Assessing shortfalls and complementary conservation areas for national plant biodiversity in South Korea

Choe, Hyeyeong; Thorne, James H.; Huber, Patrick R.; Lee, Dongkun; Quinn, James

doi:10.1371/journal.pone.0190754

Cited by 14 publications

(7 citation statements)

References 44 publications

(53 reference statements)

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“…This was in the middle of Jeju Island, the largest island located in southernmost South Korea (Figure 4). S-SDMs predicted the highest species richness to be in the northeastern part of South Korea (Gangwon Province) and Jeju Island, and the results correspond with previous literature and other studies [33,73].…”

Section: Species Richness Estimation From S-sdmssupporting

confidence: 90%

Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models

2021

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The spatial patterns of species richness can be used as indicators for conservation and restoration, but data problems, including the lack of species surveys and geographical data gaps, are obstacles to mapping species richness across large areas. Lack of species data can be overcome with remote sensing because it covers extended geographic areas and generates recurring data. We developed a Deep Learning (DL) framework using Moderate Resolution Imaging Spectroradiometer (MODIS) products and modeled potential species richness by stacking species distribution models (S-SDMs) to ask, “What are the spatial patterns of potential plant species richness across the Korean Peninsula, including inaccessible North Korea, where survey data are limited?” First, we estimated plant species richness in South Korea by combining the probability-based SDM results of 1574 species and used independent plant surveys to validate our potential species richness maps. Next, DL-based species richness models were fitted to the species richness results in South Korea, and a time-series of the normalized difference vegetation index (NDVI) and leaf area index (LAI) from MODIS. The individually developed models from South Korea were statistically tested using datasets that were not used in model training and obtained high accuracy outcomes (0.98, Pearson correlation). Finally, the proposed models were combined to estimate the richness patterns across the Korean Peninsula at a higher spatial resolution than the species survey data. From the statistical feature importance tests overall, growing season NDVI-related features were more important than LAI features for quantifying biodiversity from remote sensing time-series data.

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Section: Species Richness Estimation From S-sdmssupporting

confidence: 90%

Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models

2021

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“…The difference of 97 m elevation between areas with or without observation records (average elevation of white grids is 270 m, while average elevation of observations is 367 m.) indicates that more survey efforts are needed in smaller survey units and in low‐elevation areas. In addition, Choe, Thorne, Huber, Lee, and Quinn () identified the importance of low‐elevation areas in conservation planning for South Korea. The un‐sampled grids from Figure a could be used to identify areas for additional surveys.…”

Section: Discussionmentioning

confidence: 99%

Integrating the Rabinowitz rarity framework with a National Plant Inventory in South Korea

Choe

Thorne

Hijmans

et al. 2019

Ecology and Evolution

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Increasingly large presence‐only survey datasets are becoming available for use in conservation assessments. Potentially, these records could be used to determine spatial patterns of plant species rarity and endemism. We test the integration of a large South Korean species record database with Rabinowitz rarity classes. Rabinowitz proposed seven classes of species rarity using three variables: geographic range, habitat specificity, and local population size. We estimated the range size and local abundance of 2,215 plant species from species occurrence records and habitat specificity as the number of landcover types each species’ records were found in. We classified each species into a rarity class or as common, compared species composition by class to national lists, and mapped the spatial pattern of species richness for each rarity class. Species were classed to narrow or wide geographic ranges using 315 km, the average from a range size index of all species (D max), based on maximum distance between observations. There were four classes each within the narrow and wide range groups, sorted using cutoffs of local abundance and habitat specificity. Nationally listed endangered species only appeared in the narrow‐range classes, while nationally listed endemic species appeared in almost all classes. Species richness in most rarity classes was high in northeastern South Korea especially for species with narrow ranges. Policy implications. Large presence‐only surveys may be able to estimate some classes of rarity better than others, but modification to include estimates of local abundance and habitat types, could greatly increase their utility. Application of the Rabinowitz rarity framework to such surveys can extend their utility beyond species distribution models and can identify areas that need further surveys and for conservation priority. Future studies should be aware of the subjectivity of the rarity classification and that regional scale implementations of the framework may differ.

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“…This widely used approach aims to promote connectivity by creating prioritizations that contain large, compact protected areas (Choe et al, 2018; Harris et al, 2014; Stralberg et al, 2011). It involves minimizing the total boundary (perimeter) length of reserves (i.e.…”

Section: Methodsmentioning

confidence: 99%

“…derived from land use data; Leonard et al, 2017) to describe the strength of connectivity between different places across a landscape (see Beger et al, 2010). Other approaches are based on spatial configuration, such as the widely used approach of minimizing the total boundary (perimeter) length of protected areas within a prioritization (Choe et al, 2018; Harris et al, 2014; Stralberg et al, 2011). Approaches have also aimed to maximize connectivity based on metapopulation dynamics (Lehtomäki et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

A comparison of approaches for including connectivity in systematic conservation planning

Hanson

Vincent

Schuster

et al. 2022

Journal of Applied Ecology

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Plans for expanding protected area systems (prioritizations) often aim to facilitate connectivity. To achieve this, many approaches—based on different assumptions and datasets—have been developed. However, little is known about how such approaches influence prioritizations. We examine eight approaches that aim to promote connectivity in prioritizations. Using Washington State (USA) and its avifauna as a case study, we generated prioritizations that aimed to meet species' representation targets and promote connectivity by (a) maximizing total area; (b) further maximizing species representation; (c) minimizing boundary length; and connecting areas based on (d) minimizing human pressure, (e) minimizing naturalness‐based landscape resistance, (f) minimizing focal species landscape resistance, (g) minimizing habitat heterogeneity and (h) maximizing environmental similarity. We controlled for total expenditure, species' representation, and existing land use policies to enable comparisons among prioritizations. We then used a hierarchical cluster analysis to compare prioritizations, based on which areas they selected. We also evaluated how well each approach facilitated connectivity as measured by the other approaches. We found that different approaches for promoting connectivity can lead to very different or very similar prioritizations, depending on their underlying assumptions. In particular, the boundary length approach—which is widely used in systematic conservation planning—resulted in a prioritization that was highly dissimilar to all other prioritizations. Surprisingly, approaches based on very different underlying assumptions produced similar prioritizations, such as maximizing total area and minimizing focal species landscape resistance approaches. Moreover, when comparing the prioritizations based on the level of connectivity they could facilitate, we found that none of the prioritizations facilitated a high level of connectivity for all eight approaches. Synthesis and applications. We recommend carefully considering the assumptions and limitations that underpin approaches for promoting connectivity. Our findings demonstrate that different connectivity approaches can produce marked differences in priorities and, in turn, produce trade‐offs between different approaches. Indeed, despite the ubiquity of the boundary length approach, practitioners might find that other approaches can better achieve conservation objectives. Practitioners can use our methodology for comparing different connectivity approaches to help to navigate trade‐offs among them.

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Assessing shortfalls and complementary conservation areas for national plant biodiversity in South Korea

Cited by 14 publications

References 44 publications

Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models

Mapping Potential Plant Species Richness over Large Areas with Deep Learning, MODIS, and Species Distribution Models

Integrating the Rabinowitz rarity framework with a National Plant Inventory in South Korea

A comparison of approaches for including connectivity in systematic conservation planning

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