INTRODUCTION:

Terrestrial realms, also known as biogeographic realms, are areas where animal or plant distribution has similar or shared characteristics. There are many interpretations of the number of realms, but for this case, we used seven realms: the Palearctic, Oceania, Afrotropics, Neotropics, and Nearctic. Several researchers also claim that there is an additional realm, the Antarctic, which can be found in Antarctica (Cox, 2001).

Looking at the map of the eight realms in the world using Vardy's (1975) system, we can see that lines separate significant regions. These lines in fact, are not visible, nor are they not separated by borders like countries. This is why there are different understandings and agreements on the boundaries of each realm. However, Vardy explains that biogeographic realms are set on various variables such as geoelements, historical elements, and also published literature.

One such boundary of these realms is the 'Wallacea's Line'. The name comes from Alfred Russel Wallacea, who embarked on an epic journey to Southeast Asia between 1854 and 1862 for several reasons. The main goal of his journey was to collect as many specimens of living organisms as possible (Wyhe, 2018). In his eight-year journey while collecting his samples, he also discovered between the two major sides of the Malay Archipelago (now known as the Indo-Australia Archipelago (Insulida)) with vastly different characteristics between fauna and flora (Ali & Heaney, 2021).

This imaginary line separates Indonesia into two parts. Sulawesi, Maluku, Timor Leste, Lombok, Lesser Sundas, and parts of the Philippines are considered the East Side of the Wallacea Line. On the Western side, Kalimantan (or the Borneo Island), Java, Bali, and Sumatra are categorized as the Western Side of the Wallacea Line (Camerini, 1993).

The two different biogeographic realms have different numbers of species and differing environments. Conservationists, government officials, and other relevant institutions/personnel use two datasets to map biodiversity and its importance for conservation. These two datasets are Species Richness (SR) and Rarity-Weight Richness (RWR) (Albuquerque & Beier, 2015).

METHODS:

First, we did literature research about Wallacea’s Line. Due to the various variations of Wallacea’s Line since it was published in 1863, we chose a map of Wallacea’s Line that is updated in terms of land boundaries and also has a clear grid system. The map we used was gathered from Encyclopedia Britannia, Inc. (1997) in NOAA (2010). The following map can be seen in Figure 1.

Figure 1. Representation of Wallacea’s Line in current Indonesia-Australia (Insulida) Archipelago, which separates major Islands such as Borneo between Sulawesi, Bali, and Lombok (NOAA, 2010).

The following map was georeferenced into a coordinate reference system (CRS) of EPSG 4326 in QGIS. Points that were used as Ground Control Points (GCP) were picked randomly from a vector dataset from World Administrative Boundaries. Then, using the georeferenced map of NOAA as an outline, we created a Polyline of Wallacea’s Line. After that, we exported Wallacea’s Line as a zip file and uploaded it to GEO-MAPID for visualization.

Figure 2. Flowchart of acquiring the data and the processing that was done.

The next step was acquiring the required datasets. All of our datasets were open-sourced, meaning everyone could acquire and use the data according to their needs. The datasets that were used are divided into raster and vector datasets. Raster datasets were gathered from the International Union for Conservation of Nature (IUCN) of SR, RWR, and Threatened SR & RWR.

For vector datasets, there were two types of data used which are the following: Biogeographic Regions which were created by Dinerstein et al. (2017), and World Administrative Boundaries (Countries), which were provided by Opendatasoft (2019). These World Administrative Boundaries were clipped so that only Indonesia, Singapore, Malaysia, Timor Leste, and Papua New Guinea were visualized. This was done to visualize the Malay Archipelago when Alfred Russel Wallace embarked on his journey. The Philippines was not included due to the limited file size of the upload in GEO-MAPID.

Ecoregions, in simple terms, are regions with homogeneity of different ecological systems involving interactions between organisms and their environment (Omernik, 1995). Various versions of Ecoregions have been published online, but the most popular and freely accessible was published by Dinerstein et al. (2017). We used ecoregions to visualize the types of ecosystems in Indonesia.

Our aim here is to visualize the differences between SR and RWR in the Malay Archipelago and compare them to the two sides of Wallacea’s Line (eastern and western side). We will also visualize the different ecoregions between the two sides of Wallacea’s Line. Besides that, we will try to visualize which major islands in the Insulida Archipelago have the biggest biodiversity and which need more attention for conservation purposes.

When first opening up the project data, you will be met with various layers. Ten shapefiles, including four Species Richness layers, four Rarity-Weight Richness, one Wallacea Line, and one Biogeographic Region (Ecoregions), can be found. First, we must change the base map from Street 3D Buildings to Satellite. The satellite basemap is used to display the actual terrain. This will help the readers visually understand the actual terrains and the similarities between ecoregions.

Since MAPID cannot display raster data, we used the datasets that we have transformed into vector data. In addition, due to the limited ways we can visualize in MAPID, we also had to create specific maps using QGIS.

RWR Datasets

Due to the high number of pixels in RWR, it was challenging to visualize each pixel as a different value. Also, when converting the RWR from a raster to a vector, the values of each pixel are converted to its row. We categorized the rarity value using Natural Break (Jenks) to overcome this challenge. We used seven classes to visualize the different RWR classes.

This classification was perfect because of RWR values due to the extreme distribution. This method looks at the data as a whole and tries to identify the natural patterns in the data. With seven classes using Natural Breaks (Jenks), reading the map is more accessible and more understandable.

In simple terms, The higher the RWR class in an area, the higher the chance that a species plays a more critical role (IUCN, 2023).

Visualizing the Different Datasets

After the basemap changes, we can visualize the different layers and overlay between the other datasets. We created three folders so that the layers are not mixed up. The layers in the folders can be visualized with each other including: SR, RWR and Biogeographic Regions & Ecoregions. In SR and RWR folders, each file has two types of polygons. Files ending with “pixel” represented the actual file tile from the original dataset. Compared to files ending with “point” were also provided for easier visualisation when compared to ecoregions. The following is a combination of layers that can be displayed according to the needs of the reader:

1. 1.
   THR SR 2023 + Ecoregions + Wallacea’s Line: used to visualize the total number of threatened species found in a single area.

1. 2.
   SR 2023 + Ecoregions + Wallacea’s Line is used to visualize the total number of species found in a single area.

1. 3.
   THR RWR 2023 + Ecoregions + Wallacea’s Line: These are used to visualize the areas where there is a higher chance of a rarer threatened species of animal being found.

1. 4.
   RWR 2023 + Ecoregions + Wallacea’s Line: used to visualize the areas where there is a higher chance that a rarer species of animal is found.

1. 5.
   Ecoregions + Wallacea’s Line: used to visualize the various ecoregions in Insulinda Archipelago.

The SR and RWR only accounted for Amphibians, Birds, Mammals and Reptiles, it did not account for marine animals and other such animals (IUCN, 2017)/

RESULTS AND DISCUSSION:

Each of the layers of SR, RWR, and the threatened species of SR and RWR are shown and can be displayed in GEO-MAPID ID. For a more comprehensive visualization, by choosing the named SR/RWR ending with “pixel,” we can zoom in on different areas of the Indonesia-Australia Archipelago (Insulinda). We also created maps, which can be seen in Figures 3 to 5, using QGIS to visualize the SR/RWR over the different ecoregions.

Figure 3. The following ecoregions with their names of major Islands in the Insulinda Archipelago

This map basically represented the ecoregions that can be found in the Insulinda Archipelago. The layer can also be found in the MAPID website but with a different colour visualization

Figure 4. Distribution of the RWR classes with over the Insulinda Archipelago.

This map representes the distribution of RWR classes within the Insulinda Archipelago. We can see that over the eight major islands, Papua and Kalimantan had Class 7 of RWR. This indicated that a species of animal found there must be important to its ecosystem. It also shows that these two islands have more significant endemism than other islands.

Visually, we can also see that the RWR is not as distributed between the western side of the Wallacea Line (Peninsular Malaysia, Sumatera, Java, and Kalimantan (Borneo)) and the eastern side (Papua, Sulawesi, Nusa Tenggara, Maluku). On the eastern side, for example, in Papua and Sulawesi, the endemism is quite high, meaning that species can only be found in that specific area (pixel).

Diagram 1. The total amount of each RWR Classes per major Islands in Insulinda Archipelago.

The original plan was that the diagrams can be shown in MAPID project file but currently the feature is not available. So here we analysed it in Microsoft Excel. Diagram 1 shows the number of classes of RWR in each of the major Islands. We can see that Class 7 is only found in Kalimantan and Papua. Considering the size of the islands, Kalimantan has the most Class 1. But if we look at the distribution visually, we can see that Sulawesi has the highest distribution, which shows that Sulawesi has a high amount of endemism.

Looking at the Threatened RWR map (Figure 5), we see that the values and distribution are similar. Conservationists, biologists, government officials, and other stakeholders can use the RWR maps to review where more effort and focus needs to be made on conservation areas.

IUCN (2023) stated that their RWR calculations are done by finding the total number of species that can be found in that area. The area of a pixel is divided by the species distribution. Then, the values are summed across all the species.

Besides that, each SR and RWR means that all animals in all the Red List Categories (Not Evaluated, Data Deficient, Least Concern, Near Threatened, Vulnerable, Endangered, Critically Endangered, Extinct in the Wild, and Extinct) are calculated. Threatened species are considered Critically Endangered, Endangered, and Vulnerable (IUCN, 2023).

For SR, it can be seen through the project dataset. Besides that, the distribution of SR can be seen in diagram 2 and 3.

Diagram 2. The total amount of threatened species grouped into the major Islands of the Insulinda Archipelago

Diagram 3. The total amount of species grouped into the major Islands of the Insulinda Archipelago

We can clearly see that the highest SR is found in Kalimantan, followed by Papua, Sumatera, Peninsular Malaysia, Jawa, Sulawesi, Nusa Tenggara, and Maluku. Kalimantan, or Borneo, has the highest SR due to its lush and still well-protected forests.

REFERENCES:

Albuquerque, F., & Beier, P. (2015). Rarity-weighted richness: a simple and reliable alternative to integer programming and heuristic algorithms for minimum set and maximum coverage problems in conservation planning. PloS one, 10(3), e0119905.

Ali, J.R. Heaney, L.R. (2021). Wallace’s line, Wallacea, and associated divides and areas: history of a tortuous tangle of ideas and labels. Biological Reviews, 96. 922-942.

Camerini, J.R. 1993. Evolution, Biogeography, and Maps. An Early History of Wallacea’s Line. Isis, (84), 700-727.

Cox, B. (2001). The biogeographic regions reconsidered. Journal of biogeography, 28(4), 511-523.

Dinerstein, E., Olson, D., Joshi, A., Vynne, C., Burgess, N. D., Wikramanayake, E., … & Saleem, M. (2017). An ecoregion-based approach to protecting half the terrestrial realm. BioScience, 67(6), 534-545. https://doi.org/10.1093/biosci/bix014

McPherson, S. Irving, R. https://www.redfernnaturalhistory.com/product/geographers-and-explorers/.

National Oceanographic and Atmospheric Administration. (2010). Biogeography of the INDEX-SATAL Region

Omernik, J.M. (1995). Ecoregions: A Framework for Managing Ecosystems. The George Wright Forum. Vol: 2, (1).

Opendatasoft. (2019). World Administrative Boundaries. https://public.opendatasoft.com/explore/dataset/world-administrative-boundaries/information/

Udvardy, M. D. F. A Classification of the Biogeographical Provinces of the World. https://iucn.org/sites/default/files/import/downloads/udvardy.pdf

Wyhe, J.V. (2018). Wallace’s Help: the Many People Who Aided A.R. Wallace in the Malay Archipelago. JMBRAS, 91 (1), 41-68

Datasets used:

The SR and RWR only accounted for Amphibians, Birds, Mammals and Reptiles, it did not account for marine animals and other such animals (IUCN, 2017)

• IUCN Species Richness 2023

• IUCN Rarity Weight Richness 2023

• IUCN Threatened Species Richness 2023 IUCN Rarity Weight Richness 2023

• Biogeographic Regions (https://www.geographyrealm.com/terrestrial-ecoregions-gis-data/)

• World Administrative Boundaries (https://public.opendatasoft.com/explore/dataset/world-administrative-boundaries/export/)