Canopy Composition and Spatial Configuration Influences Beta Diversity in Temperate Regrowth Forests of Southeastern Australia

Structural features of the overstorey in managed and unmanaged forests can significantly influence plant community composition. Native Acacia species are common in temperate eucalypt forests in southeastern Australia. In these forests, intense disturbances, such as logging and wildfire, lead to high densities of regenerating trees, shrubs, and herbs. The tree layer is dominated by Acacia and Eucalyptus, that compete intensely for resources in the first decades after stand establishment. The relative abundance and size of Acacia and Eucalyptus varies widely due to stochastic factors such as dispersal, microsite variability, and weather and climatic conditions. This variability may influence the structure and composition of the herbaceous and shrub species. In the temperate forests of southeastern Australia, understorey plant diversity is assumed to be influenced by Acacia species density, rather than Eucalyptus density. To quantify the influence of Acacia and Eucalyptus density on plant community composition, we used remote sensing and machine learning methods to map canopy composition and then compare it to understorey composition. We combined unoccupied aerial vehicle (UAV or drone) imagery, supervised image classifications, and ground survey data of plant composition from post-logging regrowth forests in the Central Highlands of southeastern Australia. We found that aggregation and patch metrics of Eucalyptus and Acacia were strongly associated with understorey plant beta diversity. Increasing aggregation of Acacia and the number of Acacia patches had a significant negative effect on plant beta diversity, while the number of Eucalyptus patches had a positive influence. Our research demonstrates how accessible UAV remote sensing can be used to quantify variability in plant biodiversity in regrowth forests. This can help forest managers map patterns of plant diversity at the stand-scale and beyond to guide management activities across forested landscapes.

[1]  D. Lindenmayer,et al.  The characteristics of regeneration failure and their potential to shift wet temperate forests into alternate stable states , 2023, Forest Ecology and Management.

[2]  C. Nitschke,et al.  Concurrent assessment of functional types in extant vegetation and soil seed banks informs environmental constraints and mechanisms of plant community turnover in temperate forests of south-eastern Australia , 2022, Forest Ecology and Management.

[3]  B. Moore,et al.  Mapping canopy nitrogen‐scapes to assess foraging habitat for a vulnerable arboreal folivore in mixed‐species Eucalyptus forests , 2021, Ecology and evolution.

[4]  P. Baker,et al.  The role of climatic variability on Eucalyptus regeneration in southeastern Australia , 2021, Global Ecology and Conservation.

[5]  P. Baker,et al.  The influence of spatial patterns in foraging habitat on the abundance and home range size of a vulnerable arboreal marsupial in southeast Australia , 2021, Conservation Science and Practice.

[6]  P. Baker,et al.  Differing regeneration patterns after catastrophic fire and clearfelling: Implications for future stand dynamics and forest management , 2021 .

[7]  D. Lindenmayer,et al.  Temporal patterns of forest seedling emergence across different disturbance histories , 2021, Ecology and evolution.

[8]  C. Nitschke,et al.  Recruitment and growth dynamics of a temperate forest understorey species following wildfire in southeast Australia , 2021, Dendrochronologia.

[9]  J. Chambers,et al.  Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics , 2020, PloS one.

[10]  Katherine C. Cushman,et al.  Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees , 2020, Remote. Sens..

[11]  J. Abatzoglou,et al.  Vegetation fires in the Anthropocene , 2020, Nature Reviews Earth & Environment.

[12]  Daniel H. Thornton,et al.  Mapping foodscapes and sagebrush morphotypes with unmanned aerial systems for multiple herbivores , 2020, Landscape Ecology.

[13]  D. Kendal,et al.  New methods of spatial analysis in urban gardens inform future vegetation surveying , 2020, Landscape Ecology.

[14]  Yasumasa Hirata,et al.  Potential of UAV photogrammetry for characterization of forest canopy structure in uneven-aged mixed conifer–broadleaf forests , 2020, International Journal of Remote Sensing.

[15]  Jakub Nowosad,et al.  landscapemetrics : an open‐source R tool to calculate landscape metrics , 2019, Ecography.

[16]  A. Robinson,et al.  Competition drives the decline of a dominant midstorey tree species. Habitat implications for an endangered marsupial , 2019, Forest Ecology and Management.

[17]  David A. Coomes,et al.  Accurate Measurement of Tropical Forest Canopy Heights and Aboveground Carbon Using Structure From Motion , 2019, Remote. Sens..

[18]  Lauren T. Bennett,et al.  Predictive Ecosystem Mapping of South-Eastern Australian Temperate Forests Using Lidar-Derived Structural Profiles and Species Distribution Models , 2019, Remote. Sens..

[19]  D. Lindenmayer,et al.  Logging and fire regimes alter plant communities. , 2018, Ecological applications : a publication of the Ecological Society of America.

[20]  Raul Morais,et al.  Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry , 2017, Remote. Sens..

[21]  C. Silva,et al.  Individual tree detection from Unmanned Aerial Vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest , 2017 .

[22]  Jonathan P. Dash,et al.  Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak , 2017 .

[23]  Peijun Du,et al.  A review of supervised object-based land-cover image classification , 2017 .

[24]  Stephen B. Stewart,et al.  Improving temperature interpolation using MODIS LST and local topography: a comparison of methods in south east Australia , 2017 .

[25]  C. Nitschke,et al.  Environmental heterogeneity promotes floristic turnover in temperate forests of south-eastern Australia more than dispersal limitation and disturbance , 2017, Landscape Ecology.

[26]  Richard J. Williams,et al.  Moving from autonomous to planned adaptation in the montane forests of southeastern Australia under changing fire regimes , 2017 .

[27]  C CoopsNicholas,et al.  Unmanned aerial systems for precision forest inventory purposes: A review and case study , 2017 .

[28]  Juha Hyyppä,et al.  Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging , 2017, Remote. Sens..

[29]  G. Asner,et al.  Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation , 2017, Science.

[30]  D. Lindenmayer,et al.  Disturbance gradient shows logging affects plant functional groups more than fire. , 2016, Ecological applications : a publication of the Ecological Society of America.

[31]  Brian J. Harvey,et al.  Changing disturbance regimes, ecological memory, and forest resilience , 2016 .

[32]  J. Innes,et al.  Multiple factors influence plant richness and diversity in the cold and dry boreal forest of southwest Yukon, Canada , 2016, Plant Ecology.

[33]  Marc Olano,et al.  Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure , 2015, Remote. Sens..

[34]  David E. Knapp,et al.  Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy , 2015, PloS one.

[35]  Jianhua Gong,et al.  UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis , 2015, Remote. Sens..

[36]  T. Spies,et al.  Disturbance legacies increase the resilience of forest ecosystem structure, composition, and functioning. , 2014, Ecological applications : a publication of the Ecological Society of America.

[37]  Martin Isenburg,et al.  Generating pit-free canopy height models from airborne lidar , 2014 .

[38]  G. Williamson,et al.  Abrupt fire regime change may cause landscape‐wide loss of mature obligate seeder forests , 2014, Global change biology.

[39]  Han Y. H. Chen,et al.  Interactions between overstorey and understorey vegetation along an overstorey compositional gradient , 2013 .

[40]  Karen Anderson,et al.  Lightweight unmanned aerial vehicles will revolutionize spatial ecology , 2013 .

[41]  Stephan Getzin,et al.  Assessing biodiversity in forests using very high‐resolution images and unmanned aerial vehicles , 2012 .

[42]  H. Reynolds,et al.  Resource Heterogeneity, Soil Fertility, and Species Diversity: Effects of Clonal Species on Plant Communities , 2011, The American Naturalist.

[43]  Han Y. H. Chen,et al.  Is understory plant species diversity driven by resource quantity or resource heterogeneity? , 2010, Ecology.

[44]  Yichun Xie,et al.  Remote sensing imagery in vegetation mapping: a review , 2008 .

[45]  P. Balandier,et al.  Influence of tree species on understory vegetation diversity and mechanisms involved—A critical review for temperate and boreal forests , 2008 .

[46]  A. Hamann,et al.  Use of response functions in selecting lodgepole pine populations for future climates , 2006 .

[47]  Omri Allouche,et al.  Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS) , 2006 .

[48]  Christian Messier,et al.  Overstory influences on light attenuation patterns and understory plant community diversity and composition in southern boreal forests of Quebec , 2006 .

[49]  R. Lucas,et al.  The delineation of tree crowns in Australian mixed species forests using hyperspectral Compact Airborne Spectrographic Imager (CASI) data , 2006 .

[50]  Johannes R. Sveinsson,et al.  Random Forests for land cover classification , 2006, Pattern Recognit. Lett..

[51]  Mahesh Pal,et al.  Random forest classifier for remote sensing classification , 2005 .

[52]  Y. Bergeron,et al.  Variation of the understory composition and diversity along a gradient of productivity in Populus tremuloides stands of northern British Columbia, Canada , 2004 .

[53]  S. Popescu,et al.  Seeing the Trees in the Forest: Using Lidar and Multispectral Data Fusion with Local Filtering and Variable Window Size for Estimating Tree Height , 2004 .

[54]  A. Nicotra,et al.  Aboveground interactions and productivity in mixed-species plantations of Acacia mearnsii and Eucalyptus globulus , 2004 .

[55]  P. Ades,et al.  Review of measures of site occupancy by regeneration , 2004 .

[56]  L. Breiman.  Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[57]  Hong S. He,et al.  An aggregation index (AI) to quantify spatial patterns of landscapes , 2000, Landscape Ecology.

[58]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.

[59]  D. Ashton,et al.  Regeneration in a Pole-Stage Forest of Eucalyptus regnans Subjected to Different Fire Intensities in 1982 , 1996 .

[60]  P. Attiwill.  The disturbance of forest ecosystems: the ecological basis for conservative management , 1994 .

[61]  D. Tilman,et al.  PLANT COMPETITION AND RESOURCE AVAILABILITY IN RESPONSE TO DISTURBANCE AND FERTILIZATION , 1993 .

[62]  S. Bratton.  Resource Division in an Understory Herb Community: Responses to Temporal and Microtopographic Gradients , 1976, The American Naturalist.

[63]  Forests as Complex Social and Ecological Systems: A Festschrift for Chadwick D. Oliver , 2022, Managing Forest Ecosystems.

[64]  Edzer Pebesma,et al.  Simple Features for R: Standardized Support for Spatial Vector Data , 2018, R J..

[65]  Conghe Song,et al.  Modeling plant composition as community continua in a forest landscape with LiDAR and hyperspectral remote sensing. , 2018, Ecological applications : a publication of the Ecological Society of America.

[66]  D. Edwards,et al.  How Should Beta-Diversity Inform Biodiversity Conservation? , 2016, Trends in ecology & evolution.

[67]  Jonathan M. Chase,et al.  Navigating the multiple meanings of β diversity: a roadmap for the practicing ecologist. , 2011, Ecology letters.

[68]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[69]  R. Campbell,et al.  Development of silviculture in the native State forests of Victoria , 1999 .

[70]  D. Cole,et al.  Elemental cycling in forest ecosystems , 1981 .

[71]  D. Ashton.  The development of even-aged stands of Eucalyptus regnans F. Muell. in central Victoria , 1976 .