account for the diversity of species-specific responses at local-to-regional scales given the acknowledgement that some species are more resilient than others and local plant water stress alters regeneration probabilities58,59. Overall, Lrf as parameterized here resulted in values approaching Lrf ~0.01 by 2050, suggesting that the inability of trees to regenerate post-fire is a minor contributor to fire-fuel feedbacks through mid-century. Modifications to the parameters in Eq. 4 resulted in only minor differences in projected FFA (Supplementary Table 3).Temporary fire-fuel feedbacks L
f
Most studies in forested environments show strong fire-fuel feedbacks in the first 5–10 years post-fire55,60. This temporary fire-fuel feedback, which we refer to here as Lf, tends to wane after 10 years60, with the longevity τ of the fire-fuel feedbacks varying geographically, from as short as ~15 years in warmer sites in the southwest to over ~30 years in cold mesic systems in the northern Rockies18. Herein, we use a baseline τ = 30 years, which results in a conservative estimate of future area burned.We consider two forms for how Lf incorporates information on annual fire histories over the previous τ years: a constant feedback and a fading feedback. These forms of Lf are defined below in Eqs. 6 and 7 and plotted in Supplementary Fig. 4c.In the case of the constant feedback, the effect of burned area on Lf remains constant over the τ years following fire. At the scale of the whole western US forested area, the constant form, therefore, assumes that the transient limitation is simply proportional to the total FFA over the preceding τ years:$${L}_{{{{{{mathrm{f}}}}}}}left(tright)=gamma mathop{sum }limits_{i=-tau }^{-1}frac{{{{{{{mathrm{FFA}}}}}}}(i)}{T}.$$
(6)
In Eq. 6, parameter γ represents the strength of the feedback, described in more depth below.The fading feedback form of Lf more heavily weights the contribution from recent FFA compared to older FFA. At the scale of the whole western US forested area, this form applies constant weight to FFA in the five most recent years given strong fire-fuel feedbacks of recent fires, and increasingly reduces the contributions from prior years based on a sinusoid function:$${L}_{{{{{{mathrm{f}}}}}}}left(tright)=gamma frac{mathop{sum }nolimits_{i=-5}^{-1}{{{{{{mathrm{FFA}}}}}}}left(iright)+mathop{sum }nolimits_{i=-tau }^{-6}{{{{{{mathrm{FFA}}}}}}}left(iright)ast left[1-{cos }frac{pi left(-i-5right)}{tau -5}right]/2}{T}.$$
(7)
Given the uncertainty in the efficacy of the fire-fuel feedback, we present results using both the constant and fading formulations for the temporary fire-fuel feedbacks.We additionally considered three different fuel-limitation strengths γ in Eqs. 6 and 7 to account for direct and indirect potential effects of past fires: γ = 0.5, referred to as weak; γ = 1, referred to as moderate; and γ = 1.5, referred to as strong. For the weak (γ = 0.5) fuel-limitation case using the constant feedback model, the fractional forested area ineligible to burn is only half of the total area burned in the past 30 years, indicating that half of recent burned areas can reburn. For the strong-constant fuel-limitation case, the forested area ineligible to burn post-fire exceeds the total recent burned area by 50%. An example of a strong fuel limitation is a burn mosaic with reduced connectivity that constrains the ability of subsequent fire spread into the adjacent forest that did not burn in the previous τ years. We considered higher values of γ, but these yielded degraded cross-validation skills when modeling the historical period (Supplementary Table 2).Longevity of fire-fuel feedbacks during droughtFinally, some temporary fuel limitations can be overcome during extreme fire-weather conditions and during periods of drought. For example, while reduced fuel loads in a post-fire landscape serve as an effective barrier for fire propagation under moderate fuel aridity, the fire spread probability increases with increasing F34. Studies have found that the longevity of fire-fuel feedbacks was a third shorter during periods of extreme drought than in periods without drought stress18,34. For example, there is evidence of short-interval (95% of the iterations had bias CE  > 0, >95% of the iterations had r  > 0, and the inner 95% of the simulations included a bias of 0.Supplementary Table 2 shows that the static model and many of the dynamic models have significant cross-validated skills. However, skill decreased in the dynamic models as the feedback strength increases. While the weak dynamic feedback models had similar cross-validation skill as the static model, dynamic models with very strong feedbacks (γ ≥ 2) had sizeable underpredictions in FFA by up to 46% for the validation period. Hence, we excluded such parameters from the further analysis given that such results were incongruent with the observational record.Three statistical metrics of annual variability of FFA were calculated for both static and dynamic models. First, we used generalized extreme value theory to estimate recurrence intervals for FFA greater than equal to that of the 2020 fire season. Second, we calculated the interquartile range (IQR) in modeled FFA to examine changing interannual variability. Lastly, we examined the percent of years with modeled FFA below the 1991–2020 observed median as a measure of quiescent fire years. Calculations were performed separately for each climate model for 1991–2020 and 2021–2050. More

1.Cornell, H. V. & Harrison, S. P. What are species pools and when are they important?. Annu. Rev. Ecol. Evol. Syst. 45, 45–67 (2014).Article 

Google Scholar 
2.Vellend, M. Conceptual synthesis in community ecology. Q. Rev. Biol. 85, 183–206 (2010).Article 

Google Scholar 
3.Vellend, M. The Theory of Ecological Communities Vol. 57 (Princeton University Press, 2020).
Google Scholar 
4.Whittaker, R. H. Vegetation of the Siskiyou mountains, Oregon and California. Ecol. Monogr. 30, 279–338 (1960).Article 

Google Scholar 
5.Chase, J. M. & Myers, J. A. Disentangling the importance of ecological niches from stochastic processes across scales. Philos. Trans. R. Soc. B: Biol. Sci. 366, 2351–2363 (2011).Article 

Google Scholar 
6.Jankowski, J. E., Ciecka, A. L., Meyer, N. Y. & Rabenold, K. N. Beta diversity along environmental gradients: Implications of habitat specialization in tropical montane landscapes. J. Anim. Ecol. 78, 315–327 (2009).Article 

Google Scholar 
7.Janzen, D. H. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249 (1967).
Google Scholar 
8.Ghalambor, C. K., Huey, R. B., Martin, P. R., Tewksbury, J. J. & Wang, G. Are mountain passes higher in the tropics? Janzen’s hypothesis revisited. Integr. Comp. Biol. 46, 5–17 (2006).Article 

Google Scholar 
9.Tuomisto, H. & Ruokolainen, K. Comment on “disentangling the drivers of β diversity along latitudinal and elevational gradients”. Science 335, 1573 (2012).ADS 
Article 
CAS 

Google Scholar 
10.Harrison, S. Local and regional diversity in a patchy landscape: Native, alien, and endemic herbs on serpentine. Ecology 80, 70–80 (1999).Article 

Google Scholar 
11.Vellend, M. Parallel effects of land-use history on species diversity and genetic diversity of forest herbs. Ecology 85, 3043–3055 (2004).Article 

Google Scholar 
12.Pardini, R., de Souza, S. M., Braga-Neto, R. & Metzger, J. P. The role of forest structure, fragment size and corridors in maintaining small mammal abundance and diversity in an Atlantic forest landscape. Biol. Conserv. 124, 253–266 (2005).Article 

Google Scholar 
13.Kraft, N. J. et al. Disentangling the drivers of β diversity along latitudinal and elevational gradients. Science 333, 1755–1758 (2011).ADS 
Article 
CAS 

Google Scholar 
14.Qian, H., Chen, S., Mao, L. & Ouyang, Z. Drivers of β-diversity along latitudinal gradients revisited. Glob. Ecol. Biogeogr. 22, 659–670 (2013).Article 

Google Scholar 
15.Marathe, A., Priyadarsanan, D. R., Krishnaswamy, J. & Shanker, K. Spatial and climatic variables independently drive elevational gradients in ant species richness in the Eastern Himalaya. PLoS ONE 15, e0227628 (2020).Article 
CAS 

Google Scholar 
16.Måsviken, J., Dalerum, F. & Cousins, S. A. Contrasting altitudinal variation of alpine plant communities along the Swedish mountains. Ecol. Evol. 10, 4838–4853 (2020).Article 

Google Scholar 
17.Bruun, H. H. et al. Effects of altitude and topography on species richness of vascular plants, bryophytes and lichens in alpine communities. J. Veg. Sci. 17, 37–46 (2006).Article 

Google Scholar 
18.Xu, W., Chen, G., Liu, C. & Ma, K. Latitudinal differences in species abundance distributions, rather than spatial aggregation, explain beta-diversity along latitudinal gradients. Glob. Ecol. Biogeogr. 24, 1170–1180 (2015).Article 

Google Scholar 
19.Mori, A. S. et al. Community assembly processes shape an altitudinal gradient of forest biodiversity. Glob. Ecol. Biogeogr. 22, 878–888 (2013).Article 

Google Scholar 
20.Stegen, J. C. et al. Stochastic and deterministic drivers of spatial and temporal turnover in breeding bird communities. Glob. Ecol. Biogeogr. 22, 202–212 (2013).Article 

Google Scholar 
21.Kim, T. N., Bartel, S., Wills, B. D., Landis, D. A. & Gratton, C. Disturbance differentially affects alpha and beta diversity of ants in tallgrass prairies. Ecosphere 9, e02399 (2018).Article 

Google Scholar 
22.de Castro, F. S., Silva, P. G. D., Solar, R., Fernandes, G. W. & Neves, F. S. Environmental drivers of taxonomic and functional diversity of ant communities in a tropical mountain. Insect Conserv. Divers. 13, 393–403 (2020).Article 

Google Scholar 
23.Rodríguez, P. & Arita, H. T. Beta diversity and latitude in North American mammals: Testing the hypothesis of covariation. Ecography 27, 547–556 (2004).Article 

Google Scholar 
24.Agosti, D. & Alonso, L. The ALL protocol: A standard protocol for the collection of ground-dwelling ants. In Ants: Standard Methods for Measuring and Monitoring Biodiversity (eds Agosti, D. et al.) 204–206 (Smithsonian Institution Press, 2000).
Google Scholar 
25.Gotelli, N. J., Ellison, A. M., Dunn, R. R. & Sanders, N. J. Counting ants (Hymenoptera: Formicidae): Biodiversity sampling and statistical analysis for myrmecologists. Myrmecol. News 15, 13–19 (2011).
Google Scholar 
26.Greenslade, P. Sampling ants with pitfall traps: Digging-in effects. Insectes Soc. 20, 343–353 (1973).Article 

Google Scholar 
27.R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014).
Google Scholar  More

2D mandible outlinesIn17, elliptical Fourier analysis (EFA) is employed to investigate the lateral shape difference between 106 fossil mandibles of 5 groups: A. robustus ((n=7)), H. erectus ((n=12)), H. heidelbergensis ((n=4)), H. neanderthalensis ((n=22)), and H. sapiens ((n=61)). In the presented work, the authors apply the shape-changing chain method to the same dataset. Twelve samples were suppressed, including all 7 A. robustus samples, 4 H. neanderthalensis samples, and one H. sapiens sample. Therefore, 94 mandible profiles of 4 groups of the ancient human are analyzed: H. erectus ((n=12)), H. heidelbergensis ((n=4)), H. neanderthalensis ((n=18)), and H. sapiens ((n=60)). The dataset is in the form of Cartesian coordinates of points along the mandible boundary. Note that the shape-changing chain method does not require pre-alignment of curves or removal of the size factor. However, the mandible profile dataset the authors obtained had already lost the information of the original sample sizes. Therefore, only normalized mandibular shapes are compared herein. Figure 5 illustrates the mean shape of each group of profiles by aligning all profiles in the set using a standard Procrustes superimposition (PS) which includes translation, scaling, and rotation of the profiles27.Figure 5Mean mandibular shapes of samples from H. erectus (red circles), H. heidelbergensis (blue triangles), H. neanderthalensis (green squares), and H. sapiens (black diamonds).Full size imageUsing the relative angle method ((k=150), (T=20^circ)), five apices are reserved, constituting six sub-profiles for each profile. An illustration of the location of the apices on a mandible profile is shown in Fig. 6. Each sub-profile is then matched with a shape-changing chain individually. The determination of segment type vector of each sub-profile refers to the growth mechanism of mandible proposed by Enlow et al.28. As shown in Fig. 6, the mechanism of mandible growth involves bone resorption (indicated by the arrows pointing towards the mandible contour) and bone deposition (indicated by the arrows pointing out of the mandible contour). Although the whole mandible’s displacement direction is forwards and downwards, the reconstruction of the ascending limb is generally backwards and upwards. G-segments and C-segments are employed to approximate the growing portions in target profiles and characterize the difference in profile lengths.Figure 6A profile ((j=1)) from the H. erectus group: Five apices (red circle) are located using the relative angle method ((k=150), (T=20^circ)) and divide the profile into six sub-profiles. The arrows represent the growth pattern of the mandible28.Full size imageNote that the growths of the inferior edge of the mandibular body and the posterior edge of the mandibular ramus are more significant than the rest parts of the mandible profile. The 94 mandible profiles are then matched with a shape-changing chains using the following scheme. The segment vectors for the first, second, third, and sixth sub-profiles are defined as (left[{text{MGM}}right]) alike, and the segment vectors for the fourth and fifth sub-profiles are both defined as (left[{text{MCGM}}right]), where the C-segments and G-segments are used to capture the difference in arc lengths. Therefore, the overall segment vector is$$mathbf{V}=left[text{M G M M G M M G M M C G M M C G M M G M} , right]text{,}$$where there are a total of 20 segments—12 M-segments, 2 C-segments, and 6 G-segments. After the segment type vector is defined, the shape-changing chain is generated to match the target mandible profiles and then is optimized for each sub-profile. The maximum and mean error of all profiles of the final matching result are ({E}_{text{max}}=8.0863) and (overline{E }=0.6009) units, respectively. Figure 7 shows the best (a), the average (b), and the worst match (c) according to ({tilde{E }}_{j}). Note that in the worst match, the G-segment at the condyle (head) of the mandible causes the largest matching error. This is because the third primary segmentation point (between the two M-segments that follow) identified using the relative angle method for this specific profile is not at the tip of the condyle as the majority of the profiles.Figure 7The fitting result of 94 human mandibles. (a) The best match (the 4th profile—H. erectus, ({tilde{E }}_{4}=0.3924)); (b) The match with error closest to (overline{E }) (the 6th profile—H. erectus, ({tilde{E }}_{6}=0.6006)); (c) The worst match (the 13th profile—H. heidelbergenis, ({tilde{E }}_{13}=1.0771)).Full size imageThe orientation difference between two neighboring segments reflects the rotational angle between them, and thus are employed in the statistical analysis in the next step. Denote the direction of a vector (mathbf{u}={left{{u}_{x},{u}_{y}right}}^{T}) as (angle left(mathbf{u}right)), then the orientation change between the ({e}{text{th}}) and the ({(e+1)}{text{th}}) segments on the ({j}{text{th}}) profile is calculated as the difference between the direction of the last piece on the ({e}{text{th}}) segment and the direction of the first piece on the ({(e+1)}{text{th}}) segment$${sigma }_{j}^{e}=angle left({overline{mathbf{z}} }_{{j}_{2}}^{e+1}-{overline{mathbf{z}} }_{{j}_{1}}^{e+1}right)-angle left({overline{mathbf{z}} }_{{j}_{{m}_{j}^{e}+1}}^{e}-{overline{mathbf{z}} }_{{j}_{{m}_{j}^{e}}}^{e}right), forall e=1,dots ,q-1 j=1,dots ,p.$$
(10)
In the mandible example, 19 angular variables are generated from 20 segments. As in17, a stepwise discrimination analysis (DA) is conducted (in IBM SPSS 22) to figure out the relationship among the four homo groups. DA is a supervised classification method and returns (g-1) canonical components among (g) groups of samples29. Figure 8 shows the convex hull of four homo genus plotted with the first and the second canonical components. The three main groups: H. erectus, H. neanderthalensis, and H. sapiens, are separated from each other in the direction of the first canonical component. H. heidelbergensis and H. neanderthalensis have an overlap in the direction of the second canonical component. In stepwise DA, leave-one-out cross-validation (LOOCV) is applied to verify the stability of the linear model. As a result, the prediction accuracy is 91.5% and the cross-validation accuracy is 80.9%. This DA result suggests that the shape-changing chain method is useful in analyzing 2D shapes. The classification matrices of original prediction and LOOCV are presented in Table 2, showing the details of discrimination of the four mandibular shape groups.Figure 8Canonical plot of the 94 human mandibles from four groups (H. erectus, H. heidelbergensis, H. neanderthalensis, and H. sapiens) based on the orientation changes between segments (19 variables).Full size imageTable 2 Classification matrices of the original DA and cross-validated prediction of 94 human mandibles.Full size tableNote that the classification results as shown in Fig. 8 and Table 2 are in accordance with the results obtained with EFA in17. The high misclassification rate of H. heidelbergensis and its distribution on the canonical plot are also in keep with the mainstream opinion that H. heidelbergensis is a chronospecies evolving from H. erectus and is considered as the most recent common ancestor (MRCA) between H. sapiens and H. neanderthalensis. In the work of Lestrel et al. based on EFA, 20 harmonics are employed to match 106 mandibular shapes, producing 82 Fourier descriptors17. Then, 12 distances from the centroid to specified points on each mandible’s contour are used in statistical analysis. Compared to their study, the shape-changing chain method generates only a total of 28 variables (20 orientations of all segments and 8 arc lengths of C-segments and G-segments). The differences of orientations between neighboring segments is then calculated and generates 19 variables to be analyzed in stepwise DA. Table 3 shows a comparison of the variables generated in the approximation of curves and used for statistical analysis with the shape-changing chain method and EFA. The shape-changing chain method performs a satisfying approximation result of the mandibular shapes with much fewer variables compared with EFA.Table 3 Numbers of variables used in the shape-changing chain method and in EFA17 for fitting and analyzing human mandible profiles.Full size table2D leaf outlinesLeaf classification is a typical problem that has been studied with various methods, such as artificial neural networks (ANN)30, image moments31, and EFA9. In addition, many leaves have a symmetrical shape creating issues for effective EFA12. Using the shape-changing chain method, the fitting result reveals the growth of portions on the contour and the rotation between them. This kind of information can be used in statistical analysis. Although other methods which also make use of non-shape information (size, color, etc.) have been very convenient and efficient in recognizing leaf genera, leaf matching and classification remains a problem to test the ability of the shape-changing chain method to fit and compare profiles with complicated and largely varying shapes. In this example, nine groups of 145 leaves are studied (see the groups and the number of samples in each group in Table 3). The original scanned and binarized images of the nine genera of leaves are shown in Fig. s1. The contours are traced using the Moore-Neighbor method32 and then smoothed with the MATLAB cubic spline interpolation (see Fig. s2). All leaf profiles of their original sizes are analyzed. The arc lengths of the profiles range from 1141.5 units to 8433.1 units, the areas of the leaves range from (6.1671times {10}^{4}) units2 to (1.4615times {10}^{6}) units2.Applying the relative angle method, a number of apices are recognized on each leaf contour. These apices are the primary segmentation points that determine the boundaries of sub-profiles on leaf contours. Note that the shapes of leaves from different groups vary significantly, therefore the point interval and angle threshold used for locating apices varies from group to group. For some groups, the numbers of apices identified on different samples may be different too. Table 4 shows the parameters used for identifying apices as well as the minimum and maximum numbers of apices identified on samples for each group.Table 4 Parameters used for identifying apices on leaf contours and the number of apices identified for each group.Full size tableIn order to maintain homology, supplementary segmentation points are added to divide all sample profiles into the same number of portions. There is no need to add more segmentation points on the profile that contains the most number of apices (red oak, (j=101)), therefore the total number of segmentation points on each profile is determined to be 34, dividing each profile into 35 portions. In order to reduce the matching error, supplementary segmentation points are distributed as evenly as possible in sub-profiles formed by the primary segmentation points (original apices) using a method developed based on a genetic algorithm (GA). In this problem, the locations of the supplemented segmentation points on the ({j}{text{th}}) profile are determined through the fitness function determined as follows$${F}_{j}=sum_{e=1}^{q}{left({k}_{j}^{e+1}-{k}_{j}^{e}-frac{{N}_{j}-1}{q}right)}^{2}.$$
(11)
In Eq. (11), the number of pieces contained in the ({j}{text{th}}) profile (({N}_{j}-1)) divided by the number of portions (q) yields the average number of pieces in each portion. (({k}_{j}^{e+1}-{k}_{j}^{e})) is the number of pieces contained in the ({e}{text{th}}) portion confined by the ({e}{text{th}}) and the ({(e+1)}{text{th}}) segmentation points on the ({j}{text{th}}) profile. After encoding the locations of all segmentation points in the GA and several rounds of optimization based on a certain scale of crossover and mutation, the set of supplementary segmentation points that minimizes the fitness function, Eq. (11), is determined. The original apices (red circles) and supplementary segmentation points (green circles) distributed on samples from different groups are shown in Fig. 9. In this example, each profile is finally divided into 35 portions.Figure 9The original apices (red circles) and supplementary segmentation points (green circles) on leaf contours. (a) Cherry, (b) Dogwood, (c) Gum, (d) Hickory, (e) Mulberry, (f) Red maple, (g) Red oak, (h) Sugar maple, (i) White oak. For each group, the sample that contains the most original apices is presented.Full size imageThe length of each portion varies among profiles, thus M-segments are not applicable. In addition, some portions still contain local burrs and sharp corners, which would not be matched well by C-segments. Therefore, each portion is matched by a G-segment, and the segment vector contains 35 G-segments. The maximum and mean error of 145 leaf profiles are ({E}_{text{max}}=60.6063) and (overline{E }=8.7062) units, respectively. Figure 10 shows the best, the average, and the worst matching results of the leaves according to ({tilde{E }}_{j}). More matches of nine genera of leaves are illustrated in Fig. s3. The result show that given the distribution of apices (primary segmentation points that determine sub-profiles), the GA strategy can automatically determine the distribution of supplementary segmentation points along a profile. With the segmentation points generated from this process, the shape-changing chain matches the leaf contours with small error compared to the random segmentation in the previous study.Figure 10The fitting results of 145 leaves. (a) The best match (the 62nd profile—hickory, ({tilde{E }}_{62}=0.8748)); (b) The average match (the 88th profile—red maple, ({tilde{E }}_{88}=4.0866)); c The worst match (the 110th profile—red oak, ({tilde{E }}_{107}=9.7866)).Full size imageFor classification analysis, 34 orientation differences between neighboring segments are calculated using Eq. (10). Three more variables are employed: The number of primary segmentation points, the number of burrs (detected using the relative angle method with (k=50) and (T=30^circ)), and the arc length of each profile. This sums up to a total of 37 variables. A stepwise DA is performed to classify the 145 leaf samples, and 22 out of the 37 variables are selected for analysis. The variances of the first three canonical functions are 73.5%, 13.3%, and 7.5%, which add up to 94.3% in total. Figures 11 and 12 illustrate the 2D and 3D canonical plots of the nine genus of leaves based on the first three canonical components. The plots show that gum, red maple, and white oak are distinctively separated from other groups. Cherry and mulberry are partially overlapped in the directions of canonical Roots 1 and 2 for their similar overall shapes and serrated edges. There is also an overlap between dogwood and hickory in the directions of canonical Roots 1 and 3 for their similar shapes and smooth edges. The prediction accuracy is 98.6%, and the leave-one-out cross-validation is 97.9%. Only two samples of cherry are misidentified as mulberry, and one sample of hickory is discriminated as dogwood. The DA results reveal that the shape-changing method is capable of fitting a large number of profiles that have complicated shapes and different sizes, as well as generating useful variables for statistical analysis. The leave-one-out cross-validation accuracy suggests that this method is also effective with fewer variables. In addition, the shape-changing chain method enables direct observation and comparison of variables that have physical meanings, such as the relative angles between segments.Figure 11The 2D Canonical plots of nine genus of leaves based on 22 variables.Full size imageFigure 12The 3D Canonical plot of nine genus of leaves based on 22 variables.Full size image3D cranial suture curvesThe shape-changing chain method is now applied to 3D suture curves on human infants’ skulls from a study of coronal synostosis18,19. The dataset contains 63 samples categorized into 4 groups, including left unicoronal synostosis (LUCS, (n=8)), right unicoronal synostosis (RUCS, (n=19)), bicoronal synostosis (BCS, (n=16)), and unaffected cases ((n=20)). The original data of each sample consist of 209 anatomical landmarks and curve semilandmarks located on the skull surface, especially along some anatomical lines as sutures. In this work, three curves that characterize the skull deformation are selected for analysis: the coronal suture curve, the lambdoid suture curve, and the sagittal curve which is comprised of anatomical landmarks and curve semilandmarks located on the metopic suture, the sagittal suture, and the mid-line on the occipital bone. Figure 13 shows the three suture curves on a skull surface.Figure 13The location of the coronal suture (magenta), sagittal curve (blue) and the lambdoid suture (red) on a human infant skull. The intersection points between sutures, P1 and P2, divide the sagittal curve into three sub-profiles and the lambdoid suture into two sub-profiles.Full size imageBCS occurs when the coronal sutures on both sides of the skull fuse prematurely, causing the overall head shape to become broad and short. In this case, the relative location of the lambdoid suture on the skull will move forward compared to the unaffected cases, but its shape is not affected as obviously as the coronal suture or the sagittal curve which are directly affected by coronal synostosis. In order to investigate the relative location and orientation in addition to the shape of the suture curves, a standard Procrustes superimposition is performed on the original data so that all skulls represented by the 209 landmarks and semilandmarks are scaled to the same size and aligned. Figure 14 illustrates the mean shapes of the sagittal curves and the lambdoid sutures of each group. It can be observed that for BCS cases, the sagittal curve is shorter in the anterior–posterior direction, the coronal suture becomes wider in the left–right direction, and the lambdoid suture is longer and positioned relatively forward. These differences are in accordance with the overall wider and shorter BCS skull shape. As for LUCS and RUCS cases, all three curves display a symmetrical shape deformation or orientation change about the skull symmetry plane (X = 0).Figure 14Mean shapes of (a) the sagittal curves, (b) the coronal suture, and (c) the lambdoid sutures of four groups: LUCS (red dotted line), BCS (blue solid line), RUCS (green dotted dashed line), and unaffected cases (black dashed line). Notice the symmetry of the suture curves about the skull symmetry plane (X = 0).Full size imageThe anatomical landmarks P1 and P2 (Fig. 13) which are the intersection points between the sutures, are selected as the primary segmentation points. Thus, the sagittal curve, the coronal suture, and the lambdoid suture are divided into three, two, and two sub-profiles, respectively. Since the coronal suture and the lambdoid suture grow symmetrically about the skull symmetry plane, their segment type vectors for two sub-profiles should be symmetric, respectively. In addition, each segment type vector should contain a G- or H-segment to characterize the growth. The segment type vectors of the three curves are designated as:

Sagittal curve: (left[begin{array}{cc}{text{M}}& {text{G}}end{array}right]), (left[begin{array}{cc}{text{M}}& {text{H}}end{array}right]), (left[begin{array}{cc}{text{M}}& {text{G}}end{array}right]);

Coronal suture:(left[begin{array}{ccc}{text{M}}& {text{G}}& {text{H}}end{array}right]), (left[begin{array}{ccc}{text{H}}& {text{G}}& {text{M}}end{array}right]);

Lambdoid suture: (left[begin{array}{cc}{text{G}}& {text{H}}end{array}right]), (left[begin{array}{cc}{text{H}}& {text{G}}end{array}right]).

In spatial cases, the orientation of each segment is given by 3 parameters, and each G- or H-segment is characterized by an additional length parameter. Therefore, this matching scheme generates 21, 22, and 16 parameters to describe the shape variances for the sagittal curves, the coronal suture curves, and the lambdoid suture curves, respectively. Note that the suture curves are relatively smooth, thus the average value of the maximum error on all segments ({overline{E} }_{j}) is very significant of the matching error of the chain at the ({j}{text{th}}) profile. Therefore, this parameter is chosen to assess the error in this application. Figure 15 shows the best, the average, and the worst matches of the sagittal curves, the coronal sutures, and lambdoid sutures. The overall mean error ((overline{E })) of the sagittal curves, the coronal sutures, and the lambdoid sutures are 0.8728, 0.5060, and 0.3666 units, respectively.Figure 15The fitting results of (a–c) sagittal curves, (d–f) coronal sutures, and (g–i) lambdoid sutures from 63 samples. The left column (a, d, g) is the best match of each group, the middle column (b, e, h) is the average match of each group, and the right column (c, f, i) is the worst match of each group. (a) ({overline{E} }_{s-52}=0.5122) (unaffected); (b) ({overline{E} }_{s-19}=0.8681) (BCS); (c) ({overline{E} }_{s-46}=1.6055) (unaffected); (d) ({overline{E} }_{c-12}=0.2507) (BCS); (e) ({overline{E} }_{c-54}=0.5082) (unaffected); (f) ({overline{E} }_{c-42}=0.9636) (RUCS); g ({overline{E} }_{l-29}=0.1225) (BCS); (h) ({overline{E} }_{l-37}=0.3687) (BCS); (i) ({overline{E} }_{l-28}=1.0997) (RUCS).Full size imageIn order to represent the orientation of the spatial chain, the ({e}{text{th}}) segment at the ({j}{text{th}}) profile is characterized by a unit vector that points from the starting point to the endpoint of the segment as$${mathbf{u}}_{j}^{e}=frac{{overline{mathbf{z}} }_{{j}_{{m}_{j}^{e}+1}}^{e}-{overline{mathbf{z}} }_{{j}_{1}}^{e}}{Vert {overline{mathbf{z}} }_{{j}_{{m}_{j}^{e}+1}}^{e}-{overline{mathbf{z}} }_{{j}_{1}}^{e}Vert }.$$
(12)
Since each vector ({mathbf{u}}_{j}^{e}) contains three Cartesian coordinates, each sagittal curve, coronal suture, and lambdoid suture is thus characterized by 18, 18, and 12 variables, respectively. For lambdoid sutures, its relative location which is characterized by the coordinates of point P2 is also analyzed. Therefore, the total number of variables analyzed for a lambdoid suture is 15. This is much fewer than the 209 landmarks and semilandmarks analyzed in the work of Heuzé et al.19. Stepwise DA is conducted with the variables above, and LOOCV is performed to verify the stability of the linear model. In stepwise DA, 6, 7, and 8 variables are selected to be analyzed for sagittal curves, coronal sutures, and lambdoid sutures, respectively. Figure 16 illustrates the canonical plots of the three set of curves. As shown in Fig. 16, all three set of suture curves display strong separation among four classes on the 2D canonical plots. Besides, the LUCS and RUCS curves are distributed in the opposite directions from the BCS and unaffected ones along the first canonical component, while the BCS and unaffected curves differ in the direction of the second canonical component. These plots confirm the symmetrical shape deformation of the suture curves of LUCS and RUCS cases, and the changes in the lengths and relative locations of the suture curves of BCS as observed in Fig. 14.Figure 16The 2D canonical plots of the suture curves selected from 63 skull samples. (a) The sagittal curves based on 6 variables. (b) The coronal sutures based on 7 variables. (c) The lambdoid sutures based on 8 variables.Full size imageThe original DA prediction accuracy and cross-validated accuracy are both 100% for the sagittal curves, which indicates that the shape difference of the sagittal curves can efficiently distinguish specific diagnosis of coronal synostosis. The original DA prediction accuracy and cross-validated accuracy for the coronal sutures are 98.4% and 96.8%, respectively. There are two cases (BCS and RUCS) misclassified as unaffected case. As for the lambdoid sutures, the original DA prediction accuracy and cross-validated accuracy are both 98.4%. The only misclassified case in both predictions is that one BCS lambdoid suture is categorized as unaffected. This suggests that the coronal suture and the lambdoid suture is subjected to both shape deformation and location transformation due to coronal synostosis.These matching and classification results show that the shape-changing chain is efficient in fitting and analyzing 3D curves with a very moderate number of variables compared to other parametric methods. For example, Zhou et al. employed discrete cosine transform (DCT) to analyze the same three sets of suture curves33. In their work, 12, 6, and 6 harmonics are employed to fit the sagittal curves, the coronal suture curves, and the lambdoid suture curves, resulting in 36, 18, and 18 coefficients to be analyzed. Table 5 shows a comparison of the variables used to match the curves and perform statistical analysis with the two methods. A comparison of the classification accuracies of the two methods is not provided because Zhou et al. employed between-group principal component analysis (bgPCA) while the presented work uses stepwise DA. Note that the variables obtained in DCT are mathematical coefficients which are hard to interpret, while the variables in the shape-changing chain method represent the orientations, lengths, or locations of segments, providing direct information of the variance of the curve shapes.Table 5 Numbers of variables used in the shape-changing chain method and in DCT33 for fitting and analyzing suture curves.Full size table More

Bird dataNorth America: we used annual bird count data collated under the North American Breeding Bird Survey (NA-BBS: https://www.pwrc.usgs.gov/bbs/) from 1996 to 2017. NA-BBS survey routes, consisting of 50 survey points (hereafter sites) evenly distributed over ~24.5 miles, are distributed across the United States and Canada and are usually surveyed in June. At each site, skilled volunteers conduct a three-minute point count, recording all birds seen or heard within a 400-m radius59.Europe: we used annual bird count data from 23 survey schemes across 22 countries collated under the Pan-European Common Bird Monitoring Scheme (PECBMS: https://pecbms.info) from 1998 to 2018. In each scheme, skilled volunteers carry out either line transects, point counts or territory mapping at survey sites during the breeding season and record all birds encountered60 (Supplementary Table 5); while methods vary between survey schemes, they are consistent within schemes across the time period included here.Where count data were reported for subspecies, these were aggregated to species level and any records of hybrid species or specifying genus only were removed. The longitude and latitude of each survey site (just the first site of each NA-BBS survey route) were also provided by NA-BBS and PECBMS. Not all sites were surveyed in every year and only sites surveyed at least three times during the defined time period were included in analyses. Note that similar results were found when restricting data to sites surveyed in at least 10 years during the defined period.Sound recordingsSound files for all species detected on NA-BBS and PECBMS surveys were downloaded from Xeno Canto, an online database of sound recordings of wild birds from around the world (http://www.xeno-canto.org). Specifically, we identified all files longer than 30 s, with associated metadata categorising them as high quality (category “A”) and as either “song”, “call” or “drumming” types; sound files whose type category including the term “wingbeat”, “flap”, “begging”, “alarm” or “night” types were excluded. Sound files downloaded for NA-BBS species were restricted to those recorded in North America and those from PECBMS to recordings made in Europe. If no sound files met these requirements for a given species, we downloaded all files of shorter duration for that species that met the quality and type criteria and stitched repeats of these together to produce files longer than 30 s. Where more than 50 sound files for a given species met our criteria for inclusion, a random selection of 50 was taken for use in subsequent analyses. We used multiple sound files for each species to capture, where possible, between-individual variation in song and call structure, with the sound file(s) for inclusion in specific soundscapes randomly subsampled from this set. If no sound files for a species were available, the sites where that species was detected were removed from subsequent analyses; this represented More

NEWS
01 November 2021

Scientists say Australian plan to cull up to 10,000 wild horses doesn’t go far enough

A fast-growing population of feral horses in an alpine national park needs to be substantially reduced in number, researchers argue.

Bianca Nogrady

0

Bianca Nogrady

Bianca Nogrady is a freelance science journalist based in Sydney, Australia.

View author publications

You can also search for this author in PubMed
 Google Scholar

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

Brumbies roam a wintry landscape near Yarangobilly in Australia’s Kosciuszko National Park.Credit: Perry Duffin/EPA-EFE/Shutterstock

Up to 10,000 feral horses might be killed or removed from Australia’s largest alpine national park under a draft plan to control the rapidly growing population of non-native animals. Scientists have welcomed the idea of removing them, but are alarmed that the plan still allows for thousands to remain, threatening endangered species and habitats.The proposed cull, in Kosciuszko National Park, New South Wales (NSW), contrasts with a ban on lethal control measures in the United States, where large populations of wild horses known as mustangs also cause problems.
Australian scientists call for ‘feral horse’ culls in alpine national park
The draft plan, released last month, recommends reducing the park’s population of wild horses, known in Australia as brumbies, from an estimated 14,000 to about 3,000 through a combination of mostly ground-based shooting, as well as rounding up and rehoming.But the Australian Academy of Science argues that the number of horses should be rapidly reduced below 3,000. In an open letter with 69 signatories including scientists and institutions sent to the NSW environment minister on Friday, they note that “alpine wetlands continue to degrade even with very small numbers of feral horses. Kosciusko cannot begin to recover from drought, extensive bushfires and overgrazing if, as currently proposed, 3,000 feral horses remain.”Capitulating to lobby groupsResearchers say the draft plan capitulates to a small but vocal group that has lobbied the government to protect horses because of the animals’ heritage value. The plan would allow the remaining brumbies to roam over one-third of the park. That would include threatened alpine sphagnum bogs and the habitats of endangered and vulnerable species such as a fish called the stocky galaxias (Galaxias tantangara), the alpine tree frog (Litoria verreauxii alpina) and the broad-toothed rat (Mastacomys fuscus).Australia has no native mammals with hard hooves, and so horses do more damage to delicate vegetation and soils than soft-footed species, such as kangaroos and wallabies, as well as creating problems through over-grazing.
Ancient DNA points to origins of modern domestic horses
David Watson, an ecologist at Charles Sturt University in Albury–Wodonga — which straddles NSW and the neighbouring state of Victoria — says the NSW government “couldn’t have picked a worse place” to allow feral horses to roam. He makes the point that Australia’s alpine environment covers just 1% of the continent and has many endemic and threatened species that are found nowhere else.“These areas are just too fragile to have large herbivores trampling around in them,” adds Don Driscoll, an ecologist at Deakin University in Melbourne.Management of feral horses has been a long-running issue in Australia’s mountainous alpine region, which extends across three states. The Australian Capital Territory, which shares a border with Kosciuszko National Park, has a zero-tolerance approach to feral horses and uses methods including aerial shooting.Victoria also shares an alpine border with New South Wales, but its latest management plan, released on 1 November, recommends using culling and other measures to remove all feral horses in the most delicate alpine environments, and the steady reduction of numbers elsewhere.Brumbies and mustangsThe NSW state government had previously tried to control the brumbies by rehoming them on private land, but was never able to find a place for more than a few hundred horses a year, rehoming only about 1,000 since 2002. Jamie Pittock, an environmental scientist at the Australian National University in Canberra, says that the government’s acknowledgement that the exponentially growing population cannot be managed with rehoming alone is at least “a step forward”.But Watson says that 3,000 horses would breed rapidly enough that 1,000 would still need to be removed or killed every few years, meaning that even a small population will create a continuing headache in terms of damage to the park and removal requirements.
Ancient horses went dark to hide in forests
A spokesperson for the NSW National Parks and Wildlife Service said the proposed target of 3,000 horses would maintain the “environmental values of the park” and that removing horses from two thirds of the park would provide “effective protection” for threatened species. They did not respond to Nature’s specific questions about scientists’ criticisms of the draft plan.The United States is grappling with similar issues with mustangs in national parks, says ecosystem scientist John Derek Scasta at the University of Wyoming in Laramie. “The goal is to get within an agreed-upon number of horses that are sustainable,” he says, but not everybody agrees on what that number is.Because legislation bans culling, the US Bureau of Land Management instead relies on rounding up, sterilization, rehoming or paying to keep the horses on either private or federal holdings. But Scasta says rising numbers, and the costs of looking after them, might mean the United States has to face its own reckoning with wild horses in the not-too-distant future.

doi: https://doi.org/10.1038/d41586-021-02977-7

Related Articles

Australian scientists call for ‘feral horse’ culls in alpine national park

Ancient DNA points to origins of modern domestic horses

Ancient horses went dark to hide in forests

Subjects

Government

Policy

Environmental sciences

Conservation biology

Latest on:

Government

Scientists’ fears of racial bias surge amid US crackdown on China ties
News 29 OCT 21

Why hundreds of scientists are weighing in on a high-stakes US abortion case
News 26 OCT 21

Brazil’s scientists face 90% budget cut
Correspondence 25 OCT 21

Policy

UK research funding to grow slower than hoped
News 28 OCT 21

The advocacy frontier
Outlook 27 OCT 21

The fluoride wars rage on
Outlook 27 OCT 21

Environmental sciences

Machine learning enables global solar-panel detection
News & Views 27 OCT 21

Air quality: WHO guidelines could deepen inequities
Correspondence 26 OCT 21

Marine urban sprawl is gobbling up Earth’s coastlines
Research Highlight 26 OCT 21

Jobs

Epidemiologist / Postdoc

German Cancer Research Center in the Helmholtz Association (DKFZ)
Heidelberg, Germany

PhD Student in Liquid Biopsy

German Cancer Research Center in the Helmholtz Association (DKFZ)
Germany

PhD (f/m/d) Data-driven Continuum Modelling of Infected Cell Dynamics on the Tissue Scale / Master’s degree in physics, computer science, bioinformatics, computational biology, data science, machine learning or relevant discipline / …

Helmholtz-Zentrum Dresden-Rossendorf (HZDR)
Görlitz, Germany

PhD (f/m/d) Generative Machine Learning of Time-Lapse Virological Imaging Data / Master’s degree in Computer science, Bioinformatics, Computational biology, Data science, Machine learning or relevant discipline / Engage with our …

Helmholtz-Zentrum Dresden-Rossendorf (HZDR)
Görlitz, Germany More

1.Pérez, J., Moraleda-Muñoz, A., Marcos-Torres, F. J. & Muñoz-Dorado, J. Bacterial predation: 75 years and counting!. Environ. Microbiol. 18, 766–779 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Linares-Otoya, L. et al. Diversity and antimicrobial potential of predatory bacteria from the Peruvian coastline. Mar. Drugs. 15, E308. https://doi.org/10.3390/md15100308 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
3.Pasternak, Z. et al. By their genes ye shall know them: Genomic signatures of predatory bacteria. ISME J. 7, 756–769 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Sockett, R. E. Predatory lifestyle of Bdellovibrio bacteriovorus. Ann. Rev. Microbiol. 63, 523–539 (2009).CAS 
Article 

Google Scholar 
5.Korp, J., Vela Gurovic, M. S. & Nett, M. Antibiotics from predatory bacteria. Beilstein J. Org. Chem. 12, 594–607 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Johnke, J., Fraune, S., Bosch, T. C. G., Hentschel, U. & Schulenburg, H. Bdellovibrio and like organisms are predictors of microbiome diversity in distinct host groups. Microb. Ecol. 79, 252–257 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
7.Vila, J., Moreno-Morales, J. & Ballesté-Delpierre, C. Current landscape in the discovery of novel antibacterial agents. Clin. Microbiol. Infect. https://doi.org/10.1016/j.cmi.2019.09.015 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
8.Hobley, L. et al. Dual predation by bacteriophage and Bdellovibrio bacteriovorus can eradicate Escherichia coli prey in situations where single predation cannot. J. Bacteriol. 202, e00629-19. https://doi.org/10.1128/JB.00629-19 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
9.El-Shibiny, A., Connerton, P. L. & Connerton, I. F. Enumeration and diversity of campylobacters and bacteriophages isolated during the rearing cycles of free-range and organic chickens. Appl. Environ. Microbiol. 71, 1259–1266 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Wilkinson, D. A. et al. Updating the genomic taxonomy and epidemiology of Campylobacter hyointestinalis. Sci. Rep. 8, 2393. https://doi.org/10.1038/s41598-018-20889-x (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
11.Lee, M. D. GToTree: A user-friendly workflow for phylogenomics. Bioinformatics 35, 4162–4164 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 10, e1002195 (2011).MathSciNet 
Article 
CAS 

Google Scholar 
13.Edgar, R. C. MUSCLE: A multiple sequence alignment method with reduced time and space complexity. BMC Bioinform. 5, 113 (2004).Article 
CAS 

Google Scholar 
14.Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. TrimAl: A tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
15.Hyatt, D., LoCascio, P. F., Hauser, L. J. & Uberbacher, E. C. Gene and translation initiation site prediction in metagenomic sequences. Bioinformatics 28, 2223–2230 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Shen, W. & Xiong, J. TaxonKit: A cross-platform and efficient NCBI taxonomy toolkit. bioRxiv. (Accessed 1 June 2021); https://www.biorxiv.org/content/10.1101/513523v1 (2019).
17.Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—Approximately maximum-likelihood trees for large alignments. PLoS One 5, e9490 (2010).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
18.Tange, O. GNU Parallel. (Accessed 1 June 2021); https://zenodo.org/record/1146014#.YOHaiJhKiUk (2018).19.Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Czech, L. et al. Role of the extremolytes ectoine and hydroxyectoine as stress protectants and nutrients: Genetics, phylogenomics, biochemistry, and structural Analysis. Genes (Basel). 9, E177. https://doi.org/10.3390/genes9040177 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
21.Gregson, B. H., Metodieva, G., Metodiev, M. V., Golyshin, P. N. & McKew, B. A. Differential protein expression during growth on medium versus long-chain alkanes in the obligate marine hydrocarbon-degrading bacterium Thalassolituus oleivorans MIL-1. Front. Microbiol. 9, 3130 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
22.Pasternak, Z., Ben Sasson, T., Cohen, Y., Segev, E. & Jurkevitch, E. A new comparative-genomics approach for defining phenotype-specific indicators reveals specific genetic markers in predatory bacteria. PLoS One. 10, e0142933. https://doi.org/10.1371/journal.pone.0142933 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
23.Yakimov, M. M. et al. Thalassolituus oleivorans gen. nov., sp. nov., a novel marine bacterium that obligately utilizes hydrocarbons. Int. J. Syst. Evol. Microbiol. 54, 141–148 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Wang, Y., Yu, M., Liu, Y., Yang, X. & Zhang, X. H. Bacterioplanoides pacificum gen. nov., sp. nov., isolated from seawater of South Pacific Gyre. Int. J. Syst. Evol. Microbiol. 66, 5010–5015 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Bowditch, R. D., Baumann, L. & Baumann, P. Description of Oceanospirillum kriegii sp. nov. and O. jannaschii sp. nov. and assignment of two species of Alteromonas to this genus as O. commune comb. nov. and O. vagum comb. nov. Curr. Microbiol. 10, 221–229 (1984).CAS 
Article 

Google Scholar 
26.Dong, C., Chen, X., Xie, Y., Lai, Q. & Shao, Z. Complete genome sequence of Thalassolituus oleivorans R6-15, an obligate hydrocarbonoclastic marine bacterium from the Arctic Ocean. Stand Genom. Sci. 9, 893–901 (2014).Article 

Google Scholar 
27.Choi, A. & Cho, J.-C. Thalassolituus marinus sp. nov., a hydrocarbon utilizing marine bacterium. Int. J. Syst. Evol. Microbiol. 63, 2234–2238 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Alain, K., Harder, J., Widdel, F. & Zengler, K. Anaerobic utilization of toluene by marine alpha- and gammaproteobacteria reducing nitrate. Microbiology 158, 2946–2957 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Liu, J., Wu, W., Chen, C., Sun, F. & Chen, Y. Prokaryotic diversity, composition structure, and phylogenetic analysis of microbial communities in leachate sediment ecosystems. Appl. Microbiol. Biotechnol. 91, 1659–1675 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Yakimov, M. M., Timmis, K. N. & Golyshin, P. N. Obligate oil-degrading marine bacteria. Curr. Opin. Biotechnol. 18, 257–266 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.McKew, B. A. et al. Efficacy of intervention strategies for bioremediation of crude oil in marine systems and effects on indigenous hydrocarbonoclastic bacteria. Environ. Microbiol. 9, 1562–1571 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Satomi, M., Kimura, B., Hamada, T., Harayama, S. & Fujii, T. Phylogenetic study of the genus Oceanospirillum based on 16S rRNA and gyrB genes: emended description of the genus Oceanospirillum, description of Pseudospirillum gen. nov., Oceanobacter gen. nov. and Terasakiella gen. nov. and transfer of Oceanospirillum jannaschii and Pseudomonas stanieri to Marinobacterium as Marinobacterium jannaschii comb. nov. and Marinobacterium stanieri comb. no. Int. J. Syst. Evol. Microbiol. 52, 739–747 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Qin, Q. L. et al. A proposed genus boundary for the prokaryotes based on genomic insights. J. Bacteriol. 196, 2210–2215 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
34.Nicholson, A. C. et al. Division of the genus Chryseobacterium: Observation of discontinuities in amino acid identity values, a possible consequence of major extinction events, guides transfer of nine species to the genus Epilithonimonas, eleven species to the genus Kaistella, and three species to the genus Halpernia gen. nov., with description of Kaistella daneshvariae sp. nov. and Epilithonimonas vandammei sp. nov. derived from clinical specimens. Int. J. Syst. Evol. Microbiol. 70, 4432–4450 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 12, 635–645 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Barco, R. A. et al. A genus definition for Bacteria and Archaea based on a standard genome relatedness index. MBio 11, e02475-192020. https://doi.org/10.1128/mBio.02475-19 (2020).Article 

Google Scholar 
37.Andersson, J. O. & Andersson, S. G. Insights into the evolutionary process of genome degradation. Curr. Opin. Genet. Dev. 9, 664–671 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Wall, D. & Kaiser, D. Type IV pili and cell motility. Mol. Microbiol. 32, 1–10 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Jenal, U. & Malone, J. Mechanisms of cyclic-di-GMP signaling in bacteria. Ann. Rev. Genet. 40, 385–407 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Dow, J. M., Fouhy, Y., Lucey, J. F. & Ryan, R. P. The HD-GYP domain, cyclic di-GMP signaling, and bacterial virulence to plants. Mol. Plant Microbe Interact. 19, 1378–1384 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Hobley, L. et al. Discrete cyclic di-GMP-dependent control of bacterial predation versus axenic growth in Bdellovibrio bacteriovorus. PLoS Pathog. 8, e1002493. https://doi.org/10.1371/journal.ppat.1002493 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
42.Seccareccia, I., Kovács, Á. T., Gallegos-Monterrosa, R. & Nett, M. Unraveling the predator-prey relationship of Cupriavidus necator and Bacillus subtilis. Microbiol. Res. 192, 231–238 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Mu, D. S. et al. Bradymonabacteria, a novel bacterial predator group with versatile survival strategies in saline environments. Microbiome 8, 1262020 (2020).Article 

Google Scholar 
44.Zepeda, V. K. et al. Terasakiispira papahanaumokuakeensis gen. nov., sp. nov., a gammaproteobacterium from Pearl and Hermes Atoll, Northwestern Hawaiian Islands. Int. J. Syst. Evol. Microbiol. 65, 3609–3617 (2015).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
45.Terasaki, Y. Transfer of five species and two subspecies of Spirillum to other genera (Aquaspirillum and Oceanospirillum), with emended descriptions of the species and subspecies. Int. J. Syst. Evol. Microbiol. 29, 130–144 (1979).
Google Scholar 
46.Baker, D. A. & Park, R. W. Changes in morphology and cell wall structure that occur during growth of Vibrio sp. NCTC4716 in batch culture. J. Gen. Microbiol. 86, 12–28 (1975).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Ng, L. K., Sherburne, R., Taylor, D. E. & Stiles, M. E. Morphological forms and viability of Campylobacter species studied by electron microscopy. J. Bacteriol. 164, 338–343 (1985).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Reshetnyak, V. I. & Reshetnyak, T. M. Significance of dormant forms of Helicobacter pylori in ulcerogenesis. World J. Gastroenterol. 23, 4867–4878 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
49.Loc Carrillo, C. et al. Bacteriophage therapy to reduce Campylobacter jejuni colonization of broiler chickens. Appl. Environ. Microbiol. 71, 6554–6563 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Clinical and Laboratory Standards Institute. Methods for determining bactericidal activity of antimicrobial agents; approved guideline M26-A. Clin. Lab. Stand. Inst. 19, 7 (1999).
Google Scholar 
51.Legat, A., Gruber, C., Zangger, K., Wanner, G. & Stan-Lotter, H. Identification of polyhydroxyalkanoates in Halococcus and other haloarchaeal species. Appl. Microbiol. Biotechnol. 87, 1119–1127 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
53.Tamura, K. & Nei, M. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol. Biol. Evol. 10, 512–526 (1993).CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Felsenstein, J. Confidence limits on phylogenies: An approach using the bootstrap. Evolution 39, 783–791 (1985).PubMed 
PubMed Central 
Article 

Google Scholar 
55.Rodriguez-R, L. M. & Konstantinidis, K. T. Bypassing cultivation to identify bacterial species. Microbe 9, 111–118 (2014).
Google Scholar 
56.Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Nesshöver, C. et al. The science, policy and practice of nature-based solutions: an interdisciplinary perspective. Sci. Total Environ. 579, 1215–1227 (2017).Article 

Google Scholar 
2.Chausson, A. et al. Mapping the effectiveness of nature-based solutions for climate change adaptation. Glob. Chang. Biol. 26, 6134–6155 (2020).Article 

Google Scholar 
3.Pires, J. C. M. Negative emissions technologies: a complementary solution for climate change mitigation. Sci. Total Environ. 672, 502–514 (2019).Article 

Google Scholar 
4.McLaren, D. A comparative global assessment of potential negative emissions technologies. Process Saf. Environ. Prot. 90, 489–500 (2012).Article 

Google Scholar 
5.Anderson, K. & Peters, G. The trouble with negative emissions. Science 354, 182–183 (2016).Article 

Google Scholar 
6.Nellemann, C. et al. Blue Carbon — The Role of Healthy Oceans in Binding Carbon (UN Environment, 2009).7.Barbier, E. B. et al. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81, 169–193 (2011).Article 

Google Scholar 
8.Himes-Cornell, A., Grose, S. O. & Pendleton, L. Mangrove ecosystem service values and methodological approaches to valuation: where do we stand? Front. Mar. Sci. 5, 376 (2018).Article 

Google Scholar 
9.Friess, D. A. et al. in Oceanography and Marine Biology Vol. 58 Ch. 3 (CRC, 2020).10.Lovelock, C. E. & Duarte, C. M. Dimensions of blue carbon and emerging perspectives. Biol. Lett. 15 https://doi.org/10.1098/rsbl.2018.0781 (2019).11.Duarte, C. M., Losada, I. J., Hendriks, I. E., Mazarrasa, I. & Marbà, N. The role of coastal plant communities for climate change mitigation and adaptation. Nat. Clim. Chang. 3, 961–968 (2013).Article 

Google Scholar 
12.Duarte, C. M., Middelburg, J. J. & Caraco, N. Major role of marine vegetation on the oceanic carbon cycle. Biogeosciences 2, 1–8 (2005).Article 

Google Scholar 
13.Mcleod, E. et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Front. Ecol. Environ. 9, 552–560 (2011).Article 

Google Scholar 
14.Krause-Jensen, D. & Duarte, C. M. Substantial role of macroalgae in marine carbon sequestration. Nat. Geosci. 9, 737–742 (2016).Article 

Google Scholar 
15.Macreadie, P. I. et al. Vulnerability of seagrass blue carbon to microbial attack following exposure to warming and oxygen. Sci. Total Environ. 686, 264–275 (2019).Article 

Google Scholar 
16.Sippo, J. Z., Lovelock, C. E., Santos, I. R., Sanders, C. J. & Maher, D. T. Mangrove mortality in a changing climate: an overview. Estuar. Coast. Shelf Sci. 215, 241–249 (2018).Article 

Google Scholar 
17.Lovelock, C. E. et al. Assessing the risk of carbon dioxide emissions from blue carbon ecosystems. Front. Ecol. Environ. 15, 257–265 (2017).Article 

Google Scholar 
18.Zhao, Q. et al. Where marine protected areas would best represent 30% of ocean biodiversity. Biol. Conserv. 244, 108536 (2020).Article 

Google Scholar 
19.Duarte, C. M. et al. Rebuilding marine life. Nature 580, 39–51 (2020).Article 

Google Scholar 
20.Bayraktarov, E. et al. The cost and feasibility of marine coastal restoration. Ecol. Appl. 26, 1055–1074 (2016).Article 

Google Scholar 
21.Van, T. T. et al. Changes in mangrove vegetation area and character in a war and land use change affected region of Vietnam (Mui Ca Mau) over six decades. Acta Oecol. 63, 71–81 (2015).Article 

Google Scholar 
22.Dung, L. V., Tue, N. T., Nhuan, M. T. & Omori, K. Carbon storage in a restored mangrove forest in Can Gio Mangrove Forest Park, Mekong Delta, Vietnam. For. Ecol. Manage. 380, 31–40 (2016).Article 

Google Scholar 
23.Nam, V. N., Sasmito, S. D., Murdiyarso, D., Purbopuspito, J. & MacKenzie, R. A. Carbon stocks in artificially and naturally regenerated mangrove ecosystems in the Mekong Delta. Wetl. Ecol. Manag. 24, 231–244 (2016).Article 

Google Scholar 
24.Reynolds, L. K., Waycott, M., McGlathery, K. J. & Orth, R. J. Ecosystem services returned through seagrass restoration. Restor. Ecol. 24, 583–588 (2016).Article 

Google Scholar 
25.Das, S. Ecological restoration and livelihood: contribution of planted mangroves as nursery and habitat for artisanal and commercial fishery. World Dev. 94, 492–502 (2017).Article 

Google Scholar 
26.Kiesel, J. et al. Effective design of managed realignment schemes can reduce coastal flood risks. Estuar. Coast. Shelf Sci. 242, 106844 (2020).Article 

Google Scholar 
27.McNally, C. G., Uchida, E. & Gold, A. J. The effect of a protected area on the tradeoffs between short-run and long-run benefits from mangrove ecosystems. Proc. Natl Acad. Sci. USA 108, 13945–13950 (2011).Article 

Google Scholar 
28.Chow, J. Mangrove management for climate change adaptation and sustainable development in coastal zones. J. Sustain. For. 37, 139–156 (2018).Article 

Google Scholar 
29.Dasgupta, S., Islam, M. S., Huq, M., Huque Khan, Z. & Hasib, M. R. Quantifying the protective capacity of mangroves from storm surges in coastal Bangladesh. PLoS ONE 14, e0214079 (2019).Article 

Google Scholar 
30.Sutton-Grier, A. E. & Moore, A. Leveraging carbon services of coastal ecosystems for habitat protection and restoration. Coast. Manag. 44, 259–277 (2016).Article 

Google Scholar 
31.Owuor, M. A., Mulwa, R., Otieno, P., Icely, J. & Newton, A. Valuing mangrove biodiversity and ecosystem services: a deliberative choice experiment in Mida Creek, Kenya. Ecosyst. Serv. 40, 101040 (2019).Article 

Google Scholar 
32.Mcowen, C. J. et al. A global map of saltmarshes. Biodivers. Data J. 5, e11764 (2018).Article 

Google Scholar 
33.Bunting, P. et al. The global mangrove watch — a new 2010 global baseline of mangrove extent. Remote Sens. 10, 1669 (2018).Article 

Google Scholar 
34.Jayathilake, D. R. M. & Costello, M. J. A modelled global distribution of the seagrass biome. Biol. Conserv. 226, 120–126 (2018).Article 

Google Scholar 
35.McKenzie, L. J. et al. The global distribution of seagrass meadows. Environ. Res. Lett. 15, 74041 (2020).Article 

Google Scholar 
36.Trumbore, S. E. Potential responses of soil organic carbon to global environmental change. Proc. Natl Acad. Sci. USA 94, 8284–8291 (1997).Article 

Google Scholar 
37.Hamilton, S. E. & Friess, D. A. Global carbon stocks and potential emissions due to mangrove deforestation from 2000 to 2012. Nat. Clim. Chang. 8, 240–244 (2018).Article 

Google Scholar 
38.Ouyang, X. & Lee, S. Y. Improved estimates on global carbon stock and carbon pools in tidal wetlands. Nat. Commun. 11, 317 (2020).Article 

Google Scholar 
39.Kauffman, J. B. et al. Total ecosystem carbon stocks of mangroves across broad global environmental and physical gradients. Ecol. Monogr. 90, e01405 (2020).Article 

Google Scholar 
40.Simard, M. et al. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency. Nat. Geosci. 12, 40–45 (2019).Article 

Google Scholar 
41.Hutchison, J., Manica, A., Swetnam, R., Balmford, A. & Spalding, M. Predicting global patterns in mangrove forest biomass. Conserv. Lett. 7, 233–240 (2014).Article 

Google Scholar 
42.Atwood, T. B. et al. Global patterns in mangrove soil carbon stocks and losses. Nat. Clim. Chang. 7, 523–528 (2017).Article 

Google Scholar 
43.Sanderman, J. et al. A global map of mangrove forest soil carbon at 30 m spatial resolution. Environ. Res. Lett. 13, 55002 (2018).Article 

Google Scholar 
44.Traganos, D. et al. Towards global-scale seagrass mapping and monitoring using Sentinel-2 on Google Earth Engine: the case study of the Aegean and Ionian Seas. Remote Sens. 10, 1227 (2018).Article 

Google Scholar 
45.Hossain, M. S. & Hashim, M. Potential of Earth Observation (EO) technologies for seagrass ecosystem service assessments. Int. J. Appl. Earth Obs. Geoinf. 77, 15–29 (2019).Article 

Google Scholar 
46.Atwood, T. B., Witt, A., Mayorga, J., Hammill, E. & Sala, E. Global patterns in marine sediment carbon stocks. Front. Mar. Sci. 7, 165 (2020).Article 

Google Scholar 
47.Coastal carbon atlas. Coastal Carbon Research Coordination Network. CCRCN https://ccrcn.shinyapps.io/CoastalCarbonAtlas/_w_8595a9b5/#tab-6425-6 (2019).48.UNEP-WCMC. Ocean data viewer: global distribution of seagrasses. UNEP https://doi.org/10.34892/x6r3-d211 (2018).49.Hammerstrom, K. K., Kenworthy, W. J., Fonseca, M. S. & Whitfield, P. E. Seed bank, biomass, and productivity of Halophila decipiens, a deep water seagrass on the west Florida continental shelf. Aquat. Bot. 84, 110–120 (2006).Article 

Google Scholar 
50.Pergent-Martini, C. et al. Descriptors of Posidonia oceanica meadows: use and application. Ecol. Indic. 5, 213–230 (2005).Article 

Google Scholar 
51.Esteban, N., Unsworth, R. K. F., Gourlay, J. B. Q. & Hays, G. C. The discovery of deep-water seagrass meadows in a pristine Indian Ocean wilderness revealed by tracking green turtles. Mar. Pollut. Bull. 134, 99–105 (2018).Article 

Google Scholar 
52.York, P. H. et al. Dynamics of a deep-water seagrass population on the Great Barrier Reef: annual occurrence and response to a major dredging program. Sci. Rep. 5, 13167 (2015).Article 

Google Scholar 
53.Serrano, O. et al. Australian vegetated coastal ecosystems as global hotspots for climate change mitigation. Nat. Commun. 10, 4313 (2019).Article 

Google Scholar 
54.Chmura, G. L., Anisfeld, S. C., Cahoon, D. R. & Lynch, J. C. Global carbon sequestration in tidal, saline wetland soils. Glob. Biogeochem. Cycles 17, 1111 (2003).Article 

Google Scholar 
55.Hengl, T. et al. SoilGrids250m: global gridded soil information based on machine learning. PLoS ONE 12, e0169748 (2017).Article 

Google Scholar 
56.Rogers, K. et al. Wetland carbon storage controlled by millennial-scale variation in relative sea-level rise. Nature 567, 91–95 (2019).Article 

Google Scholar 
57.Rovai, A. S. et al. Global controls on carbon storage in mangrove soils. Nat. Clim. Chang. 8, 534–538 (2018).Article 

Google Scholar 
58.Worthington, T. A. et al. A global biophysical typology of mangroves and its relevance for ecosystem structure and deforestation. Sci. Rep. 10, 14652 (2020).Article 

Google Scholar 
59.Maher, D. T., Call, M., Santos, I. R. & Sanders, C. J. Beyond burial: lateral exchange is a significant atmospheric carbon sink in mangrove forests. Biol. Lett. 14, 20180200 (2018).Article 

Google Scholar 
60.Santos, I. R., Maher, D. T., Larkin, R., Webb, J. R. & Sanders, C. J. Carbon outwelling and outgassing vs. burial in an estuarine tidal creek surrounded by mangrove and saltmarsh wetlands. Limnol. Ocean 64, 996–1013 (2019).Article 

Google Scholar 
61.Kelleway, J. J. et al. A national approach to greenhouse gas abatement through blue carbon management. Glob. Environ. Chang. 63, 102083 (2020).Article 

Google Scholar 
62.Goldberg, L., Lagomasino, D., Thomas, N. & Fatoyinbo, T. Global declines in human-driven mangrove loss. Glob. Chang. Biol. 68, 5844–5855 (2020).Article 

Google Scholar 
63.Richards, D. R. & Friess, D. A. Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012. Proc. Natl. Acad. Sci. 113, 344–349 (2016).Article 

Google Scholar 
64.Thomas, N. et al. Distribution and drivers of global mangrove forest change, 1996–2010. PLoS ONE 12, e0179302 (2017).Article 

Google Scholar 
65.Worthington, T. & Spalding, M. Mangrove restoration potential: a global map highlighting a critical opportunity (OECD, 2018).66.Kearney, M. S., Riter, J. C. A. & Turner, R. E. Freshwater river diversions for marsh restoration in Louisiana: twenty-six years of changing vegetative cover and marsh area. Geophys. Res. Lett. 38, 16405 (2011).Article 

Google Scholar 
67.Lee, S. Y., Hamilton, S., Barbier, E. B., Primavera, J. & Lewis, R. R. Better restoration policies are needed to conserve mangrove ecosystems. Nat. Ecol. Evol. 3, 870–872 (2019).Article 

Google Scholar 
68.Lovelock, C. E. & Brown, B. M. Land tenure considerations are key to successful mangrove restoration. Nat. Ecol. Evol. 3, 1135 (2019).Article 

Google Scholar 
69.Herr, D., Blum, J., Himes-Cornell, A. & Sutton-Grier, A. An analysis of the potential positive and negative livelihood impacts of coastal carbon offset projects. J. Environ. Manag. 235, 463–479 (2019).Article 

Google Scholar 
70.Mojica Vélez, J. M., Barrasa García, S. & Espinoza Tenorio, A. Policies in coastal wetlands: key challenges. Environ. Sci. Policy 88, 72–82 (2018).Article 

Google Scholar 
71.Zeng, Y. et al. Economic and social constraints on reforestation for climate mitigation in Southeast Asia. Nat. Clim. Chang. 10, 842–844 (2020).Article 

Google Scholar 
72.van Katwijk, M. M. et al. Global analysis of seagrass restoration: the importance of large-scale planting. J. Appl. Ecol. 53, 567–578 (2016).Article 

Google Scholar 
73.Waycott, M. et al. Accelerating loss of seagrasses across the globe threatens coastal ecosystems. Proc. Natl Acad. Sci. USA 106, 12377–12381 (2009).Article 

Google Scholar 
74.Orth, R. J. et al. A global crisis for seagrass ecosystems. Bioscience 56, 987–996 (2006).Article 

Google Scholar 
75.Tan, Y. M. et al. Seagrass restoration is possible: insights and lessons from Australia and New Zealand. Front. Mar. Sci. 7, 617 (2020).Article 

Google Scholar 
76.Greiner, J. T., McGlathery, K. J., Gunnell, J. & McKee, B. A. Seagrass restoration enhances ‘blue carbon’ sequestration in coastal waters. PLoS ONE 8, e72469 (2013).Article 

Google Scholar 
77.Orth, R. J. et al. Restoration of seagrass habitat leads to rapid recovery of coastal ecosystem services. Sci. Adv. 6, eabc6434 (2020).Article 

Google Scholar 
78.Cunha, A. H. et al. Changing paradigms in seagrass restoration. Restor. Ecol. 20, 427–430 (2012).Article 

Google Scholar 
79.Rezek, R. J., Furman, B. T., Jung, R. P., Hall, M. O. & Bell, S. S. Long-term performance of seagrass restoration projects in Florida, USA. Sci. Rep. 9, 15514 (2019).Article 

Google Scholar 
80.Worthington, T. A. et al. Harnessing big data to support the conservation and rehabilitation of mangrove forests globally. One Earth 2, 429–443 (2020).Article 

Google Scholar 
81.Kandus, P. et al. Remote sensing of wetlands in South America: status and challenges. Int. J. Remote Sens. 39, 993–1016 (2018).Article 

Google Scholar 
82.Gallant, A. L. The challenges of remote monitoring of wetlands. Remote Sens. 7, 10938–10950 (2015).Article 

Google Scholar 
83.Unsworth, R. K. F. et al. Sowing the seeds of seagrass recovery using hessian bags. Front. Ecol. Evol. 7, 311 (2019).Article 

Google Scholar 
84.Duarte, C. M., Dennison, W. C., Orth, R. J. W. & Carruthers, T. J. B. The charisma of coastal ecosystems: addressing the imbalance. Estuaries Coasts 31, 233–238 (2008).Article 

Google Scholar 
85.de los Santos, C. B. et al. Recent trend reversal for declining European seagrass meadows. Nat. Commun. 10, 3356 (2019).Article 

Google Scholar 
86.Hamilton, S. E. & Casey, D. Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21). Glob. Ecol. Biogeogr. 25, 729–738 (2016).Article 

Google Scholar 
87.Pendleton, L. et al. Estimating global “blue carbon” emissions from conversion and degradation of vegetated coastal ecosystems. PLoS ONE 7, e43542 (2012).Article 

Google Scholar 
88.Deegan, L. A. et al. Coastal eutrophication as a driver of salt marsh loss. Nature 490, 388–392 (2012).Article 

Google Scholar 
89.Cardoso, P. G., Raffaelli, D. & Pardal, M. A. The impact of extreme weather events on the seagrass Zostera noltii and related Hydrobia ulvae population. Mar. Pollut. Bull. 56, 483–492 (2008).Article 

Google Scholar 
90.Rogers, K. Accommodation space as a framework for assessing the response of mangroves to relative sea-level rise. Singap. J. Trop. Geogr. 42, 163–183 (2021).Article 

Google Scholar 
91.Marbà, N. & Duarte, C. M. Mediterranean warming triggers seagrass (Posidonia oceanica) shoot mortality. Glob. Chang. Biol. 16, 2366–2375 (2010).Article 

Google Scholar 
92.Lefcheck, J. S., Wilcox, D. J., Murphy, R. R., Marion, S. R. & Orth, R. J. Multiple stressors threaten the imperiled coastal foundation species eelgrass (Zostera marina) in Chesapeake Bay, USA. Glob. Chang. Biol. 23, 3474–3483 (2017).Article 

Google Scholar 
93.Arias-Ortiz, A. et al. A marine heatwave drives massive losses from the world’s largest seagrass carbon stocks. Nat. Clim. Chang. 8, 338–344 (2018).Article 

Google Scholar 
94.Kendrick, G. A. et al. A systematic review of how multiple stressors from an extreme event drove ecosystem-wide loss of resilience in an iconic seagrass community. Front. Mar. Sci. 6, 455 (2019).Article 

Google Scholar 
95.Duke, N. C. et al. Large-scale dieback of mangroves in Australia’s Gulf of Carpentaria: a severe ecosystem response, coincidental with an unusually extreme weather event. Mar. Freshw. Res. 68, 1816–1829 (2017).Article 

Google Scholar 
96.Taillie, P. J. et al. Widespread mangrove damage resulting from the 2017 Atlantic mega hurricane season. Environ. Res. Lett. 15, 64010 (2020).Article 

Google Scholar 
97.Asbridge, E., Lucas, R., Rogers, K. & Accad, A. The extent of mangrove change and potential for recovery following severe Tropical Cyclone Yasi, Hinchinbrook Island, Queensland, Australia. Ecol. Evol. 8, 10416–10434 (2018).Article 

Google Scholar 
98.Hickey, S. M. et al. Is climate change shifting the poleward limit of mangroves? Estuaries Coasts 40, 1215–1226 (2017).Article 

Google Scholar 
99.Saintilan, N., Wilson, N. C., Rogers, K., Rajkaran, A. & Krauss, K. W. Mangrove expansion and salt marsh decline at mangrove poleward limits. Glob. Chang. Biol. 20, 147–157 (2014).Article 

Google Scholar 
100.Whitt, A. A. et al. March of the mangroves: drivers of encroachment into southern temperate saltmarsh. Estuar. Coast. Shelf Sci. 240, 106776 (2020).Article 

Google Scholar 
101.Cavanaugh, K. C. et al. Sensitivity of mangrove range limits to climate variability. Glob. Ecol. Biogeogr. 27, 925–935 (2018).Article 

Google Scholar 
102.Cavanaugh, K. C. et al. Poleward expansion of mangroves is a threshold response to decreased frequency of extreme cold events. Proc. Natl Acad. Sci. USA 111, 723–727 (2014).Article 

Google Scholar 
103.Coldren, G. A., Langley, J. A., Feller, I. C. & Chapman, S. K. Warming accelerates mangrove expansion and surface elevation gain in a subtropical wetland. J. Ecol. 107, 79–90 (2019).Article 

Google Scholar 
104.Yando, E. S. et al. Salt marsh–mangrove ecotones: using structural gradients to investigate the effects of woody plant encroachment on plant–soil interactions and ecosystem carbon pools. J. Ecol. 104, 1020–1031 (2016).Article 

Google Scholar 
105.Doughty, C. L. et al. Mangrove range expansion rapidly increases coastal wetland carbon storage. Estuaries Coasts 39, 385–396 (2016).Article 

Google Scholar 
106.Lovelock, C. E. et al. Sea level and turbidity controls on mangrove soil surface elevation change. Estuar. Coast. Shelf Sci. 153, 1–9 (2015).Article 

Google Scholar 
107.Woodroffe, C. D. et al. Mangrove sedimentation and response to relative sea-level rise. Ann. Rev. Mar. Sci. 8, 243–266 (2016).Article 

Google Scholar 
108.Lovelock, C. E. & Reef, R. Variable impacts of climate change on blue carbon. One Earth 3, 195–211 (2020).Article 

Google Scholar 
109.Saintilan, N. et al. Thresholds of mangrove survival under rapid sea level rise. Science 368, 1118–1121 (2020).Article 

Google Scholar 
110.Nicholls, R. J. Coastal flooding and wetland loss in the 21st century: changes under the SRES climate and socio-economic scenarios. Glob. Environ. Chang. 14, 69–86 (2004).Article 

Google Scholar 
111.Schuerch, M. et al. Future response of global coastal wetlands to sea-level rise. Nature 561, 231–234 (2018).Article 

Google Scholar 
112.Adame, M. F. et al. Future carbon emissions from global mangrove forest loss. Glob. Chang. Biol. 27, 2856–2866 (2021).Article 

Google Scholar 
113.Griscom, B. W. et al. Natural climate solutions. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).Article 

Google Scholar 
114.Friedlingstein, P. et al. Global Carbon Budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).Article 

Google Scholar 
115.Morris, R. L., Boxshall, A. & Swearer, S. E. Climate-resilient coasts require diverse defence solutions. Nat. Clim. Chang. 10, 485–487 (2020).Article 

Google Scholar 
116.Macreadie, P. I. et al. The future of blue carbon science. Nat. Commun. 10, 3998 (2019).Article 

Google Scholar 
117.Wylie, L., Sutton-Grier, A. E. & Moore, A. Keys to successful blue carbon projects: lessons learned from global case studies. Mar. Policy 65, 76–84 (2016).Article 

Google Scholar 
118.Howard, J. F. et al. Clarifying the role of coastal and marine systems in climate mitigation. Front. Ecol. Environ. 15, 42–50 (2017).Article 

Google Scholar 
119.Lenihan, H. S. & Peterson, C. H. How habitat degradation through fishery disturbance enhances impacts of hypoxia on oysters reefs. Ecol. Appl. 8, 128–140 (1998).Article 

Google Scholar 
120.Ellison, A. M., Felson, A. J. & Friess, D. A. Mangrove rehabilitation and restoration as experimental adaptive management. Front. Mar. Sci. 7, 327 (2020).Article 

Google Scholar 
121.Lester, S. E., Dubel, A. K., Hernan, G., McHenry, J. & Rassweiler, A. Spatial planning principles for marine ecosystem restoration. Front. Mar. Sci. 7, 328 (2020).Article 

Google Scholar 
122.Herr, D. & Landis, E. Coastal blue carbon ecosystems: opportunities for nationally determined contributions. Policy brief (IUCN, 2016).123.Apple Newsroom. Conserving mangroves, a lifeline for the world. Apple (22 April 2019) https://www.apple.com/newsroom/2019/04/conserving-mangroves-a-lifeline-for-the-world124.Hochard, J. P., Hamilton, S. & Barbier, E. B. Mangroves shelter coastal economic activity from cyclones. Proc. Natl Acad. Sci. USA 116, 12232–12237 (2019).Article 

Google Scholar 
125.Herr, D., von Unger, M., Laffoley, D. & McGivern, A. Pathways for implementation of blue carbon initiatives. Aquat. Conserv. Mar. Freshw. Ecosyst. 27, 116–129 (2017).Article 

Google Scholar 
126.Friess, D. A. et al. in Sustainable Development Goals: Their Impacts on Forests and People Ch. 14 (eds Katila, P. et al.) 445–481 (Cambridge Univ. Press, 2019).127.Waltham, N. J. et al. UN Decade on Ecosystem Restoration 2021–2030 — what chance for success in restoring coastal ecosystems? Front. Mar. Sci. 7, 71 (2020).Article 

Google Scholar 
128.Convention on Biological Diversity. Conference of the Parties Decision X/2: strategic plan for biodiversity 2011–2020. CBD https://www.cbd.int/decision/cop/?id=12268 (2011).129.United Nations. Transforming our world: the 2030 Agenda for Sustainable Development (UN, 2015).130.Brander, L. M. et al. The global costs and benefits of expanding marine protected areas. Mar. Policy 116, 103953 (2020).Article 

Google Scholar 
131.Howard, J. F. et al. The potential to integrate blue carbon into MPA design and management. Aquat. Conserv. 27, 100–115 (2017).Article 

Google Scholar 
132.Needelman, B. A. et al. The science and policy of the Verified Carbon Standard methodology for tidal wetland and seagrass restoration. Estuaries Coasts 41, 2159–2171 (2018).Article 

Google Scholar 
133.Michaelowa, A., Hermwille, L., Obergassel, W. & Butzengeiger, S. Additionality revisited: guarding the integrity of market mechanisms under the Paris Agreement. Clim. Policy 19, 1211–1224 (2019).Article 

Google Scholar 
134.Intergovernmental Panel on Climate Change. 2013 Supplement to the 2006 IPCC guidelines for national greenhouse gas inventories: wetlands (IPCC, 2014).135.United Nations Environment Programme. Out of the blue: the value of seagrasses to the environment and to people (UNEP, 2020).136.Murdiyarso, D. et al. The potential of Indonesian mangrove forests for global climate change mitigation. Nat. Clim. Chang. 5, 1089–1092 (2015).Article 

Google Scholar 
137.Jones, T. et al. Madagascar’s mangroves: quantifying nation-wide and ecosystem specific dynamics, and detailed contemporary mapping of distinct ecosystems. Remote Sens. 8, 106 (2016).Article 

Google Scholar 
138.Holmquist, J. R. et al. Uncertainty in United States coastal wetland greenhouse gas inventorying. Environ. Res. Lett. 13, 115005 (2018).Article 

Google Scholar 
139.Maher, D. T., Drexl, M., Tait, D. R., Johnston, S. G. & Jeffrey, L. C. iAMES: an inexpensive, automated methane ebullition sensor. Environ. Sci. Technol. 53, 6420–6426 (2019).Article 

Google Scholar 
140.Primavera, J. H. & Esteban, J. M. A. A review of mangrove rehabilitation in the Philippines: successes, failures and future prospects. Wetl. Ecol. Manag. 16, 345–358 (2008).Article 

Google Scholar 
141.Silliman, B. R. et al. Facilitation shifts paradigms and can amplify coastal restoration efforts. Proc. Natl Acad. Sci. USA 112, 14295–14300 (2015).Article 

Google Scholar 
142.Enwright, N. M., Griffith, K. T. & Osland, M. J. Barriers to and opportunities for landward migration of coastal wetlands with sea-level rise. Front. Ecol. Environ. 14, 307–316 (2016).Article 

Google Scholar 
143.Burkholz, C., Garcias-Bonet, N. & Duarte, C. M. Warming enhances carbon dioxide and methane fluxes from Red Sea seagrass (Halophila stipulacea) sediments. Biogeosciences 17, 1717–1730 (2020).Article 

Google Scholar 
144.Bianchi, T. S. et al. Historical reconstruction of mangrove expansion in the Gulf of Mexico: linking climate change with carbon sequestration in coastal wetlands. Estuar. Coast. Shelf Sci. 119, 7–16 (2013).Article 

Google Scholar 
145.Apostolaki, E. T. et al. Exotic Halophila stipulacea is an introduced carbon sink for the eastern Mediterranean Sea. Sci. Rep. 9, 9643 (2019).Article 

Google Scholar 
146.Bell, J. & Lovelock, C. E. Insuring mangrove forests for their role in mitigating coastal erosion and storm-surge: an Australian case study. Wetlands 33, 279–289 (2013).Article 

Google Scholar 
147.Reguero, B. G. et al. Financing coastal resilience by combining nature-based risk reduction with insurance. Ecol. Econ. 169, 106487 (2020).Article 

Google Scholar 
148.Thomas, S. Blue carbon: knowledge gaps, critical issues, and novel approaches. Ecol. Econ. 107, 22–38 (2014).Article 

Google Scholar 
149.International Partnership for Blue Carbon. Blue carbon partnership. IPBC https://bluecarbonpartnership.org (2017).150.Boon, P. I. & Prahalad, V. Ecologists, economics and politics: problems and contradictions in applying neoliberal ideology to nature conservation in Australia. Pac. Conserv. Biol. 23, 115–132 (2017).Article 

Google Scholar 
151.Adame, M. F. et al. The undervalued contribution of mangrove protection in Mexico to carbon emission targets. Conserv. Lett. 11, e12445 (2018).Article 

Google Scholar 
152.Bell-James, J. & Lovelock, C. E. Legal barriers and enablers for reintroducing tides: an Australian case study in reconverting ponded pasture for climate change mitigation. Land Use Policy 88, 104192 (2019).Article 

Google Scholar 
153.Gattuso, J.-P. et al. Ocean solutions to address climate change and its effects on marine ecosystems. Front. Mar. Sci. 5, 337 (2018).Article 

Google Scholar 
154.Saderne, V. et al. Role of carbonate burial in blue carbon budgets. Nat. Commun. 10, 1106 (2019).Article 

Google Scholar 
155.Duarte, C. M., Wu, J., Xiao, X., Bruhn, A. & Krause-Jensen, D. Can seaweed farming play a role in climate change mitigation and adaptation? Front. Mar. Sci. 4, 100 (2017).
Google Scholar 
156.Froehlich, H. E., Afflerbach, J. C., Frazier, M. & Halpern, B. S. Blue growth potential to mitigate climate change through seaweed offsetting. Curr. Biol. 29, 3087–3093.e3 (2019).Article 

Google Scholar 
157.Ritchie, H. & Roser, M. CO2 and greenhouse gas emissions. Our World in Data https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions (2017).158.Smith, S. V. Marine macrophytes as a global carbon sink. Science 211, 838–840 (1981).Article 

Google Scholar 
159.Intergovernmental Panel on Climate Change. Special report on the ocean and cryosphere in a changing climate (IPCC, 2019).160.Verified Carbon Standard. VM0007 REDD+ methodology framework (REDD+MF) (VCS, 2020).161.Carnell, P. E. et al. Mapping ocean wealth Australia: the value of coastal wetlands to people and nature. The Nature Conservancy https://doi.org/10.21153/carnell2019mapping (2019).162.Jänes, H. et al. Stable isotopes infer the value of Australia’s coastal vegetated ecosystems from fisheries. Fish Fish. 21, 80–90 (2020).Article 

Google Scholar 
163.Jänes, H. et al. Quantifying fisheries enhancement from coastal vegetated ecosystems. Ecosyst. Serv. 43, 101105 (2020).Article 

Google Scholar 
164.Huang, B. et al. Quantifying welfare gains of coastal and estuarine ecosystem rehabilitation for recreational fisheries. Sci. Total Environ. 710, 134680 (2020).Article 

Google Scholar  More