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            "title": "Multilabel classification of peatland plant species from high-resolution drone images",
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            "abstractNote": "Biodiversity monitoring programs are essential for detecting changes in species distributions and correlating these changes with biotic and abiotic factors. This information is crucial for identifying early problems before they become too difficult to address and for implementing effective management strategies. Traditionally, biodiversity monitoring for small plant species has relied on the quadrat method, which requires botanists to identify species in the field. While this method has its advantages, it is limited by the availability of botanists, restricting the scale of monitoring programs. In this study, we explored the potential of using high-resolution photos and artificial intelligence to estimate small plant species cover in peatlands, thereby reducing the need for field-based species identification by botanists. Our approach involves dividing quadrat images into smaller tiles, applying a multi-label classification model to each tile, and calculating species cover based on the identified tiles. Data were collected from 32 sites across Quebec, and images were annotated for five common species: Chamaedaphne calyculata, Kalmia angustifolia, Andromeda polifolia, Rhododendron groenlandicum, and Larix laricina. Our model achieved a global F1 score of 71.68 %, with the highest-performing species (Larix laricina) reaching 87.17 %. Although some species showed lower performance, the estimated species cover by our model in a whole quadrat was comparable to traditional methods. Our results demonstrate that this method offers significant advantages for monitoring broad changes in vegetation.",
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            "abstractNote": "Airborne hyperspectral imaging holds great promise for estimating plant diversity and composition, given its unprecedented combination of aerial coverage, spatial resolution, and spectral detail. Recently, there has been renewed attention toward the spectral variation hypothesis (SVH), which predicts that higher spectral variation is correlated with greater plant diversity. While several studies have highlighted methodological challenges involved with the SVH, there is little consensus about when it yields strong predictions of taxonomic, functional, and phylogenetic diversity. In part, this may be because prior studies have not explicitly considered how underlying environmental gradients drive changes in spectral and species composition. In this study, we tested the SVH separately in open vegetation (i.e. grasses and shrubs) and in forests at five sites across Canada. Generalized additive models revealed that spectral diversity was a better predictor of functional α-diversity than of taxonomic or phylogenetic α-diversity in both vegetation types. Mantel tests and Procrustes analyses revealed weak to moderate associations between spectral and plant β-diversity and composition in open vegetation, and moderate associations in forests. The better fit in forests appeared to be influenced by the presence of an elevational gradient and associated species turnover (from deciduous to coniferous trees); we observed weaker relationships when examining only a subset of this gradient. We suggest that the high variability in the strength of associations between plant and spectral diversity reported to date might be affected by the presence of environmental gradients. Finally, we found that different wavelength bands contributed to spectral α-diversity in open vegetation vs. forests, suggesting different spectral features are important for different vegetation types. In conclusion, spectral diversity is a potentially powerful tool for biodiversity assessment, but it requires a context-specific approach.",
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            "abstractNote": "In collaboration with the International Union for the Conservation of Nature (IUCN) Taskforce on Biodiversity and Protected Areas, countries worldwide are working to develop a new systematic approach to inform the Key Biodiversity Areas (KBA) initiative. The goal is to map KBAs from national to global scales with a baseline international standard in support of biodiversity conservation efforts. According to the IUCN standard, one of the five criteria used to identify Potential KBAs is the Ecological Integrity (EI) of the ecosystem. Sites identified with respect to EI must have an intact ecological community and be characterized by minimal anthropogenic disturbance. In this study a new EI metric, phenospectral similarity (PSpecM), has been developed and implemented in Google Earth Engine to identify potential forest stands of high EI from a large set of candidate stands. The implementation of the PSpecM requires a network of known reference sites of high EI and target ecological units of the same land cover type for comparison to help identify potential sites of high EI. Here we tested the PSpecM on ~12,000 km2 study area in Laurentian region, Quebec, Canada using Sentinel-2 and PlanetScope (Dove) satellite imagery. Considering the phenological effect on reflectance, we found a 2700 km² spatial extent, equivalent to approximately 22% of the study area, commonly delineated as potential areas of high EI by both Planetscope (Dove) and Sentinel-2. Without consideration of phenology the total area delineated as potential areas of high EI increased to 5505 km², equivalent to around 45% of the study area. Our results show that the PSpecM can be computed for rapid assessments of forest stands to identify potential areas of high EI on a large geographic scale and serve as an additional conservation tool that can be applied to the ongoing global and national identification of KBAs.",
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            "title": "Limited impact of soil inocula from arbuscular and ectomycorrhizal-dominated sites on root morphology and growth of four tree seedling species from a temperate deciduous forest",
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            "abstractNote": "The potential of tree planting as a natural climate solution is often undermined by inadequate monitoring of tree planting projects. Current monitoring methods involve measuring trees by hand for each species, requiring extensive cost, time, and labour. Advances in drone remote sensing and computer vision offer great potential for mapping and characterizing trees from aerial imagery, and large pre-trained vision models, such as the Segment Anything Model (SAM), may be a particularly compelling choice given limited labeled data. In this work, we compare SAM methods for the task of automatic tree crown instance segmentation in high resolution drone imagery of young tree plantations. We explore the potential of SAM for this task, and find that methods using SAM out-of-the-box do not outperform a custom Mask R-CNN, even with well-designed prompts, but that there is potential for methods which tune SAM further. We also show that predictions can be improved by adding Digital Surface Model (DSM) information as an input.",
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            "abstractNote": "Insect and pathogen outbreaks have a major impact on northern forest ecosystems. Even for pathogens that have been present in a region for decades, such as beech bark disease (BBD), new waves of tree mortality are expected. Hence, there is a need for innovative approaches to monitor disease advancement in real time. Here, we test whether airborne hyperspectral imaging – involving data from 344 wavelengths in the visible, near infrared (NIR) and short-wave infrared (SWIR) – can be used to assess beech bark disease severity in southern Quebec, Canada. Field data on disease severity were linked to airborne hyperspectral data for individual beech crowns. Partial least-squares regression (PLSR) models using airborne imaging spectroscopy data predicted a small proportion of the variance in beech bark disease severity: the best model had an R2 of only 0.09. Wavelengths with the strongest contributions were from the red-edge region ( 715 nm) and the SWIR ( 1287 nm), which may suggest mediation by canopy greenness, water content, and canopy architecture. Similar models using hyperspectral data taken directly on individual leaves had no explanatory power (R2 = 0). In addition, airborne and leaf-level hyperspectral datasets were uncorrelated. The failure of leaf-level models suggests that canopy structure was likely responsible for the limited predictive ability of the airborne model. Somewhat better performance in predicting disease severity was found using common band ratios for canopy greenness assessment (e.g., the Green Normalized Difference Vegetation Index, gNDVI, and the Normalized Phaeophytinization Index, NPQI); these variables explained up to 19% of the variation in disease severity. Overall, we argue that the complexity of hyperspectral data is not necessary for assessing BBD spread and that spectral data in general may not provide an efficient means of improving BBD monitoring on a larger scale.",
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            "title": "Methodological considerations for studying spectral-plant diversity relationships",
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                    "firstName": "Anna L.",
                    "lastName": "Crofts"
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                    "firstName": "Robert",
                    "lastName": "Jackisch"
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                    "lastName": "Kothari"
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                    "firstName": "Paul",
                    "lastName": "Hacker"
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                    "firstName": "Nicholas",
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            "abstractNote": "The Spectral Variation Hypothesis (SVH) posits that higher spectral diversity indicates higher biodiversity, which would allow imaging spectroscopy to be used in biodiversity assessment and monitoring. However, its applicability varies due to ecological and methodological factors. Key methodological factors impacting spectral diversity metrics include spatial resolution, shadow removal, and spectral transformations. This study investigates how these methodological considerations affect the application of the SVH across ecosystems and sites. Using field and hyperspectral data from forest and open (e.g., wetland, grassland, savannah) ecosystems from five sites of the Canadian Airborne Biodiversity Observatory (CABO), we analyzed three variance-based spectral diversity metrics across and within vegetation sites, examining the effects of illumination corrections, spatial resolution, and shadow filtering on the spectral-plant functional diversity relationship. Our findings highlight that the relationship between spectral diversity metrics and functional diversity are strongly influenced by methods, especially spectral transformations. These illumination corrections notably impacted the spectral regions of importance and the resulting relationships to plant functional diversity. Depending on methodological choices, we observed correlations that varied not only in strength but also direction: in open vegetation we saw negative correlations when using brightness normalization, and positive correlations when using continuum removal. Shadow removal and spatial resolution were important but had less impact on the correlations. By systematically analyzing these methodological aspects, our study not only aims to guide researchers through potential challenges in SVH studies but also highlights the inherent sensitivity of spectral-functional diversity relationships to methodological choices. The variability and context-dependence of these relationships across and within sites emphasize the need for adaptable, site-specific approaches, presenting a key challenge in developing robust methods to enhance biodiversity monitoring and conservation strategies.",
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                {
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            "title": "SelvaBox: A high-resolution dataset for tropical tree crown detection",
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                    "creatorType": "author",
                    "firstName": "Hugo",
                    "lastName": "Baudchon"
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                {
                    "creatorType": "author",
                    "firstName": "Arthur",
                    "lastName": "Ouaknine"
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                {
                    "creatorType": "author",
                    "firstName": "Martin",
                    "lastName": "Weiss"
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                {
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                    "firstName": "Mélisande",
                    "lastName": "Teng"
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                {
                    "creatorType": "author",
                    "firstName": "Thomas R.",
                    "lastName": "Walla"
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                {
                    "creatorType": "author",
                    "firstName": "Antoine",
                    "lastName": "Caron-Guay"
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                {
                    "creatorType": "author",
                    "firstName": "Christopher",
                    "lastName": "Pal"
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                {
                    "creatorType": "author",
                    "firstName": "Etienne",
                    "lastName": "Laliberté"
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            ],
            "abstractNote": "Detecting individual tree crowns in tropical forests is essential to study these complex and crucial ecosystems impacted by human interventions and climate change. However, tropical crowns vary widely in size, structure, and pattern and are largely overlapping and intertwined, requiring advanced remote sensing methods applied to high-resolution imagery. Despite growing interest in tropical tree crown detection, annotated datasets remain scarce, hindering robust model development. We introduce SelvaBox, the largest open-access dataset for tropical tree crown detection in high-resolution drone imagery. It spans three countries and contains more than 83,000 manually labeled crowns - an order of magnitude larger than all previous tropical forest datasets combined. Extensive benchmarks on SelvaBox reveal two key findings: (1) higher-resolution inputs consistently boost detection accuracy; and (2) models trained exclusively on SelvaBox achieve competitive zero-shot detection performance on unseen tropical tree crown datasets, matching or exceeding competing methods. Furthermore, jointly training on SelvaBox and three other datasets at resolutions from 3 to 10 cm per pixel within a unified multi-resolution pipeline yields a detector ranking first or second across all evaluated datasets. Our dataset, code, and pre-trained weights are made public.",
            "genre": "",
            "repository": "arXiv",
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            "date": "2025-06-30",
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            "shortTitle": "SelvaBox",
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            "title": "Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery",
            "creators": [
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                    "firstName": "Mélisande",
                    "lastName": "Teng"
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                {
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                    "firstName": "Yoshua",
                    "lastName": "Bengio"
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                    "creatorType": "author",
                    "firstName": "Hugo",
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            ],
            "abstractNote": "Information on trees at the individual level is crucial for monitoring forest ecosystems and planning forest management. Current monitoring methods involve ground measurements, requiring extensive cost, time and labor. Advances in drone remote sensing and computer vision offer great potential for mapping individual trees from aerial imagery at broad-scale. Large pre-trained vision models, such as the Segment Anything Model (SAM), represent a particularly compelling choice given limited labeled data. In this work, we compare methods leveraging SAM for the task of automatic tree crown instance segmentation in high resolution drone imagery in three use cases: 1) boreal plantations, 2) temperate forests and 3) tropical forests. We also study the integration of elevation data into models, in the form of Digital Surface Model (DSM) information, which can readily be obtained at no additional cost from RGB drone imagery. We present BalSAM, a model leveraging SAM and DSM information, which shows potential over other methods, particularly in the context of plantations. We find that methods using SAM out-of-the-box do not outperform a custom Mask R-CNN, even with well-designed prompts. However, efficiently tuning SAM end-to-end and integrating DSM information are both promising avenues for tree crown instance segmentation models.",
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            "date": "2025-06-05",
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            "accessDate": "2025-06-06T01:44:15Z",
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            "shortTitle": "Bringing SAM to new heights",
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    {
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            "creatorSummary": "Blanchard et al.",
            "parsedDate": "2024",
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            "itemType": "journalArticle",
            "title": "Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Florence",
                    "lastName": "Blanchard"
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                {
                    "creatorType": "author",
                    "firstName": "Anne",
                    "lastName": "Bruneau"
                },
                {
                    "creatorType": "author",
                    "firstName": "Etienne",
                    "lastName": "Laliberté"
                }
            ],
            "abstractNote": "Premise Spectroscopy is a powerful remote sensing tool for monitoring plant biodiversity over broad geographic areas. Increasing evidence suggests that foliar spectral reflectance can be used to identify trees at the species level. However, most studies have focused on only a limited number of species at a time, and few studies have explored the underlying phylogenetic structure of leaf spectra. Accurate species identifications are important for reliable estimations of biodiversity from spectral data. Methods Using over 3500 leaf-level spectral measurements, we evaluated whether foliar reflectance spectra (400–2400 nm) can accurately differentiate most tree species from a regional species pool in eastern North America. We explored relationships between spectral, phylogenetic, and leaf functional trait variation as well as their influence on species classification using a hurdle regression model. Results Spectral reflectance accurately differentiated tree species (κ = 0.736, ±0.005). Foliar spectra showed strong phylogenetic signal, and classification errors from foliar spectra, although present at higher taxonomic levels, were found predominantly between closely related species, often of the same genus. In addition, we find functional and phylogenetic distance broadly control the occurrence and frequency of spectral classification mistakes among species. Conclusions Our results further support the link between leaf spectral diversity, taxonomic hierarchy, and phylogenetic and functional diversity, and highlight the potential of spectroscopy to remotely sense plant biodiversity and vegetation response to global change.",
            "publicationTitle": "American Journal of Botany",
            "publisher": "",
            "place": "",
            "date": "2024",
            "volume": "111",
            "issue": "4",
            "section": "",
            "partNumber": "",
            "partTitle": "",
            "pages": "e16314",
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            "seriesTitle": "",
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            "journalAbbreviation": "",
            "DOI": "10.1002/ajb2.16314",
            "citationKey": "blanchardFoliarSpectraAccurately2024",
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            "PMCID": "",
            "ISSN": "1537-2197",
            "archive": "",
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            "shortTitle": "",
            "language": "en",
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            "callNumber": "",
            "rights": "© 2024 The Authors. American Journal of Botany published by Wiley Periodicals LLC on behalf of Botanical Society of America.",
            "extra": "_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ajb2.16314",
            "tags": [
                {
                    "tag": "functional diversity",
                    "type": 1
                },
                {
                    "tag": "hurdle GLM",
                    "type": 1
                },
                {
                    "tag": "hyperspectral spectroscopy",
                    "type": 1
                },
                {
                    "tag": "leaf-level reflectance",
                    "type": 1
                },
                {
                    "tag": "phylogenetics",
                    "type": 1
                },
                {
                    "tag": "tree species classification",
                    "type": 1
                }
            ],
            "collections": [],
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            "dateAdded": "2025-04-29T15:55:48Z",
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            "creatorSummary": "Crofts et al.",
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            "itemType": "journalArticle",
            "title": "Linking aerial hyperspectral data to canopy tree biodiversity: An examination of the spectral variation hypothesis",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "Anna L.",
                    "lastName": "Crofts"
                },
                {
                    "creatorType": "author",
                    "firstName": "Christine I. B.",
                    "lastName": "Wallis"
                },
                {
                    "creatorType": "author",
                    "firstName": "Sabine",
                    "lastName": "St-Jean"
                },
                {
                    "creatorType": "author",
                    "firstName": "Sabrina",
                    "lastName": "Demers-Thibeault"
                },
                {
                    "creatorType": "author",
                    "firstName": "Deep",
                    "lastName": "Inamdar"
                },
                {
                    "creatorType": "author",
                    "firstName": "J. Pablo",
                    "lastName": "Arroyo-Mora"
                },
                {
                    "creatorType": "author",
                    "firstName": "Margaret",
                    "lastName": "Kalacska"
                },
                {
                    "creatorType": "author",
                    "firstName": "Etienne",
                    "lastName": "Laliberté"
                },
                {
                    "creatorType": "author",
                    "firstName": "Mark",
                    "lastName": "Vellend"
                }
            ],
            "abstractNote": "Imaging spectroscopy is emerging as a leading remote sensing method for quantifying plant biodiversity. The spectral variation hypothesis predicts that variation in plant hyperspectral reflectance is related to variation in taxonomic and functional identity. While most studies report some correlation between spectral and field-based (i.e., taxonomic and functional) expressions of biodiversity, the observed strength of association is highly variable, and the utility in applying spectral community properties to examine environmental drivers of communities remains unknown. We linked hyperspectral data acquired by airborne imaging spectrometers with precisely geolocated field plots to examine the spectral variation hypothesis along a temperate-to-boreal forest gradient in southern Québec, Canada. First, we examine the degree of association between spectral and field-based dimensions of canopy tree composition and diversity. Second, we ask whether the relationships between field-based community properties and the environment are reproduced when using spectral community properties. We found support for the spectral variation hypothesis with the strength of association generally greater for the functional than taxonomic dimension, but the strength of relationships was highly variable and dependent on the choice of method or metric used to quantify spectral and field-based community properties. Using a multivariate approach (comparisons of separate ordinations), spectral composition was moderately well correlated with field-based composition; however, the degree of association increased when univariately relating the main axes of compositional variation. Spectral diversity was most tightly associated with functional diversity metrics that quantify functional richness and divergence. For predicting canopy tree composition and diversity using environmental variables, the same qualitative conclusions emerge when hyperspectral or field-based data are used. Spatial patterns of canopy tree community properties were strongly related to the turnover from temperate-to-boreal communities, with most variation explained by elevation. Spectral composition and diversity provide a straightforward way to quantify plant biodiversity across large spatial extents without the need for a priori field observations. While commonly framed as a potential tool for biodiversity monitoring, we show that spectral community properties can be applied more widely to assess the environmental drivers of biodiversity, thereby helping to advance our understanding of the drivers of biogeographical patterns of plant communities.",
            "publicationTitle": "Ecological Monographs",
            "publisher": "",
            "place": "",
            "date": "2024",
            "volume": "94",
            "issue": "3",
            "section": "",
            "partNumber": "",
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            "pages": "e1605",
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            "seriesTitle": "",
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            "DOI": "10.1002/ecm.1605",
            "citationKey": "croftsLinkingAerialHyperspectral2024",
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            "accessDate": "2025-04-29T15:51:32Z",
            "PMID": "",
            "PMCID": "",
            "ISSN": "1557-7015",
            "archive": "",
            "archiveLocation": "",
            "shortTitle": "Linking aerial hyperspectral data to canopy tree biodiversity",
            "language": "en",
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            "rights": "© 2024 The Authors. Ecological Monographs published by Wiley Periodicals LLC on behalf of The Ecological Society of America.",
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                {
                    "tag": "boreal forest",
                    "type": 1
                },
                {
                    "tag": "hyperspectral reflectance",
                    "type": 1
                },
                {
                    "tag": "imaging spectroscopy",
                    "type": 1
                },
                {
                    "tag": "northern temperate forest",
                    "type": 1
                },
                {
                    "tag": "spectral composition",
                    "type": 1
                },
                {
                    "tag": "spectral diversity",
                    "type": 1
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                {
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            "creatorSummary": "Osei Darko et al.",
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            "title": "Phenospectral similarity as an index of ecological integrity",
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                {
                    "creatorType": "author",
                    "firstName": "Patrick",
                    "lastName": "Osei Darko"
                },
                {
                    "creatorType": "author",
                    "firstName": "Etienne",
                    "lastName": "Laliberté"
                },
                {
                    "creatorType": "author",
                    "firstName": "Margaret",
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                {
                    "creatorType": "author",
                    "firstName": "J. Pablo",
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                {
                    "creatorType": "author",
                    "firstName": "Andrew",
                    "lastName": "Gonzalez"
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                {
                    "creatorType": "author",
                    "firstName": "Juan",
                    "lastName": "Zuloaga"
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            ],
            "abstractNote": "In collaboration with the International Union for the Conservation of Nature (IUCN) Taskforce on Biodiversity and Protected Areas, countries worldwide are working to develop a new systematic approach to inform the Key Biodiversity Areas (KBAs) initiative. The goal is to map KBAs from the national to global scales with a baseline international standard in support of biodiversity conservation efforts. According to the IUCN standard, one of the five criteria used to identify potential KBAs, is the Ecological Integrity (EI) of the ecosystem. Sites identified with respect to EI must have an intact ecological community and be characterized by minimal anthropogenic disturbance. In this study, a new EI metric, phenospectral similarity (PSpecM), has been developed and implemented in Google Earth Engine to identify potential forest stands of high EI from a large set of candidate stands. The implementation of PSpecM requires a network of known reference sites of high EI and target ecological units of the same land cover type for comparison to help identify potential sites of high EI. Here, we tested PSpecM on a ∼12,000 km2 study area in the Laurentian region, Quebec, Canada, using Sentinel-2 and PlanetScope (Dove) satellite imagery. Considering the phenological effect on reflectance, we found a 2,700 km2 spatial extent, equivalent to approximately 22% of the study area, commonly delineated as potential areas of high EI by both PlanetScope (Dove) and Sentinel-2. Without consideration of phenology, the total area delineated as potential areas of high EI increased to 5,505 km2, equivalent to around 45% of the study area. Our results show that PSpecM can be computed for rapid assessments of forest stands to identify potential areas of high EI on a large geographic scale and serve as an additional conservation tool that can be applied to the ongoing global and national identification of KBAs.",
            "publicationTitle": "Frontiers in Environmental Science",
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            "volume": "12",
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            "shortTitle": "",
            "language": "English",
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            "tags": [
                {
                    "tag": "Biodiversity",
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                {
                    "tag": "Google Earth Engine",
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                    "tag": "Multi-spectral",
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                {
                    "tag": "PlanetScope",
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            "title": "Influence of temperate forest autumn leaf phenology on segmentation of tree species from UAV imagery using deep learning",
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            "abstractNote": "Remote sensing of forests has become increasingly accessible with the use of unoccupied aerial vehicles (UAV), along with deep learning, allowing for repeated high-resolution imagery and the capturing of phenological changes at larger spatial and temporal scales. In temperate forests during autumn, leaf senescence occurs when leaves change colour and drop. However, the influence of leaf senescence in temperate forests on tree species segmentation using a Convolutional Neural Network (CNN) has not yet been evaluated. Here, we acquired high-resolution UAV imagery over a temperate forest in Quebec, Canada on seven occasions between May and October 2021. We segmented and labelled 23,000 tree crowns from 14 different classes to train and validate a CNN for each imagery acquisition. The CNN-based segmentation showed the highest F1-score (0.72) at the start of leaf colouring in early September and the lowest F1-score (0.61) at peak fall colouring in early October. The timing of the events occurring during senescence, such as leaf colouring and leaf fall, varied substantially between and within species and according to environmental conditions, leading to higher variability in the remotely sensed signal. Deciduous and evergreen tree species that presented distinctive and less temporally-variable traits between individuals were better classified. While tree segmentation in a heterogenous forest remains challenging, UAV imagery and deep learning show high potential in mapping tree species. Our results from a temperate forest with strong leaf colour changes during autumn senescence show that the best performance for tree species segmentation occurs at the onset of this colour change.",
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                    "tag": "Leaf phenology",
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            "abstractNote": "Soil phosphorus (P) is a growth-limiting nutrient in tropical ecosystems, driving diverse P-acquisition strategies among plants. Particularly, mining for inorganic P through phosphomonoesterase (PME) activity is essential, given the substantial proportion of organic P in soils. Yet, the relationship between PME activity and other nutrient-acquisition root traits remains unclear. We measured root PME activity and commonly measured root traits, including root diameter, specific root length (SRL), root tissue density (RTD), and nitrogen concentration ([N]) in 18 co-occurring species across soils with varying P availability to better understand trees response to P supply. Root [N] and RTD were inversely related, and that axis was not clearly related to soil P supply. Both traits, however, correlated positively and negatively with PME activity, which responded strongly to P supply. Conversely, root diameter was inversely related to SRL, but this axis was not related to P supply. This pattern suggests that limiting similarity influenced variation along the diameter–SRL axis, explaining local trait diversity. Meanwhile, variation along the root [N]–RTD axis might best reflect environmental filtering. Overall, P availability indicator traits such as PME activity and root hairs only tended to be associated with these axes, highlighting limitations of these axes in describing convergent adaptations at local sites.",
            "publicationTitle": "New Phytologist",
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            "date": "2024",
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            "DOI": "10.1111/nph.19567",
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                    "tag": "environmental filtering",
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                    "tag": "limiting similarity",
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            "title": "A shift from phenol to silica-based leaf defences during long-term soil and ecosystem development",
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                    "creatorType": "author",
                    "firstName": "Felix",
                    "lastName": "de Tombeur"
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                    "creatorType": "author",
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                    "creatorType": "author",
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                    "firstName": "Michel-Pierre",
                    "lastName": "Faucon"
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                    "creatorType": "author",
                    "firstName": "Graham",
                    "lastName": "Zemunik"
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                {
                    "creatorType": "author",
                    "firstName": "Benjamin L.",
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                    "firstName": "Jean-Thomas",
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                    "firstName": "Grégory",
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            "abstractNote": "The resource availability hypothesis predicts that plants adapted to infertile soils have high levels of anti-herbivore leaf defences. This hypothesis has been mostly explored for secondary metabolites such as phenolics, whereas it remains underexplored for silica-based defences. We determined leaf concentrations of total phenols and silicon (Si) in plants growing along the 2-million-year Jurien Bay chronosequence, exhibiting an extreme gradient of soil fertility. We found that nitrogen (N) limitation on young soils led to a greater expression of phenol-based defences, whereas old, phosphorus (P)-impoverished soils favoured silica-based defences. Both defence types were negatively correlated at the community and individual species level. Our results suggest a trade-off among these two leaf defence strategies based on the strength and type of nutrient limitation, thereby opening up new perspectives for the resource availability hypothesis and plant defence research. This study also highlights the importance of silica-based defences under low P supply.",
            "publicationTitle": "Ecology Letters",
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            "date": "2021",
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            "DOI": "10.1111/ele.13713",
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                {
                    "tag": "phenylalanine",
                    "type": 1
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                {
                    "tag": "plant defence strategies",
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                {
                    "tag": "plant phenols",
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                {
                    "tag": "plant silicon",
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            "title": "Soil fertility shapes belowground food webs across a regional climate gradient",
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                {
                    "creatorType": "author",
                    "firstName": "Etienne",
                    "lastName": "Laliberté"
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                {
                    "creatorType": "author",
                    "firstName": "Paul",
                    "lastName": "Kardol"
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                    "firstName": "Raphael K.",
                    "lastName": "Didham"
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                    "firstName": "David A.",
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            "abstractNote": "Changes in soil fertility during pedogenesis affect the quantity and quality of resources entering the belowground subsystem. Climate governs pedogenesis, yet how climate modulates responses of soil food webs to soil ageing remains unexplored because of the paucity of appropriate model systems. We characterised soil food webs along each of four retrogressive soil chronosequences situated across a strong regional climate gradient to show that belowground communities are predominantly shaped by changes in fertility rather than climate. Basal consumers showed hump-shaped responses to soil ageing, which were propagated to higher-order consumers. There was a shift in dominance from bacterial to fungal energy channels with increasing soil age, while the root energy channel was most important in intermediate-aged soils. Our study highlights the overarching importance of soil fertility in regulating soil food webs, and indicates that belowground food webs will respond more strongly to shifts in soil resources than climate change.",
            "publicationTitle": "Ecology Letters",
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            "DOI": "10.1111/ele.12823",
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            "title": "Soil abiotic and biotic properties constrain the establishment of a dominant temperate tree into boreal forests",
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                    "creatorType": "author",
                    "firstName": "Alexis",
                    "lastName": "Carteron"
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                    "creatorType": "author",
                    "firstName": "Vlad",
                    "lastName": "Parasquive"
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                    "firstName": "Benjamin L.",
                    "lastName": "Turner"
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                    "creatorType": "author",
                    "firstName": "Mark",
                    "lastName": "Vellend"
                },
                {
                    "creatorType": "author",
                    "firstName": "Etienne",
                    "lastName": "Laliberté"
                }
            ],
            "abstractNote": "Climate warming is expected to cause the poleward and upward elevational expansion of temperate plant species, but non-climatic factors such as soils could constrain this range expansion. However, the extent to which edaphic constraints on range expansion have an abiotic (e.g. soil chemistry) or biotic (e.g. micro-organisms) origin remains undetermined. We conducted greenhouse experiments to test if the survival and growth of a major North American temperate tree species, Acer saccharum (sugar maple), is independently or jointly constrained by abiotic and biotic properties of field-collected soils from within and beyond the species' elevational range. Abiotic factors, particularly low base cation concentrations, were major constraints to seedling establishment in boreal forest soils (beyond the range edge), but insufficient arbuscular mycorrhizal fungal inoculum (biotic factor) also strongly reduced seedling performance in these soils. Synthesis. Our results suggest that forecasting future changes in forest composition under climate warming requires consideration of soil properties as well as the mycorrhizal status of tree species.",
            "publicationTitle": "Journal of Ecology",
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            "pages": "931-944",
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            "journalAbbreviation": "J Ecol",
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            "rights": "© 2020 British Ecological Society",
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            "title": "Foliar spectra and traits of bog plants across nitrogen deposition gradients",
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                    "firstName": "Alizée",
                    "lastName": "Girard"
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                    "creatorType": "author",
                    "firstName": "Anna K.",
                    "lastName": "Schweiger"
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            "abstractNote": "Bogs, as nutrient-poor ecosystems, are particularly sensitive to atmospheric nitrogen (N) deposition. Nitrogen deposition alters bog plant community composition and can limit their ability to sequester carbon (C). Spectroscopy is a promising approach for studying how N deposition affects bogs because of its ability to remotely determine changes in plant species composition in the long term as well as shorter-term changes in foliar chemistry. However, there is limited knowledge on the extent to which bog plants differ in their foliar spectral properties, how N deposition might affect those properties, and whether subtle inter- or intraspecific changes in foliar traits can be spectrally detected. The objective of the study was to assess the effect of N deposition on foliar traits and spectra. Using an integrating sphere fitted to a field spectrometer, we measured spectral properties of leaves from the four most common vascular plant species (Chamaedaphne calyculata, Kalmia angustifolia, Rhododendron groenlandicum and Eriophorum vaginatum) in three bogs in southern Qu&eacute;bec and Ontario, Canada, exposed to different atmospheric N deposition levels, including one subjected to a 18-year N fertilization experiment. We also measured chemical and morphological properties of those leaves. We found detectable intraspecific changes in leaf structural traits and chemistry (namely chlorophyll b and N concentrations) with increasing N deposition and identified spectral regions that helped distinguish the site-specific populations within each species. Most of the variation in leaf spectral, chemical, and morphological properties was among species. As such, species had distinct spectral foliar signatures, allowing us to identify them with high accuracy with partial least squares discriminant analyses (PLSDA). Predictions of foliar traits from spectra using partial least squares regression (PLSR) were generally accurate, particularly for the concentrations of N and C, soluble C, leaf water, and dry matter content (&lt;10% RMSEP). However, these multi-species PLSR models were not accurate within species, where the range of values was narrow. To improve the detection of short-term intraspecific changes in functional traits, models should be trained with more species-specific data. Our field study showing clear differences in foliar spectra and traits among species, and some within-species differences due to N deposition, suggest that spectroscopy is a promising approach for assessing long-term vegetation changes in bogs subject to atmospheric pollution.",
            "publicationTitle": "Remote Sensing",
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                    "tag": "functional traits",
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                {
                    "tag": "ombrotrophic bog",
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                {
                    "tag": "partial least squares discriminant analysis (PLSDA)",
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                {
                    "tag": "partial least squares regression (PLSR)",
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                    "tag": "peatland",
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                    "tag": "reflectance",
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            "title": "Greater root phosphatase activity in nitrogen-fixing rhizobial but not actinorhizal plants with declining phosphorus availability",
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                    "creatorType": "author",
                    "firstName": "Guochen K.",
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                    "creatorType": "author",
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                    "lastName": "Turner"
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                    "creatorType": "author",
                    "firstName": "Felipe E.",
                    "lastName": "Albornoz"
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                    "firstName": "Patrick E.",
                    "lastName": "Hayes"
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                    "creatorType": "author",
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            ],
            "abstractNote": "* The abundance of nitrogen (N)-fixing plants in ecosystems where phosphorus (P) limits plant productivity poses a paradox because N fixation entails a high P cost. One explanation for this paradox is that the N-fixing strategy allows greater root phosphatase activity to enhance P acquisition from organic sources, but evidence to support this contention is limited.\n\n\n* We measured root phosphomonoesterase (PME) activity of 10 N-fixing species, including rhizobial legumes and actinorhizal Allocasuarina species, and eight non-N-fixing species across a retrogressive soil chronosequence showing a clear shift from N to P limitation of plant growth and representing a strong natural gradient in P availability.\n\n\n* Legumes showed greater root PME activity than non-legumes, with the difference between these two groups increasing markedly as soil P availability declined. By contrast, root PME activity of actinorhizal species was always lower than that of co-occurring legumes and not different from non-N-fixing plants.\n\n\n* The difference in root PME activity between legumes and actinorhizal plants was not reflected in a greater or similar reliance on N fixation for N acquisition by actinorhizal species compared to co-occurring legumes.\n\n\n* Synthesis. Our results support the idea that N-fixing legumes show high root phosphatase activity, especially at low soil P availability, but suggest that this is a phylogenetically conserved trait rather than one directly linked to their N-fixation capacity.",
            "publicationTitle": "Journal of Ecology",
            "publisher": "",
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            "date": "2017",
            "volume": "105",
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            "pages": "1246-1255",
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            "journalAbbreviation": "J Ecol",
            "DOI": "10.1111/1365-2745.12758",
            "citationKey": "pngGreaterRootPhosphatase2017",
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                    "tag": "Fabaceae",
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                {
                    "tag": "Phosphomonoesterase",
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                {
                    "tag": "Soil chronosequence",
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                    "tag": "nitrogen paradox",
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                    "tag": "plant–soil (below-ground) interactions",
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                    "firstName": "Benjamin L.",
                    "lastName": "Turner"
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            "abstractNote": "Long-term ecosystem development involves changes in plant community composition and diversity associated with pedogenesis and nutrient availability, but comparable changes in soil microbial communities remain poorly understood. In particular, it is unclear whether the diversity of plants and microbes respond to similar abiotic drivers, or become decoupled as resources change over long time-scales. We characterized communities of archaea, bacteria and fungi in soils along a 2-million-year chronosequence of coastal dunes in a biodiversity hot spot in Western Australia. The chronosequence involves marked changes in soil pH and nutrient availability that drive major shifts in plant community composition and diversity as soils age. Patterns of α-diversity for microbial groups differed along the chronosequence. Bacterial α-diversity was greatest in intermediate-aged soils; archaeal diversity was greater in young alkaline or intermediate-aged soils, while fungal α-diversity—like plant diversity—was greatest in old, strongly weathered soils where phosphorus is the limiting nutrient. Changes in microbial community composition along the chronosequence were explained primarily by the long-term decline in soil pH, with a smaller influence of the relative abundance of plant nutrient-acquisition strategies. However, changes between the prokaryote and fungal communities, and between fungal and plant communities, became increasingly decoupled along the chronosequence, demonstrating that the coordination of change in biological communities by abiotic drivers becomes weaker during long-term ecosystem development. Several bacterial taxa, including DA101 (Verrucomicrobia), “Candidatus Solibacter” (Acidobacteria) and Gaiella (Actinobacteria), were particularly abundant on the oldest dunes, indicating that they are adapted to acquire phosphorus from extremely infertile soils. However, we cannot disentangle the influence of phosphorus from the long-term decline in soil pH along the chronosequence. Synthesis. These results provide evidence for contrasting patterns of plant and microbial community composition and α-diversity in response to acidification and nutrient depletion during long-term pedogenesis.",
            "publicationTitle": "Journal of Ecology",
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            "pages": "606-621",
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            "journalAbbreviation": "J Ecol",
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