Monitoring spatial and temporal dynamics in plant species and community diversity is, especially in the rapidly changing Anthropocene, one of the major challenges in ecology and conservation. Long-term and accurate data from systematic survey programmes, with standardized sampling designs and rigorous protocols, are unfortunately scarce and concentrated in few regions, and there is no recent trend of improvement. For example, recent trends show that the implementation of traditional field and natural history museum collections are not keeping pace; that is, the number of species occurrences and habitat data seem inadequate to examine the changes occurring. On the other hand, increasing availability of big datasets, often derived from modern digital technology, is promisingly supplementing information to monitor changes.
In plant science, presence-only and vegetation-plot databases are two of the most common and powerful tools to supplement existing research and provide new perspectives on more complex and geographically broader questions. Floristic and vegetation data were historically almost exclusively retrieved by experts, while, more recently, an increasing contribution from citizen science programmes and naturalist community platforms is an interesting opportunity, albeit the quality of the data is debatable. Online platforms can accumulate large and updated data with relatively little effort. Otherwise, monitoring programs based on expert knowledge are still crucial to avoid biases in species identification and unbalanced efforts on particular species and habitats. Despite the high potential of the up-to-date collected information, several challenges remain. The scientific and economic value of expert data providers might, for instance, be recognized in order to re-evaluate the figure of the field botanist and to keep high quality and reliability of the data collected. Moreover, the formulation of more inclusive data sharing agreements might allow the growth of stable cross-country and cross-biomes collaborations, and enhance the interest and reliability of the research products.
In addition, merging of big datasets to tackle research questions can be challenging. Utilizing trait datasets in combination with vegetation classification datasets, for example, may help interpret trends in geographic vegetation shifts, especially in relation to climate datasets.
With the rapid development of implementing, managing, and processing big data in plant science, this collection of articles aims to bring ideas, evidence, good practice and critical appraisals on this topic. Original Research and Reviews articles in this research field are welcome. The potential topics include, but are not limited to:
1. Challenges and opportunities in enlarging collaborations across countries, biomes, and disciplines
2. Challenges in dealing with large, complex, and potentially biased big datasets
3. Local and global trends in data collection and sharing
4. Using big data to map vegetation and habitat classifications across large scales
5. Using big data to improve assessments of plant species’ and habitats’ vulnerability
6. Using big data to predict plant species and community dynamics in response to climate change and other human-related impacts
Monitoring spatial and temporal dynamics in plant species and community diversity is, especially in the rapidly changing Anthropocene, one of the major challenges in ecology and conservation. Long-term and accurate data from systematic survey programmes, with standardized sampling designs and rigorous protocols, are unfortunately scarce and concentrated in few regions, and there is no recent trend of improvement. For example, recent trends show that the implementation of traditional field and natural history museum collections are not keeping pace; that is, the number of species occurrences and habitat data seem inadequate to examine the changes occurring. On the other hand, increasing availability of big datasets, often derived from modern digital technology, is promisingly supplementing information to monitor changes.
In plant science, presence-only and vegetation-plot databases are two of the most common and powerful tools to supplement existing research and provide new perspectives on more complex and geographically broader questions. Floristic and vegetation data were historically almost exclusively retrieved by experts, while, more recently, an increasing contribution from citizen science programmes and naturalist community platforms is an interesting opportunity, albeit the quality of the data is debatable. Online platforms can accumulate large and updated data with relatively little effort. Otherwise, monitoring programs based on expert knowledge are still crucial to avoid biases in species identification and unbalanced efforts on particular species and habitats. Despite the high potential of the up-to-date collected information, several challenges remain. The scientific and economic value of expert data providers might, for instance, be recognized in order to re-evaluate the figure of the field botanist and to keep high quality and reliability of the data collected. Moreover, the formulation of more inclusive data sharing agreements might allow the growth of stable cross-country and cross-biomes collaborations, and enhance the interest and reliability of the research products.
In addition, merging of big datasets to tackle research questions can be challenging. Utilizing trait datasets in combination with vegetation classification datasets, for example, may help interpret trends in geographic vegetation shifts, especially in relation to climate datasets.
With the rapid development of implementing, managing, and processing big data in plant science, this collection of articles aims to bring ideas, evidence, good practice and critical appraisals on this topic. Original Research and Reviews articles in this research field are welcome. The potential topics include, but are not limited to:
1. Challenges and opportunities in enlarging collaborations across countries, biomes, and disciplines
2. Challenges in dealing with large, complex, and potentially biased big datasets
3. Local and global trends in data collection and sharing
4. Using big data to map vegetation and habitat classifications across large scales
5. Using big data to improve assessments of plant species’ and habitats’ vulnerability
6. Using big data to predict plant species and community dynamics in response to climate change and other human-related impacts
