Modern approaches for leveraging biodiversity collections to understand change in plant-insect interactions

被引:9
作者
Balmaki, Behnaz [1 ,2 ]
Rostami, Masoud A. A. [1 ,2 ]
Christensen, Tara [1 ,2 ]
Leger, Elizabeth A. A. [1 ,2 ]
Allen, Julie M. M. [1 ,2 ]
Feldman, Chris R. R. [1 ,2 ]
Forister, Matthew L. L. [1 ,2 ]
Dyer, Lee A. A. [1 ,2 ]
机构
[1] Univ Nevada, Dept Biol, Reno, NV 89557 USA
[2] Univ Nevada, Museum Nat Hist, Reno, NV 89557 USA
关键词
plant-pollinator; interaction network; pollen analysis; museum collection; convolutional neural network; POLLINATION RESEARCH; POLLEN; NETWORKS; DIVERSITY; DECLINES; IMPACTS; CLIMATE; SPACE;
D O I
10.3389/fevo.2022.924941
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Research on plant-pollinator interactions requires a diversity of perspectives and approaches, and documenting changing pollinator-plant interactions due to declining insect diversity and climate change is especially challenging. Natural history collections are increasingly important for such research and can provide ecological information across broad spatial and temporal scales. Here, we describe novel approaches that integrate museum specimens from insect and plant collections with field observations to quantify pollen networks over large spatial and temporal gradients. We present methodological strategies for evaluating insect-pollen network parameters based on pollen collected from museum insect specimens. These methods provide insight into spatial and temporal variation in pollen-insect interactions and complement other approaches to studying pollination, such as pollinator observation networks and flower enclosure experiments. We present example data from butterfly pollen networks over the past century in the Great Basin Desert and Sierra Nevada Mountains, United States. Complementary to these approaches, we describe rapid pollen identification methods that can increase speed and accuracy of taxonomic determinations, using pollen grains collected from herbarium specimens. As an example, we describe a convolutional neural network (CNN) to automate identification of pollen. We extracted images of pollen grains from 21 common species from herbarium specimens at the University of Nevada Reno (RENO). The CNN model achieved exceptional accuracy of identification, with a correct classification rate of 98.8%. These and similar approaches can transform the way we estimate pollination network parameters and greatly change inferences from existing networks, which have exploded over the past few decades. These techniques also allow us to address critical ecological questions related to mutualistic networks, community ecology, and conservation biology. Museum collections remain a bountiful source of data for biodiversity science and understanding global change.
引用
收藏
页数:13
相关论文
共 78 条
[1]   Tomato Fruit Detection and Counting in Greenhouses Using Deep Learning [J].
Afonso, Manya ;
Fonteijn, Hubert ;
Fiorentin, Felipe Schadeck ;
Lensink, Dick ;
Mooij, Marcel ;
Faber, Nanne ;
Polder, Gerrit ;
Wehrens, Ron .
FRONTIERS IN PLANT SCIENCE, 2020, 11
[2]   Year-to-year variation in the topology of a plant-pollinator interaction network [J].
Alarcon, Ruben ;
Waser, Nickolas M. ;
Ollerton, Jeff .
OIKOS, 2008, 117 (12) :1796-1807
[3]   A novel deep learning method for detection and classification of plant diseases [J].
Albattah, Waleed ;
Nawaz, Marriam ;
Javed, Ali ;
Masood, Momina ;
Albahli, Saleh .
COMPLEX & INTELLIGENT SYSTEMS, 2022, 8 (01) :507-524
[4]   Review of deep learning: concepts, CNN architectures, challenges, applications, future directions [J].
Alzubaidi, Laith ;
Zhang, Jinglan ;
Humaidi, Amjad J. ;
Al-Dujaili, Ayad ;
Duan, Ye ;
Al-Shamma, Omran ;
Santamaria, J. ;
Fadhel, Mohammed A. ;
Al-Amidie, Muthana ;
Farhan, Laith .
JOURNAL OF BIG DATA, 2021, 8 (01)
[5]   Non-native insects dominate daytime pollination in a high-elevation Hawaiian dryland ecosystem [J].
Aslan, Clare E. ;
Shiels, Aaron B. ;
Haines, William ;
Liang, Christina T. .
AMERICAN JOURNAL OF BOTANY, 2019, 106 (02) :313-324
[6]   POLLEN73S: An image dataset for pollen grains classification [J].
Astolfi, Gilberto ;
Goncalves, Ariadne Barbosa ;
Menezes, Geazy Vilharva ;
Brito Borges, Felipe Silveira ;
Melo Nunes Astolfi, Angelica Christina ;
Matsubara, Edson Takashi ;
Alvarez, Marco ;
Pistori, Hemerson .
ECOLOGICAL INFORMATICS, 2020, 60
[7]   Constructing more informative plant-pollinator networks: visitation and pollen deposition networks in a heathland plant community [J].
Ballantyne, G. ;
Baldock, Katherine C. R. ;
Willmer, P. G. .
PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, 2015, 282 (1814) :14-22
[8]   Reconstructing butterfly-pollen interaction networks through periods of anthropogenic drought in the Great Basin (USA) over the past century [J].
Balmaki, Behnaz ;
Christensen, Tara ;
Dyer, Lee A. .
ANTHROPOCENE, 2022, 37
[9]   Late Holocene paleoenvironmental changes in the Seal Beach wetland (California, USA): A micropaleontological perspective [J].
Balmaki, Behnaz ;
Wigand, Peter E. ;
Frontalini, Fabrizio ;
Shaw, Timothy A. ;
Avnaim-Katav, Simona ;
Rostami, Masoud Asgharian .
QUATERNARY INTERNATIONAL, 2019, 530 :14-24
[10]   Historical changes in northeastern US bee pollinators related to shared ecological traits [J].
Bartomeus, Ignasi ;
Ascher, John S. ;
Gibbs, Jason ;
Danforth, Bryan N. ;
Wagner, David L. ;
Hedtke, Shannon M. ;
Winfree, Rachael .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2013, 110 (12) :4656-4660