CerrejonValeriaMansuyEtAl2020

Reference

Cerrejón, C., Valeria, O., Mansuy, N., Barbé, M., Fenton, N.J. (2020) Predictive mapping of bryophyte richness patterns in boreal forests using species distribution models and remote sensing data. Ecological Indicators, 119. (Scopus )

Abstract

Bryophytes represent an essential component of global biodiversity and play a significant role in many ecosystems, including boreal forests. In Canadian boreal forests, industrial exploitation of natural resources threatens bryophyte species and the ecological processes and services they support. However, the consideration of bryophytes in conservation issues is limited by current knowledge gaps on their distribution and diversity patterns. This is mainly due to the ineffectiveness of traditional field surveys to acquire information over large areas. Using remote sensing data in combination with species distribution models (SDMs), we aim to predict and map diversity patterns (in terms of richness) of i) total bryophytes, and ii) bryophyte guilds (mosses, liverworts and sphagna) in 28,436 km2 of boreal forests of Quebec (Canada). A bryophyte presence/absence database was used to develop four response variables: total bryophyte richness, moss richness, liverwort richness and sphagna richness. We pre-selected a group of 38 environmental predictors including climate, topography, soil moisture and drainage as well as vegetation. Then a final set of predictors was selected individually for each response variable through a two-step selection procedure. The Random Forest (RF) algorithm was used to develop spatially explicit regression models and to generate predictive cartography at 30 m resolution for the study area. Predictive mapping-associated uncertainty statistics were provided. Our models explained a significant fraction of the variation in total bryophyte and guild level richness, both in the calibration (42 to 52%) and validation sets (38 to 48%), outperforming models from previous studies. Vegetation (mainly NDVI) and climatic variables (temperature, precipitation, and freeze–thaw events) consistently appeared among the most important predictors for all bryophyte groups modeled. However, guild-level models identified differences in important factors determining the richness of each of the guilds and, therefore, in their predicted richness patterns. For example, the predictor number of days > 30 °C was especially relevant for liverworts, while drainage class, topographic position index and PALSAR HH-polarized L-band were identified among the most important predictors for sphagna. These differences have important implications for management and conservation strategies for bryophytes. This study provides evidence of the potential of remote sensing for assessing and making predictions on bryophyte diversity across the landscape. © 2020 Elsevier Ltd

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@ARTICLE { CerrejonValeriaMansuyEtAl2020,
    AUTHOR = { Cerrejón, C. and Valeria, O. and Mansuy, N. and Barbé, M. and Fenton, N.J. },
    JOURNAL = { Ecological Indicators },
    TITLE = { Predictive mapping of bryophyte richness patterns in boreal forests using species distribution models and remote sensing data },
    YEAR = { 2020 },
    NOTE = { cited By 0 },
    VOLUME = { 119 },
    ABSTRACT = { Bryophytes represent an essential component of global biodiversity and play a significant role in many ecosystems, including boreal forests. In Canadian boreal forests, industrial exploitation of natural resources threatens bryophyte species and the ecological processes and services they support. However, the consideration of bryophytes in conservation issues is limited by current knowledge gaps on their distribution and diversity patterns. This is mainly due to the ineffectiveness of traditional field surveys to acquire information over large areas. Using remote sensing data in combination with species distribution models (SDMs), we aim to predict and map diversity patterns (in terms of richness) of i) total bryophytes, and ii) bryophyte guilds (mosses, liverworts and sphagna) in 28,436 km2 of boreal forests of Quebec (Canada). A bryophyte presence/absence database was used to develop four response variables: total bryophyte richness, moss richness, liverwort richness and sphagna richness. We pre-selected a group of 38 environmental predictors including climate, topography, soil moisture and drainage as well as vegetation. Then a final set of predictors was selected individually for each response variable through a two-step selection procedure. The Random Forest (RF) algorithm was used to develop spatially explicit regression models and to generate predictive cartography at 30 m resolution for the study area. Predictive mapping-associated uncertainty statistics were provided. Our models explained a significant fraction of the variation in total bryophyte and guild level richness, both in the calibration (42 to 52%) and validation sets (38 to 48%), outperforming models from previous studies. Vegetation (mainly NDVI) and climatic variables (temperature, precipitation, and freeze–thaw events) consistently appeared among the most important predictors for all bryophyte groups modeled. However, guild-level models identified differences in important factors determining the richness of each of the guilds and, therefore, in their predicted richness patterns. For example, the predictor number of days > 30 °C was especially relevant for liverworts, while drainage class, topographic position index and PALSAR HH-polarized L-band were identified among the most important predictors for sphagna. These differences have important implications for management and conservation strategies for bryophytes. This study provides evidence of the potential of remote sensing for assessing and making predictions on bryophyte diversity across the landscape. © 2020 Elsevier Ltd },
    AFFILIATION = { Institut de Recherche sur les Forêts, Université du Québec en Abitibi-Témiscamingue, 445 boul. de l'Université, Rouyn-Noranda, Québec J9X 5E4, Canada; Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, 5320 122 st., Edmonton, AB T6H 3S5, Canada; Exploramer, 1 rue du Quai, Sainte-Anne-des-Monts, Québec G4V 2B6, Canada },
    ART_NUMBER = { 106826 },
    AUTHOR_KEYWORDS = { Black spruce forests; Conservation; Digital mapping; Indicators; Machine learning; Predictive modeling },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1016/j.ecolind.2020.106826 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089596334&doi=10.1016%2fj.ecolind.2020.106826&partnerID=40&md5=c14e7481a4cc17bc73aa6ac1d318dc18 },
}

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