Abstract
The early detection of plant pathogens at the landscape scale holds great promise for better managing forest ecosystem threats. In Hawai‘i, two recently described fungal species are responsible for increasingly widespread mortality in ‘ōhi‘a Metrosideros polymorpha, a foundational tree species in Hawaiian native forests. In this study, we share work from repeat laboratory and field measurements to determine if visible near-infrared and optical remote sensing can detect pre-symptomatic trees infected with these pathogens. After generating a dense time series of laboratory spectral reflectance data and red green blue (RGB) images for inoculated ‘ōhi‘a seedlings, seedlings subjected to extreme drought, and control plants, we found few obvious spectral indicators that could be used for reliable pre-symptomatic detection in the inoculated seedlings, which quickly experienced complete and total wilting following stress onset. In the field, we found similar results when we collected repeat multispectral and RGB imagery over inoculated mature trees (sudden onset of symptoms with little advance warning). We found selected vegetation indices to be reliable indicators for detecting non-specific stress in ‘ōhi‘a trees, but never providing more than five days prior warning relative to visual detection in the laboratory trials. Finally, we generated a sequence of linear support vector machine classification models from the laboratory data at time steps ranging from pre-treatment to late-stage stress. Overall classification accuracies increased with stress stage maturity, but poor model performance prior to stress onset and the sudden onset of symptoms in infected trees suggest that early detection of rapid ‘ōhi‘a death over timescales helpful for land managers remains a challenge.
Highlights
Introduced pathogens pose a major threat to forests worldwide, resulting in significant ecological, economic, and cultural costs [1,2,3]
We examined the ability of linear support vector machine (SVM) learning, applied to the laboratory spectroradiometer and red green blue (RGB) photography datasets, to discriminate between the different treatments: control, C. lukuohia inoculation, C. huliohia inoculation, and extreme drought at vegetation stress stages ranging from pre-treatment to late stage stress (Figure 3)
Similar results were found for the small unmanned aerial system (sUAS)-derived field trial data, though we only compared two vegetation indices (VIs) (NDVI and excess green minus excess red (ExG-excess red (ExR)))
Summary
Introduced pathogens pose a major threat to forests worldwide, resulting in significant ecological, economic, and cultural costs [1,2,3]. In the Hawaiian Islands, introduced fungal pathogens are causing a widespread and growing outbreak of mortality in Metrosideros polymorpha Gaud. (‘ohi‘a), a keystone species in Hawaiian native forests [4,5,6]. Trees affected by the colloquially termed “rapid ‘ohi‘a death” (ROD) display a sudden onset of visible canopy wilting and browning lasting from a period of weeks to months, related to the disruption of their internal vascular system by either of two introduced fungal. 2 o2f o23f 22 from a period of weeks to months, related to the disruption of their internal vascular system by either patohfotgwenosinintrothdeugceednufusnCgearlatpoactyhsotigse[n7s,8i]n. Detection is of great interest across the entire Hawaiian archipelago, where native forests are alreadyEuanrldyedrettheectcioonmibsionfegdreparteisnstuerreesstoafcarogsgsrethsesievnetiirnetrHodawucaeiidansparecchieipsealangdoc, lwimheartee nchatainvegefo[r1e3s,t1s4], butaprearatlirceualdayrlyunindearrethaes wcohmebreinReOd Dprhesassuyreest toof easgtgarbelsissihveitsinetlfro[1d5u]c.eOdnspaelcaiensdasncadpcelibmaasties,cshyamngpeto[m13a–tic tree1s4]h,abvuetbpeaerntiscuuclacrelsysfiunllyarmeaaspwpehderveiaRvOiDsibhleasanydethtyopeersstapbelcitsrhalitrseemlfo[t1e5s]e. Terhearlmonael orerminotceosmenbsininagti,oenithweirthalhoynpe eorrsipneccotrmalbdinaatati,ohnaws bitehen usehdytpoedrsepteeccttraavl adraiteat,yhoafsfobreeesnt sutrseesdsotorsd[2et2e–c2t4a].vOarthieetrymofetfhooredsstosftreeasrsloyrds e[t2e2c–t2io4n]. fOorthfoerremstepthaothdosgoefns, inceluadrliyng cdaentiencetioonlfactfioorn [2f5o]reasntd gpasatchhorgoemnsa,togirnacpluhdyi/nelgectrcoanniicnneoseoslf[a2c6t]i,omn ay[a2l5s]o hoanlddprogmasise forcRhOroDm.atography/electronic noses [26], may hold promise for ROD
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