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Article

Using Spectral Indices Derived from Remote Sensing Imagery to Represent Arthropod Biodiversity Gradients in a European Sphagnum Peat Bog

by
Maria A. Minor
1,*,
Sergey G. Ermilov
2,
Omid Joharchi
2 and
Dmitriy A. Philippov
3
1
School of Natural Sciences, Massey University, Palmerston North 4410, New Zealand
2
Tyumen State University, 625003 Tyumen, Russia
3
Papanin Institute for Biology of Inland Waters, Russian Academy of Sciences, 152742 Borok, Russia
*
Author to whom correspondence should be addressed.
Arthropoda 2023, 1(1), 35-46; https://doi.org/10.3390/arthropoda1010006
Submission received: 15 November 2022 / Revised: 14 December 2022 / Accepted: 20 December 2022 / Published: 31 December 2022
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Abstract

:
Monitoring of peatlands is an important conservation issue. We investigated communities of soil mites (Acari: Oribatida, Mesostigmata) inhabiting a relatively undisturbed European boreal mire characterized by a mosaic of oligotrophic and meso-eutrophic areas. We assess the potential of using remote sensing approach as a mapping and predictive tool for monitoring productivity and arthropod biodiversity in a peat bog. In georeferenced plots, Acari biodiversity, water table level, water pH and plot productivity class on the oligotrophic-eutrophic gradient were recorded. Data from the Landsat 8 OLI sensor were used to calculate several spectral indices known to represent productivity and surface moisture gradients in terrestrial ecosystems. We then explored the relationship between spectral indices, environmental gradients and biodiversity of mites. We found that several spectral indices were significantly and consistently correlated with local environmental variables and biodiversity of soil mites. The Excess Green Index performed best as a predictor of plot trophic class on the oligotrophic-eutrophic gradient and showed significant relationship with Oribatida diversity in 2016. However, following hot summer in 2019, there was no significant relationship between abundance and species richness of Oribatida and remotely sensed data; there was a weak correlation between abundance of Mesostigmata and spectral indices which represent surface moisture gradient (e.g., Normalised Difference Moisture Index). We discuss advantages and challenges of using spectral indices derived from remote sensing imagery to map biodiversity gradients in a peatland.

1. Introduction

Ombrotrophic (rain-fed) peat bogs are characterized by high water table, acidic nutrient-poor conditions and dominance of Sphagnum mosses and occur in the boreal and temperate zone on most continents [1]. Worldwide, peatland environments are important as providers of ecosystem services and as long-term carbon storage reservoirs [2,3,4].
The plant communities of peat bogs provide information on ecosystem processes such as primary production and carbon sequestration and are sensitive indicators of environmental change [5,6,7,8,9,10,11]. Based on plant communities, bog environments are usually classified along the oligotrophy–eutrophy ‘productivity’ gradient [12]. Although not strictly a nutrient gradient, as plant communities reflect predominantly the gradient of pH values, the oligotrophy–eutrophy gradient frequently also reflects the availability of nutrients [13,14]. If a bog receives an additional nutrient input, either globally in the form of nutrient pollution from atmospheric sources, or locally from mineral-rich ground water seepage or from surface streams, the resulting areas of nutrient enrichment are characterized by higher pH and characteristic changes in vegetation [15]. The second major gradient in bogs is the ground water level, driven by the micro-topography of hummocks and hollows, which gives bogs their characteristic patterning and which is linked to hydrology and carbon sequestration [1,16]. Both productivity and microtopography exert significant controls on patterns of plant and animal diversity in peat bogs.
Bog environments are a habitat for many species; among invertebrates which are abundant in peat bogs are mites (Arthropoda: Acari). Mite taxa such as Oribatida are well studied, abundant and diverse, and are known to respond to a wide range of environmental and anthropogenic stressors in bogs and elsewhere [17,18,19,20,21,22,23,24,25]. At a site level, the diversity patterns of mites are influenced by the same gradients that are recognized as richness drivers for broader groups of bog organisms, such as pH, nutrient availability and ground water level [25,26].
Peat bogs worldwide have been affected by nutrient pollution, peat extraction, drainage and other types of development, leading to degradation and loss of biological diversity [2,3,27]. Consequently, monitoring of environmental conditions and productivity of peatlands is important. Due to access difficulties and expense of ground sampling in remote peat bogs, coupled with the need for spatially-explicit landscape-scale information, various remote sensing methods have been used for monitoring of peatlands [28,29,30,31,32]. For example, remote sensing has been used to obtain information on bog hydrology and water table depth, functional types and phenology of plants, and to map vegetation classes and fertility gradients [9,28,29,31,33,34]. Other relevant values, such as the Leaf Area Index or the Normalized Difference Vegetation Index, both important parameters linked to terrestrial ecosystem productivity, are routinely mapped using spectral indices derived from remote sensing imagery [10,35,36,37,38,39].
Because satellite data can be remotely obtained and are free for satellites such as Landsat 8 or Sentinel-2, they are useful to enhance our understanding of response of bog arthropod communities to productivity gradients in less accessible areas. In this study, we investigated communities of free-living mites inhabiting a relatively undisturbed European boreal mire “Shichengskoe”. Shichengskoe mire system is characterized by a mosaic of oligotrophic and meso-eutrophic areas, and our previous data for this system show that the pH gradient and the nutrient availability gradient in this bog are linked [25]. The trophic class of sampled plots (classified on a oligotrophy–eutrophy gradient based on plant community features) was one of the best predictors of abundance and species richness of non-aquatic oribatid mites in this bog [25]. For aquatic Oribatida, water table depth was of significant importance [25]. Mesostigmatid mites of the bog have not been studied previously. Here, we aimed to assess the potential of using remote sensing data as a mapping and predictive tool for monitoring arthropod biodiversity of a peat bog. As the connection between environmental data and mite biodiversity can been demonstrated directly, we aimed to confirm the link between spectral data and environmental data and to check if the link between spectral data and biodiversity data is consistent with the ground survey results.

2. Materials and Methods

2.1. Study Site

The mire system “Boloto Shichengskoe” in Vologda region in the north-western Russia (59°56′30.4″ N, 41°16′57.1″ E, 120 m a.s.l.) is a large (15,900 ha) wetland system of predominantly lacustrine origin (Figure 1a). The central part of the mire is occupied by a shallow oligotrophic lake Shichengskoe (1060 ha). Extensive area of the mire is the oligotrophic peat bog, dominated by Sphagnum mosses. Within the oligotrophic bog, there are several ground water seeps, associated with forested (Picea-Pinus-shrublets) islands. Areas near the seeps and the south-eastern part of the mire system are meso- and eutrophic [40]. The climate in the region is humid continental (Dfb in Köppen climate classification) with long moderately cold winter (mean temperature of January −12 °C) and short warm summer (mean temperature of July 16–17 °C). Annual precipitation is 500–650 mm; snow cover lasts 165–170 days of the year [40].

2.2. Sampling on the Ground

Two ground datasets were used in this study. The first dataset (training set) was collected in July 2016 in Sphagnum-dominated communities in the ombrotrophic western part of the mire system (Figure 1b). Samples were collected from 48 plots dominated by Sphagnum species; each plot was 1 m2 in size. The minimum distance between plots was 5–7 m, but usually over 30 m. In each sampling plot, the following was recorded: GPS coordinates, Sphagnum moss identity, water table level, water pH and Sphagnum nutrient content (C, N, P, K) (see details in [25]). Based on plant community features, each plot was assigned to one of the five qualitative productivity classes (“trophic classes”) on the oligotrophic–eutrophic gradient (oligotrophic, oligo-mesotrophic, mesotrophic, meso-eutrophic, eutrophic) [41,42]. Such trophic classes do not directly reflect nutrient status, but instead are based on plant indicator species of the poor-rich (acidity–alkalinity) gradient—however, in Shichengskoe mire these trophic classes are correlated with pH and Sphagnum nutrient content measurements [25]. The distribution of trophic classes among plots was not homogeneous, as meso-eutrophic and eutrophic patches are rarer on the ground. There were 21 oligotrophic plots, 18 oligo-mesotrophic and mesotrophic plots, and 9 meso-eutrophic and eutrophic plots. Sphagnum moss for mite extraction was collected as 10 × 10 cm samples to the depth of living moss plants (including capitula and the length of stems) (one sample per plot). Mites from moss samples were extracted in modified Berlese funnels until samples were fully dry (at least for five days). Adult Oribatida were identified to a species level using published keys and original species descriptions and classified into two functional groups following [22,43]—‘aquatic’ species, living on submerged vegetation in freshwater habitats, and ‘terrestrial’ (all other species). Oribatida juveniles were excluded from abundance and richness counts.
The second dataset (validation set) was collected in August 2019 using the same methods, except Sphagnum nutrient content was not measured. Sampling was again limited to 1 m2 plots in Sphagnum-dominated communities but covered a larger extent of the mire (Figure 1b). In each sampling plot GPS coordinates, Sphagnum moss identity, trophic class on the oligotrophic–eutrophic gradient, ground water level and pH were recorded. Mites were sampled and extracted using the same methods as above. Adult Oribatida and Mesostigmata were identified to a species level. Aquatic and terrestrial Oribatida were counted separately, with juveniles excluded. Mesostigmata are all terrestrial; their juveniles were excluded from abundance and richness counts. Some of the records in this dataset were excluded from the analysis as plots were located too close to each other—at the end, the dataset comprised 42 records (see Data S1 in Supplementary Materials).

2.3. Remote Sensing Data

The U.S. Geological Survey Landsat 8 OLI/TIRS scenes for path 177, row 18 covering Vologda region were used with the 2016 (scene LC81770182016151LGN01, 30 May 2016) and 2019 (scene LC08_L2SP_177018_20190608_20200828_02_T1, 6 August 2019) data sets. The satellite imagery search criteria were (a) acquisition date close to the date of sampling and the period of maximum vegetation activity and (b) minimum cloud cover (5% threshold). The 24-Jul-2016 scene, also available, was rejected due to the larger % cloud cover and the presence of haze.
Remote sensing images require radiometric and geometric correction. The path 177 row 18 scenes for 30 May 2016 and 6 August 2019 are “level T1”, indicating that they have been geometrically corrected. As a rule, systematically corrected T1 Landsat 8 data have geodetic accuracy of ≤12 m [44].
The remote sensing data were processed using the ArcMap 10.6, Esri Inc., Redlands, CA, USA. Landsat 8 scenes were cropped to the area of interest and co-registered. The 16-bit digital numbers (DNband) recorded in the Landsat 8 OLI spectral radiance bands 2–7 were converted to the top of the atmosphere band reflectances (rband) using solar zenith angle and band-specific scaling coefficients provided in the Landsat 8 OLI metadata file [44]. Spectral bands were significantly correlated to local environmental data (Table 1). However, such correlations are difficult to interpret biologically, so we focused on known spectral indices which represent productivity gradients and surface moisture gradients in terrestrial ecosystems.
The following spectral indices representing productivity gradient and ground water level gradient were calculated for the area of interest and sampled using ground plot coordinates:
(i)
Normalized Difference Vegetation Index (NDVI), computed using band 4 (red) and band 5 (NIR) reflectances as (r5 − r4)/(r5 + r4). NDVI is an indicator of the amount of vegetation; it approaches 1.0 if a pixel contains vegetation; 0 if a pixel contains soil; and −1.0 if a pixel contains water. NDVI is commonly used in remote sensing as a proxy for productivity [36];
(ii)
Excess Green (ExG), calculated as 2r3 − r4 − r2;
(iii)
Excess Green minus Excess Red (ExG−ExR), calculated as 1.4r4 − r3 using normalized reflectances of band 2 (blue), band 3 (green) and band 4 (red) [45]. Both ExG and ExG−ExR have been found useful as proxies of gross primary productivity [45,46];
(iv)
Normalized Difference Moisture Index (NDMI), computed using band 5 (NIR) and band 6 (SWIR1) reflectances as (r5 − r6)/(r5 + r6) [47]. NDMI is used to determine vegetation water content; it is sensitive to changes in liquid water content and in spongy mesophyll of vegetation canopies [47,48], otherwise known as NDWI (normalized difference water index);
(v)
Moisture Stress Index (SWIR1/NIR), computed as r6/r5; this index is negatively correlated with surface water content and has been suggested as a broad-band index of surface moisture (reflective of water table position) in peatlands [32,49]. Moisture Stress Index is used for canopy stress analysis, productivity prediction and biophysical modeling [50].

2.4. Data Analysis

The values of spectral indices were calculated and extracted from the satellite imagery for the GPS coordinates of plots using the ArcMap 10.6 software package. The values of spectral indices were compared with local-scale environmental and biodiversity parameters for that year using Pearson correlation in R 4.0.5. Variables at a plot level in 2016 were: water table level; water pH; Sphagnum nutrient content (C, N, K, P); trophic class; Oribatida abundance and species richness (see [25] for detailed analysis of 2016 data). Variables at a plot level in 2019 were: water table level; water pH; trophic class; Oribatida abundance and species richness; Mesostigmata abundance and species richness.
The 2016 dataset was used as a training set—using the 2016 data, the spectral indices with the significant correlation to environmental and biodiversity parameters were selected to develop the regression model. Random forest regression (randomForest package in R) [51] was employed for variable selection and to predict the trophic class of ground plots. The output provides the total % variance explained, and the importance score for each explanatory variable. The significance of random forest model was tested using permutation procedure (rfUtilities package in R 4.0.5; [52]).

3. Results

Previous results showed that plot trophic class and water table depth were the two best predictors of Oribatida biodiversity in Shichengskoe mire [25]. Higher abundance and species richness of oribatid mites in 2016 were correlated with higher productivity and lower water table depth (Table 2). In 2019, we observed no relationship between plot trophic class and abundance, or species richness of mites (Table 2) and the total abundance of mites collected in 2019 sampling was low (Appendix A, Table A1 and Table A2), possibly due to the heat wave anomaly—June and July 2019 were the hottest months on record for the region. The relationship between water table level and mite diversity, on the other hand, remained consistent between 2016 and 2019, with higher abundance and richness of terrestrial Oribatida and Mesostigmata associated with lower water table (Table 2).
Landsat 8 OLI spectral indices representing productivity gradient and ground water level gradient were consistently correlated to local environmental gradients in Shichengskoe mire in both 2016 and 2019 data sets (Table 3 and Table 4). ExG and NDVI performed well as productivity indices, showing significant correlative relationship with trophic classification of sampling plots. The NDMI and SWIR1/NIR showed significant correlation with water table depth in both data sets. ExG−ExR was correlated to both trophic class and ground water level in 2016, but only to trophic class in 2019.
Spectral indices representing productivity gradient were significantly correlated with oribatid mites biodiversity in 2016, consistent with the ground data; the strongest relationship was between Oribatida biodiversity and the ExG index (Table 3, Figure 2a,b). However, spectral indices representing ground water level gradient showed no correlation with Oribatida diversity in 2016, even though the ground data (Table 2) suggest that this gradient was important. In the 2019 dataset, a significant correlation was seen between abundance and richness of Mesostigmata and spectral indices representing water table gradient, which is consistent with the ground data (Table 2 and Table 4).
Random forest regression model predicting trophic class using spectral indices was significant at p < 0.001, with ExG index again showing as the best predictor of trophic class (Table 5). The random forest model based on 2016 satellite and ground data predicted the trophic class of plots visited in 2019 with 69.0% accuracy; this of course is relying on the assumption that the trophic class did not change between 2016 and 2019.

4. Discussion

The use of data derived from remote sensing imagery for assessment and prediction of biodiversity is receiving increasing attention [53,54,55,56]. Unlike vegetation, soil communities cannot be directly linked to spectral indices, but close links between landscape forms, vegetation and soil biota allow us to link information from remote sensors to soil biodiversity patterns. There are only a few such studies—but, for example, satellite-derived spectral information has been used to predict patterns of biodiversity in soil microbiome communities, soil mesofauna (springtails) and earthworms [57,58,59].
Our results demonstrate both the potential and the limitations of using freely available satellite data for generating information on environmental gradients, productivity and biodiversity in a peatland. Among spectral productivity indices, the ExG index was the best predictor of trophic class and could be used as a broad-scale variable representing productivity gradient in the Shichengskoe mire, with a significant relationship with Oribatida diversity. Both ExG and ExG−ExR have been used successfully elsewhere as a proxy for gross primary productivity [45,46]. On the other hand, NDVI is known to perform less well in peatland environments due to atypical near-infrared reflectance of Sphagnum mosses [60,61].
The application of spectral indices derived from remote sensing imagery to map ecological gradients in peatlands presents several challenges. The first of these is the scale—for medium-scale sensors such as Landsat 8 OLI or Sentinel-2, it results in aggregation and averaging of patterns and features which are smaller than the pixel size of the sensor [28]. The use of fine-scale sensors such as IKONOS improves the accuracy of peatland land cover mapping [31] but is more costly. The second challenge is the difficulty of deriving the details of bog microtopography from the satellite sensor data. Peat bog microtopography (hummocks and hollows) are linked to hydrology and biodiversity [1,24,26]. In the Shichengskoe mire, two major environmental drivers which explained the abundance, species richness and community composition of Oribatida were trophic class, linked to acidity–alkalinity (pH) gradient and nutrients (N-P-K) availability, and water table level, linked to microtopography [25]. For aquatic Oribatida, the water table was the single most important variable [25]. Our 2019 data (this study) suggests again that microtopography and moisture are significant drivers for diversity of both Oribatida and Mesostigmata mites. A satellite sensor, by definition, records a “flat’ pixel reflectance value and loses structural information, which may result in a poor discrimination of microtopography and its effects. Soil moisture indices (such as NDMI) may have limitations in peat bog environment, especially during dry conditions [32,62]. Crichton et al. [28] used Landsat ETM+ to map the spatial distribution of six ecohydrological classes on a lowland ombrotrophic peatland in the UK; their accuracy with Landsat ETM+ bands alone was 74%; including the texture brightness layer derived from band 5 increased accuracy of prediction. Other option includes supplementing satellite imagery with airborne Light Detection and Ranging (LiDAR) data [31,63,64] or using the Synthetic Aperture Radar (SAR) backscatter in Sentinel-1 satellite [65], which adds information on surface texture and water table depth to increase accuracy.

5. Conclusions

To conclude, Landsat 8 OLI spectral indices representing productivity gradient and ground water level gradient were consistently correlated to local environmental gradients in Shichengskoe mire. Spectral indices were significantly correlated with mite biodiversity parameters, and the patterns were consistent with the ground data; the strongest relationship was between Oribatida biodiversity and the ExG index in 2016. Changing weather conditions can override local environmental gradients, as we have seen with lower abundance and diversity of Oribatida after the abnormally hot summer of 2019.
There is an opportunity to use freely available medium-scale remotely sensed data for soil biodiversity monitoring in space and time, once the links between biodiversity of specific taxa and land features which affect land reflectance values (e.g., soil type, soil moisture, vegetation) are established. Ground data should be used to validate the accuracy of predictions. Surface topography (e.g., digital elevation model) and texture (roughness, microtopography) data, if possible, should be used to supplement spectral data.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/arthropoda1010006/s1, Data S1: Sampling plots in Shichengskoe mire: sampling dates, GPS coordinates, environmental parameters, dominant Sphagnum species.

Author Contributions

Conceptualization, M.A.M. and D.A.P.; methodology, D.A.P., S.G.E., O.J. and M.A.M.; fieldwork, D.A.P.; data analysis, S.G.E., O.J. and M.A.M.; writing—original draft preparation, M.A.M.; writing—review and editing, S.G.E. and D.A.P.; funding acquisition, D.A.P. and S.G.E. All authors have read and agreed to the published version of the manuscript.

Funding

This research was partially supported by the cooperative agreement No. FEWZ-2021-0004 from the Russian Ministry of Science and Higher Education. Work by D.A. Philippov was supported within the framework of the state assignments from the Ministry of Science and Higher Education of the Russian Federation (project no. 121051100099-5). Fieldwork was carried out as a part of the Russian Science Foundation grant no. 14-14-01134 and Russian Foundation for Basic Research grant no. 18-04-00988.

Data Availability Statement

The data on mite biodiversity presented in this study are available from the corresponding author upon request. Voucher specimens for identified species are in authors’ own collection, S.E. (Oribatida) and O.J. (Mesostigmata), Tyumen State University, Tyumen, Russia. The Landsat 8 imagery is publicly available at https://www.usgs.gov/landsat-missions/landsat-data-access, accessed on 12 November 2022.

Acknowledgments

We thank Alexander A. Prokin (Papanin Institute for Biology of Inland Waters, Russian Academy of Sciences, Borok, Russia) for organizing chemical analysis, Victoria V. Yurchenko (Papanin Institute for Biology of Inland Waters, Russian Academy of Sciences, Borok, Russia) for pH analysis. We also thank the anonymous reviewers, whose constructive comments helped to improve the paper.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Abundances (raw counts) of oribatid mites in Sphagnum bog plots, Shichengskoe mire (n—number of samples).
Table A1. Abundances (raw counts) of oribatid mites in Sphagnum bog plots, Shichengskoe mire (n—number of samples).
Species 1Jul 2016,
n = 48		Aug 2019,
n = 53		SpeciesJul 2016,
n = 48		Aug 2019,
n = 53
Achipteria coleoptrata (L., 1758)222Limnozetes palmerae Behan-Pelletier, 1989 642474
Acrotritia ardua (Koch, 1841) 6316Limnozetes rugosus (Sellnick, 1923) 33346
Adoristes ovatus (Koch, 1839)1218Liochthonius alpestris (Forsslund, 1958)94334
Atropacarus striculus (Koch, 1835) 27387Malaconothrus foveolatus (Willmann, 1931)–688
Autogneta traegardhi Forsslund, 1947 1–Malaconothrus monodactylus (Michael, 1888)259333
Banksinoma lanceolata (Michael, 1885)–4Malaconothrus vietsi (Willmann, 1925)27 –
Camisia solhoeyi Colloff, 1993 3–Microppia minus (Paoli, 1908) –1
Carabodes labyrinthicus (Michael, 1879) 57–Nanhermannia comitalis Berlese, 1916 74–
Carabodes rugosior Berlese, 1916 11–Nanhermannia coronata Berlese, 1913 421182
Cepheus cepheiformis (Nicolet, 1855) 15–Nothrus pratensis Sellnick, 1928 222236
Ceratoppia bipilis (Hermann, 1804)1–Oppiella nova (Oudemans, 1902) 1446436
Ceratoppia quadridentata (Haller, 1882)2–Oribatula tibialis (Nicolet, 1855) 2–
Ceratozetes sellnicki Rajski, 1958 –1Parachipteria punctata (Nicolet, 1855) 43–
Chamobates cuspidatus (Michael, 1884) 24–Pergalumna emarginata (Banks, 1895) 1222
Diapterobates humeralis (Hermann, 1804)229Phthiracarus boresetosus Jacot, 193018414
Epidamaeus kamaensis (Sellnick, 1926)1–Phthiracarus laevigatus (Koch, 1841) 4–
Eupelops occultus (Koch, 1835) 24–Pilogalumna tenuiclava (Berlese, 1908) 4832
Eupelops strenzkei (Knülle, 1954) 1613Punctoribates sellnicki Willmann, 1928 –13
Fuscozetes fuscipes (Koch, 1844) 5–Quadroppia quadricarinata (Michael, 1885) 4–
Fuscozetes setosus (Koch, 1839) –5Rhinoppia hygrophila (Mahunka, 1987)–29
Galumna lanceata (Oudemans, 1900) 20–Scheloribates circumcarinatus Weigmann & Miko, 1998 619
Galumna obvia (Berlese, 1914) 17–Scheloribates labyrinthicus Jeleva, 1962–11
Heminothrus longisetosus (Willmann, 1925) 3–Scheloribates laevigatus (C.L. Koch, 1835) 1763
Heminothrus peltifer (Koch, 1839) 431Suctobelbella palustris (Forsslund, 1953) 14117
Heminothrus thori (Berlese, 1904) 1–Tectocepheus velatus (Michael, 1880) 56044
Hoplophthiracarus illinoisensis (Ewing, 1909) 796949Trhypochthoniellus longisetus (Berlese, 1904) 2177
Hydrozetes lacustris (Michael, 1882) 3815Trhypochthonius tectorum (Berlese, 1896) 246
Hypochthonius rufulus Koch, 1835 901Trimalaconothrus foveolatus Willmann, 1931 352–
Liebstadia similis (Michael, 1888) 33Tyrphonothrus angulatus (Willmann, 1931)547
Limnozetes ciliatus (Schrank, 1803) 354851Tyrphonothrus maior (Berlese, 1910) 80082
Oribatida total80485083
1 Identification keys: Weigmann [66]; Balogh and Balogh [67]; Norton and Behan-Pelletier [68].
Table
Table A2. Abundances (raw counts) of mesostigmatid mites in Sphagnum bog plots, Shichengskoe mire (n = 53).
Table A2. Abundances (raw counts) of mesostigmatid mites in Sphagnum bog plots, Shichengskoe mire (n = 53).
Species 1August 2019
Lysigamasus lapponicus (Trägårdh, 1910) 21
Veigaia transisale (Oudemans, 1902) 25
Veigaia nemorensis (C.L.Koch, 1839) 11
Cheiroseius bryophilus Karg, 1969 13
Cheiroseius mutilus (Berlese, 1916)9
Cheiroseius serratus (Halbert, 1915)2
Cheiroseius laelaptoides (Berlese, 1887) 5
Platyseius italicus (Berlese, 1905) 8
Ololaelaps venetus (Berlese 1903) 12
Gaeolaelaps nolli (Karg, 1962)4
Parazecon radiatus (Berlese, 1910) 54
Zercon zelawaiensis Sellnick, 1944 32
Prozecon kochi Sellnick, 194377
Epicrius bureschi Balogh, 1958 2
Acugamasus montanus (Willmann, 1936) 7
Mesostigmata total282
1 Identification keys: Karg [69]; Gilyarov and Bregetova [70]; Evans and Hyatt [71]; Evans and Till [72]; Mašán and Fend’a [73].

References

  1. Rydin, H.; Jeglum, J.K. The Biology of Peatlands, 2nd ed.; Oxford University Press: Oxford, UK, 2013. [Google Scholar]
  2. Gorham, E. Northern peatlands: Role in the carbon cycle and probable responses to climate warming. Ecol. Appl. 1991, 1, 182–195. [Google Scholar] [CrossRef] [PubMed]
  3. Limpens, J.; Berendse, F.; Blodau, C.; Canadell, J.G.; Freeman, C.; Holden, J.; Roulet, N.; Rydin, H.; Schaepman-Strub, G. Peatlands and the carbon cycle: From local processes to global implications—A synthesis. Biogeosciences 2008, 5, 1475–1491. [Google Scholar] [CrossRef] [Green Version]
  4. Kimmel, K.; Mander, Ü. Ecosystem services of peatlands: Implications for restoration. Prog. Phys. Geogr. 2010, 34, 491–514. [Google Scholar] [CrossRef]
  5. Waddington, J.M.; Griffis, T.J.; Rouse, W.R. Northern Canadian wetlands: Net ecosystem CO2 exchange and climate change. Clim. Change 1998, 40, 267–275. [Google Scholar] [CrossRef]
  6. Bubier, J.L.; Moore, T.R.; Bledzki, L.A. Effects of nutrient addition on vegetation and carbon cycling in an ombrotrophic bog. Glob. Change Biol. 2007, 13, 1168–1186. [Google Scholar] [CrossRef] [Green Version]
  7. Strack, M.; Waddington, J.M. Response of peatland carbon dioxide and methane fluxes to a water table drawdown experiment. Global Biogeochem. Cycles 2007, 21, GB1007. [Google Scholar] [CrossRef]
  8. Camill, P.; Barry, A.; Williams, E.; Andreassi, C.; Limmer, J.; Solick, D. Climate-vegetation-fire interactions and their impact on long-term carbon dynamics in a boreal peatland landscape in northern Manitoba, Canada. J. Geophys. Res. Biogeosci. 2009, 114, G04017. [Google Scholar] [CrossRef]
  9. Harris, A.; Charnock, R.; Lucas, R.M. Hyperspectral remote sensing of peatland floristic gradients. Remote Sens. Environ. 2015, 162, 99–111. [Google Scholar] [CrossRef] [Green Version]
  10. McPartland, M.Y.; Kane, E.S.; Falkowski, M.J.; Kolka, R.; Turetsky, M.R.; Palik, B.; Montgomery, R.A. The response of boreal peatland community composition and NDVI to hydrologic change, warming, and elevated carbon dioxide. Glob. Change Biol. 2019, 25, 93–107. [Google Scholar] [CrossRef] [Green Version]
  11. Tian, J.; Branfireun, B.A.; Lindo, Z. Global change alters peatland carbon cycling through plant biomass allocation. Plant Soil 2020, 455, 53–64. [Google Scholar] [CrossRef]
  12. Tahvanainen, T. Water chemistry of mires in relation to the poor-rich vegetation gradient and contrasting geochemical zones of the north-eastern fennoscandian Shield. Folia Geobot. 2004, 39, 353–369. [Google Scholar] [CrossRef]
  13. Wheeler, B.D.; Proctor, M.C.F. Ecological gradients, subdivisions and terminology of north-west European mires. J. Ecol. 2000, 88, 187–203. [Google Scholar] [CrossRef]
  14. Bragazza, L.; Gerdol, R. Are nutrient availability and acidity-alkalinity gradients related in Sphagnum-dominated peatlands? J. Veg. Sci. 2002, 13, 473–482. [Google Scholar] [CrossRef]
  15. Ruuhijärvi, R.; Lindholm, T. Ecological gradients as the basis of Finnish mire site type system. In Finland—Land of Mires; Lindholm, T., Heikkilä, R., Eds.; The Finnish Environment 23/2006; Finnish Environment Institute: Helsinki, Finland, 2006; pp. 119–126. [Google Scholar]
  16. Hajkova, P.; Hajek, M. Sphagnum distribution patterns along environmental gradients in Bulgaria. J. Bryol. 2007, 29, 18–26. [Google Scholar] [CrossRef]
  17. Markkula, I. Comparison of the communities of oribatids (Acari: Cryptostigmata) of virgin and forest ameliorated pine bogs. Ann. Zool. Fenn. 1986, 23, 33–38. [Google Scholar]
  18. Borcard, D.; Von Ballmoos, V.C. Oribatid mites (Acari, Oribatida) of a primary peat bog pasture transition in the Swiss Jura Mountains. Ecoscience 1997, 4, 470–479. [Google Scholar] [CrossRef]
  19. Starý, J. Contribution to the knowledge of the oribatid mite fauna (Acari, Oribatida) of peat bogs in Bohemian Forest. Silva Gabreta 2006, 12, 35–47. [Google Scholar]
  20. Gergócs, V.; Hufnagel, L. Application of oribatid mites as indicators (review). AEER 2009, 7, 79–98. [Google Scholar] [CrossRef]
  21. Gulvik, M.E. Mites (Acari) as indicators of soil biodiversity and land use monitoring: A review. Pol. J. Ecol. 2007, 55, 415–440. [Google Scholar]
  22. Seniczak, A. Oribatid mites (Acari, Oribatida) and their seasonal dynamics in a floating bog mat in Jeziorka Kozie Reserve, Tuchola Forest (Poland). Biol. Lett. 2011, 48, 3–11. [Google Scholar] [CrossRef] [Green Version]
  23. Lehmitz, R. The oribatid mite community of a German peatland in 1987 and 2012—Effects of anthropogenic desiccation and afforestation. Soil Org. 2014, 86, 131–145. [Google Scholar]
  24. Minor, M.A.; Ermilov, S.G.; Philippov, D.A.; Prokin, A.A. Relative importance of local habitat complexity and regional factors for assemblages of oribatid mites (Acari: Oribatida) in Sphagnum peat bogs. Exp. Appl. Acarol. 2016, 70, 275–286. [Google Scholar] [CrossRef] [PubMed]
  25. Minor, M.A.; Ermilov, S.G.; Philippov, D.A. Hydrology-driven environmental variability determines abiotic characteristics and Oribatida diversity patterns in a Sphagnum peatland system. Exp. Appl. Acarol. 2019, 77, 43–58. [Google Scholar] [CrossRef] [PubMed]
  26. Donaldson, G.M. Oribatida (Acari) associated with three species of Sphagnum at Spruce Hole Bog, New Hampshire, US. Can. J. Zool. 1996, 74, 1713–1720. [Google Scholar] [CrossRef]
  27. Kreyling, J.; Tanneberger, F.; Jansen, F.; van der Linden, S.; Aggenbach, C.; Blüml, V.; Jurasinski, G. Rewetting does not return drained fen peatlands to their old selves. Nat. Comm. 2021, 12, 5693. [Google Scholar] [CrossRef]
  28. Crichton, K.A.; Anderson, K.; Bennie, J.J.; Milton, E.J. Characterizing peatland carbon balance estimates using freely available Landsat ETM+ data. Ecohydrology 2015, 8, 493–503. [Google Scholar] [CrossRef]
  29. Harris, A.; Bryant, R.G. A multi-scale remote sensing approach for monitoring northern peatland hydrology: Present possibilities and future challenges. J. Environ. Manag. 2009, 90, 2178–2188. [Google Scholar] [CrossRef]
  30. Dissanska, M.; Bernier, M.; Payette, S. Object-based classification of very high resolution panchromatic images for evaluating recent change in the structure of patterned peatlands. Can. J. Remote Sens. 2009, 35, 189–215. [Google Scholar] [CrossRef]
  31. Anderson, K.; Bennie, J.J.; Milton, E.J.; Hughes, P.D.M.; Lindsay, R.; Meade, R. Combining LiDAR and IKONOS data for eco-hydrological classification of an ombrotrophic bog. J. Environ. Qual. 2010, 39, 260–273. [Google Scholar] [CrossRef]
  32. Meingast, K.M.; Falkowski, M.J.; Kane, E.S.; Potvin, L.R.; Benscoter, B.W.; Smith, A.M.S.; Bourgeau-Chavez, L.L.; Miller, M.E. Spectral detection of near-surface moisture content and water-table position in northern peatland ecosystems. Remote Sens. Environ. 2014, 152, 536–546. [Google Scholar] [CrossRef]
  33. Middleton, M.; Närhi, P.; Arkimaa, H.; Hyvönen, E.; Kuosmanen, V.; Treitz, P.; Sutinen, R. Ordination and hyperspectral remote sensing approach to classify peatland biotopes along soil moisture and fertility gradients. Remote Sens. Environ. 2012, 124, 596–609. [Google Scholar] [CrossRef]
  34. Linkosalmi, M.; Tuovinen, J.P.; Nevalainen, O.; Peltoniemi, M.; Taniş, C.M.; Arslan, A.N.; Aurela, M. Tracking vegetation phenology of pristine northern boreal peatlands by combining digital photography with CO2 flux and remote sensing data. Biogeosciences 2022, 19, 4747–4765. [Google Scholar] [CrossRef]
  35. Pettorelli, N.; Vik, J.O.; Mysterud, A.; Gaillard, J.M.; Tucker, C.J.; Stenseth, N.C. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol. 2005, 20, 503–510. [Google Scholar] [CrossRef]
  36. Boelman, N.T.; Stieglitz, M.; Rueth, H.M.; Sommerkorn, M.; Griffin, K.L.; Shaver, G.R.; Gamon, J.A. Response of NDVI, biomass, and ecosystem gas exchange to long-term warming and fertilization in wet sedge tundra. Oecologia 2003, 135, 414–421. [Google Scholar] [CrossRef]
  37. Sonnentag, O.; Chen, J.M.; Roberts, D.A.; Talbot, J.; Halligan, K.; Govind, A. Mapping tree and shrub leaf area indices in an ombrotrophic peatland through multiple end member spectral unmixing. Int. J. Remote Sens. 2007, 109, 342–360. [Google Scholar]
  38. Dube, T.; Pandit, S.; Shoko, C.; Ramoelo, A.; Mazvimavi, D.; Dalu, T. Numerical assessments of leaf area index in tropical savanna rangelands, South Africa using Landsat 8 OLI derived metrics and in-situ measurements. Remote Sens. 2019, 11, 829. [Google Scholar] [CrossRef] [Green Version]
  39. Räsänen, A.; Juutinen, S.; Kalacska, M.; Aurela, M.; Heikkinen, P.; Mäenpää, K.; Virtanen, T. Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing. GIScience Remote Sens. 2020, 57, 943–964. [Google Scholar] [CrossRef]
  40. Philippov, D.A.; Ermilov, S.G.; Zaytseva, V.L.; Pestov, S.V.; Kuzmin, E.A.; Shabalina, J.N.; Sazhnev, A.S.; Ivicheva, K.N.; Sterlyagova, I.N.; Leonov, M.M.; et al. Biodiversity of a boreal mire, including its hydrographic network (Shichengskoe mire, north-western Russia). Biodivers. Data J. 2021, 9, e77615. [Google Scholar] [CrossRef]
  41. Rydin, H.; Sjörs, H.; Löfroth, M. Mires. Acta Phytogeogr. Suec. 1999, 84, 91–112. [Google Scholar]
  42. Eurola, S.; Huttunen, A. Mire plant species and their ecology in Finland. In Finland—Land of Mires; Lindholm, T., Heikkilä, R., Eds.; The Finnish Environment 23/2006; Finnish Environment Institute: Helsinki, Finland, 2006; pp. 127–144. [Google Scholar]
  43. Weigmann, G.; Deichsel, R. Acari: Limnic Oribatida. In Chelicerata: Araneae, Acari I. Susswasserfauna von Mitteleuropa; Gerecke, R., Ed.; Elsevier Spektrum Akademischer Verlag: München, Germany, 2006; Volume 7. [Google Scholar]
  44. Landsat 8 (L8) Data Users Handbook. Version 5.0, U.S. Geological Survey. 2019. Available online: https://www.usgs.gov/landsat-missions/landsat-8-data-users-handbook (accessed on 20 May 2022).
  45. Meyer, G.E.; Neto, J.C. Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric. 2008, 63, 282–293. [Google Scholar] [CrossRef]
  46. Liu, Z.; Hu, H.; Yu, H.; Yang, X.; Yang, H.; Ruan, C.; Wang, Y.; Tang, J. Relationship between leaf physiologic traits and canopy color indices during the leaf expansion period in an oak forest. Ecosphere 2015, 6, 259. [Google Scholar] [CrossRef]
  47. Gao, B.-C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 1996, 58, 257–266. [Google Scholar] [CrossRef]
  48. Ceccato, P.; Flasse, S.; Tarantola, S.; Jacquemoud, S.; Grégoire, J.M. Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sens. Environ. 2001, 77, 22–33. [Google Scholar] [CrossRef]
  49. Rock, B.N.; Vogelmann, J.E.; Williams, D.L.; Vogelmann, A.F.; Hoshizaki, T. Remote detection of forest damage. Bioscience 1986, 36, 439–445. [Google Scholar] [CrossRef]
  50. Welikhe, P.; Quansah, J.E.; Fall, S.; Elhenney, W.M. Estimation of soil moisture percentage using LANDSAT-based Moisture Stress Index. J. Remote Sens. GIS 2017, 6, 200. [Google Scholar] [CrossRef]
  51. Genuer, R.; Poggi, J.-M.; Tuleau-Malot, C. Variable selection using Random Forests. Pattern Recognit. Lett. 2010, 31, 2225–2236. [Google Scholar] [CrossRef]
  52. Murphy, M.A.; Evans, J.S.; Storfer, A.S. Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics. Ecology 2010, 91, 252–261. [Google Scholar] [CrossRef] [Green Version]
  53. Chust, G.; Lek, S.; Deharveng, L.; Ventura, D.; Ducrot, D.; Pretus, J. The effects of the landscape pattern on arthropod assemblages: An analysis of scale-dependence using satellite data. Belg. J. Entomol. 2000, 2, 99–110. [Google Scholar]
  54. Hamilton, S.K.; Kellndorfer, J.; Lehner, B.; Tobler, M. Remote sensing of floodplain geomorphology as a surrogate for biodiversity in a tropical river system (Madre de Dios, Peru). Geomorphology 2007, 89, 23–38. [Google Scholar] [CrossRef]
  55. Madonsela, S.; Cho, M.A.; Ramoelo, A.; Mutanga, O. Remote sensing of species diversity using Landsat 8 spectral variables. ISPRS J. Photogramm. Remote Sens. 2017, 133, 116–127. [Google Scholar] [CrossRef] [Green Version]
  56. Rocchini, D.; Boyd, D.S.; Féret, J.B.; Foody, G.M.; He, K.S.; Lausch, A.; Pettorelli, N. Satellite remote sensing to monitor species diversity: Potential and pitfalls. Remote Sens. Ecol. Conserv. 2016, 2, 25–36. [Google Scholar] [CrossRef]
  57. Chust, G.; Pretus, J.L.; Ducrot, D.; Bedos, A.; Deharveng, L. Response of soil fauna to landscape heterogeneity: Determining optimal scales for biodiversity modeling. Conserv. Biol. 2003, 17, 1712–1723. [Google Scholar] [CrossRef]
  58. Fleri, J.R.; Arcese, P. Predictive mapping to identify refuges for plant communities threatened by earthworm invasion. Ecol. Solut. Evid. 2021, 2, e12064. [Google Scholar] [CrossRef]
  59. Skidmore, A.K.; Siegenthaler, A.; Wang, T.; Darvishzadeh, R.; Zhu, X.; Chariton, A.; De Groot, G.A. Mapping the relative abundance of soil microbiome biodiversity from eDNA and remote sensing. Sci. Remote Sens. 2022, 6, 100065. [Google Scholar] [CrossRef]
  60. Bubier, J.L.; Rock, B.N.; Crill, P.M. Spectral reflectance measurements of boreal wetland and forest mosses. J. Geophys. Res. Atmos. 1997, 102, 29483–29494. [Google Scholar] [CrossRef]
  61. Whiting, G.J. CO2 exchange in the Hudson-Bay lowlands—Community characteristics and multispectral reflectance properties. J. Geophys. Res. Atmos. 1994, 99, 1519–1528. [Google Scholar] [CrossRef]
  62. Zhang, W.; Lu, Q.; Song, K.; Qin, G.; Wang, Y.; Wang, X.; Li, H.; Li, J.; Liu, G.; Li, H. Remotely Sensing the Ecological Influences of Ditches in Zoige Peatland, Eastern Tibetan Plateau. Int. J. Remote Sens. 2014, 35, 5186–5197. [Google Scholar] [CrossRef]
  63. Hasan, A.; Pilesjö, P.; Persson, A. On generating digital elevation models from liDAR data–resolution versus accuracy and topographic wetness index indices in northern peatlands. Geod. Cartogr. 2012, 38, 57–69. [Google Scholar] [CrossRef] [Green Version]
  64. Carless, D.; Luscombe, D.J.; Gatis, N.; Anderson, K.; Brazier, R.E. Mapping landscape-scale peatland degradation using airborne lidar and multispectral data. Landsc. Ecol. 2019, 34, 1329–1345. [Google Scholar] [CrossRef] [Green Version]
  65. Lees, K.J.; Artz, R.R.E.; Chandler, D.; Aspinall, T.; Boulton, C.A.; Buxton, J.; Cowie, N.R.; Lenton, T.M. Using remote sensing to assess peatland resilience by estimating soil surface moisture and drought recovery. Sci. Total Environ. 2021, 761, 143312. [Google Scholar] [CrossRef]
  66. Weigmann, G. Hornmilben (Oribatida); Die Tierwelt Deutschlands Bd. 76, Goecke and Evers: Keltern, Germany, 2006; p. 520. [Google Scholar]
  67. Balogh, J.; Balogh, P. Identification Keys to the Oribatid Mites of the Extra-Holarctic Regions; Well-Press Publ Limited: Miskolc, Hungary, 2002; Volume 1, p. 453. [Google Scholar]
  68. Norton, R.A.; Behan-Pelletier, V.M. Chapter 15. Oribatida. In A Manual of Acarology; Krantz, G.W., Walter, D.E., Eds.; Texas Tech Univ Press: Lubbock, TX, USA, 2009; pp. 430–564. [Google Scholar]
  69. Karg, W. Acari (Acarina), Milben. Parasitiformes (Anactinochaeta). Cohors Gamasina Leach: Raubmilben, 2nd ed.; VEB Gustav Fischer Verlag: Jena, Germany, 1993; p. 523. [Google Scholar]
  70. Gilyarov, M.S.; Bregetova, N.G. Key to the Soil Inhabiting Mites, Mesostigmata; Nauka: Leningrad, Russia, 1977; p. 717. (In Russian) [Google Scholar]
  71. Evans, G.O.; Hyatt, K.H. A revision of the Platyseiinae (Mesostigmata: Aceosejidae) based on material in the collections of the British Museum (Natural History). Bull. Brit. Mus. Nat. Hist. Zool. 1960, 6, 27–101. [Google Scholar]
  72. Evans, G.O.; Till, W.M. Studies on the British Dermanyssidae (Acari: Mesostigmata). Part II. Classification. Bull. Brit. Mus. Nat. Hist. Zool. 1966, 14, 107–370. [Google Scholar]
  73. Mašán, P.; Fend’a, P. Zerconid mites of Slovakia (Acari, Mesostigmata, Zerconidae); Institute of Zoology, Slovak Academy of Sciences: Bratislava, Slovakia, 2004; p. 238. [Google Scholar]
Arthropoda 01 00006 g001 550
Figure 1. Study area: (a) Shichengskoe mire system with Shichengskoe lake (scale bar: 2 km); in lighter colour is the ombrotrophic Sphagnum peat bog. Image courtesy of the U.S. Geological Survey. (b) Sampling plots, 2016—purple, 2019—green (scale bar: 2 km).
Figure 1. Study area: (a) Shichengskoe mire system with Shichengskoe lake (scale bar: 2 km); in lighter colour is the ombrotrophic Sphagnum peat bog. Image courtesy of the U.S. Geological Survey. (b) Sampling plots, 2016—purple, 2019—green (scale bar: 2 km).
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Arthropoda 01 00006 g002 550
Figure 2. Landsat 8 OLI spectral index ExG vs. biodiversity of oribatid mites in Sphagnum bog plots (red—terrestrial Oribatida, blue—aquatic Oribatida), Shichengskoe mire, July 2016: (a) abundance; (b) species richness.
Figure 2. Landsat 8 OLI spectral index ExG vs. biodiversity of oribatid mites in Sphagnum bog plots (red—terrestrial Oribatida, blue—aquatic Oribatida), Shichengskoe mire, July 2016: (a) abundance; (b) species richness.
Arthropoda 01 00006 g002
Table
Table 1. Landsat 8 OLI (path 177, row 18, 30-May-2016) bands correlations with water pH, plot trophic class and water table depth in Shichengskoe mire (Pearson r, * p < 0.05, ** p < 0.01).
Table 1. Landsat 8 OLI (path 177, row 18, 30-May-2016) bands correlations with water pH, plot trophic class and water table depth in Shichengskoe mire (Pearson r, * p < 0.05, ** p < 0.01).
Band 2Band 3Band 4Band 5Band 6Band 7
pH−0.17−0.06−0.150.09−0.22−0.45 **
Trophic class−0.32 *−0.13−0.38 **−0.11−0.22−0.46 **
Water table depth0.30 *0.260.35 *0.38 **0.190.09
Table
Table 2. Pearson’s correlations (r, + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001) for local environmental data and mite diversity in Shichengskoe mire; na—data not collected in 2016.
Table 2. Pearson’s correlations (r, + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001) for local environmental data and mite diversity in Shichengskoe mire; na—data not collected in 2016.
Mesostigmata DiversityOribatida Diversity
AbundanceRichnessAbundance, Aquatic Abundance, TerrestrialRichness, Terrestrial
2019Trophic class−0.070.030.26 +−0.09−0.04
pH−0.10−0.040.17−0.08−0.08
Water table depth−0.54 ***−0.45 **0.28 +−0.21−0.33 *
2016Trophic classnana−0.240.46 ***0.63 ***
pHnana0.120.060.19
Water table depthnana0.66 ***−0.44 **−0.41 **
Table
Table 3. Pearson’s correlations (r, + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001, n = 48) for Landsat 8 OLI spectral indices, local environmental data and Oribatida biodiversity in Shichengskoe mire (July 2016). NDVI—Normalized Difference Vegetation Index; ExG—Excess Green index; ExG−ExR—Excess Green minus Excess Red index; NDMI—Normalized Difference Moisture Index; SWIR1/NIR—Moisture Stress Index.
Table 3. Pearson’s correlations (r, + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001, n = 48) for Landsat 8 OLI spectral indices, local environmental data and Oribatida biodiversity in Shichengskoe mire (July 2016). NDVI—Normalized Difference Vegetation Index; ExG—Excess Green index; ExG−ExR—Excess Green minus Excess Red index; NDMI—Normalized Difference Moisture Index; SWIR1/NIR—Moisture Stress Index.
IndexTrophic ClasspH Water Table Depth Nutrients in Sphagnum TissuesOribatida Diversity
C:NPKAbundance,
Aquatic		Abundance,
Terrestrial		Richness, Terrestrial
NDVI0.55 ***0.47 **0.13−0.19−0.31 *0.21−0.180.38 **0.45 ***
ExG0.63 ***0.25 +−0.19−0.24 +−0.28 +0.37 **−0.35 **0.52 ***0.63 ***
ExG−ExR−0.54 ***−0.210.39 **0.34 **−0.43 **−0.36 **0.26 +−0.41 **−0.50 ***
NDMI0.080.27 +0.35 **0.09−0.60 ***−0.050.060.000.02
SWIR1/NIR−0.11−0.27 +−0.33 **−0.080.61 ***0.03−0.04−0.03−0.06
Table
Table 4. Pearson’s correlations (r, + p < 0.1, * p < 0.05, ** p < 0.01, n = 42) for Landsat 8 OLI spectral indices, local environmental and productivity data, and mite biodiversity in Shichengskoe mire (August 2019).
Table 4. Pearson’s correlations (r, + p < 0.1, * p < 0.05, ** p < 0.01, n = 42) for Landsat 8 OLI spectral indices, local environmental and productivity data, and mite biodiversity in Shichengskoe mire (August 2019).
IndexTrophic ClasspHWater Table DepthMesostigmata DiversityOribatida Diversity
AbundanceRichnessAbundance, AquaticAbundance,
Terrestrial		Richness, Terrestrial
NDVI0.35 *0.21−0.150.26 +0.190.200.01−0.03
ExG0.140.230.16−0.090.11−0.11−0.02−0.05
ExG−ExR−0.44 **−0.220.13−0.25−0.20−0.20−0.010.01
NDMI−0.150.010.42 **−0.30 +−0.26 +−0.12−0.11−0.12
SWIR1/NIR0.150.00−0.42 **0.30 *0.250.130.100.11
Table
Table 5. Random forest model variable selection for Landsat 8 spectral indices best explaining plot trophic class (based on plant indicator species of the poor-rich (acidity–alkalinity) gradient) in Shichengskoe mire, July 2016. The higher % increase MSE, the more important a variable is in explaining observed patterns.
Table 5. Random forest model variable selection for Landsat 8 spectral indices best explaining plot trophic class (based on plant indicator species of the poor-rich (acidity–alkalinity) gradient) in Shichengskoe mire, July 2016. The higher % increase MSE, the more important a variable is in explaining observed patterns.
Model InformationVariable Selection%IncMSE
No. of trees: 300
No. of variables tried at each split: 3
Mean of squared residuals: 0.234
No. of permutations: 999
Model significant at p = 0.001
Model R-square: 0.528 		ExG0.375
ExG−ExR 0.124
NDVI0.047
SWIR1/NIR0.027
NDMI0.023
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Minor, M.A.; Ermilov, S.G.; Joharchi, O.; Philippov, D.A. Using Spectral Indices Derived from Remote Sensing Imagery to Represent Arthropod Biodiversity Gradients in a European Sphagnum Peat Bog. Arthropoda 2023, 1, 35-46. https://doi.org/10.3390/arthropoda1010006

AMA Style

Minor MA, Ermilov SG, Joharchi O, Philippov DA. Using Spectral Indices Derived from Remote Sensing Imagery to Represent Arthropod Biodiversity Gradients in a European Sphagnum Peat Bog. Arthropoda. 2023; 1(1):35-46. https://doi.org/10.3390/arthropoda1010006

Chicago/Turabian Style

Minor, Maria A., Sergey G. Ermilov, Omid Joharchi, and Dmitriy A. Philippov. 2023. "Using Spectral Indices Derived from Remote Sensing Imagery to Represent Arthropod Biodiversity Gradients in a European Sphagnum Peat Bog" Arthropoda 1, no. 1: 35-46. https://doi.org/10.3390/arthropoda1010006

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