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Muhammed, K. Define–Investigate–Estimate–Map (DIEM) Framework for Modeling Habitat Threats. Encyclopedia. Available online: https://encyclopedia.pub/entry/17281 (accessed on 20 June 2024).
Muhammed K. Define–Investigate–Estimate–Map (DIEM) Framework for Modeling Habitat Threats. Encyclopedia. Available at: https://encyclopedia.pub/entry/17281. Accessed June 20, 2024.
Muhammed, Khaleel. "Define–Investigate–Estimate–Map (DIEM) Framework for Modeling Habitat Threats" Encyclopedia, https://encyclopedia.pub/entry/17281 (accessed June 20, 2024).
Muhammed, K. (2021, December 18). Define–Investigate–Estimate–Map (DIEM) Framework for Modeling Habitat Threats. In Encyclopedia. https://encyclopedia.pub/entry/17281
Muhammed, Khaleel. "Define–Investigate–Estimate–Map (DIEM) Framework for Modeling Habitat Threats." Encyclopedia. Web. 18 December, 2021.
Define–Investigate–Estimate–Map (DIEM) Framework for Modeling Habitat Threats
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The DIEM framework illustrates a method of defining threats on the basis of the derived definition, investigating an area using available spatial data, estimating threat severity using the principles used in existing equations, and mapping threats using spatial analysis methods.

GIS habitat threat

1. Introduction

In general, various events and activities put habitats at risk of being degraded. These stressors are referred to as habitat threats. Habitats are at risk of degradation due to human and/or environmental pressure such as land use, climate change [1], increasing population, urbanization, agriculture, and running-water diversions [2]. Habitats are also threatened because of side effects of human development, which include habitat loss, land fragmentation, deforestation, conversion to intensive agriculture, pollution (by synthetic pesticides and fertilizers), biological factors (pathogens and introduced species) [3], and constructed dams for water consumption and energy production [4]. Globally, the terrestrial landscape has a 7.56 million km network of streams, and river channels with a surface area of approximately 773,000 km2 [5] are impacted. Increasing population demands for water and land resources and increased food production add to the stresses on these terrestrial resources. Of the world population, 50% lives within 3 km of freshwater; more than 50% of the historical expanse of floodplains is constricted, and more than 600,000 km of inland waterways globally are altered for navigation [2]. Paukert et al. [4] observed that this pressure endangers freshwater biodiversity more than it does terrestrial and marine systems. They stated and estimated that 68% of all mussel species, 51% of crayfish species, 40% of amphibian species, and 46% of fish species in United States freshwater are considered to be vulnerable or thought to be extinct. Globally, 10% to 50% of species are threatened with extinction; in the United States, at least one-third of native species are considered to be imperiled [6]. Of the world’s insect species, 40% may become extinct over the next few decades [3]. Therefore, it is necessary to understand threats that impact natural habitats and biodiversity [7].

The developed DIEM framework defines, investigates, estimates, and maps habitat threats.

Define: The users need to define habitat threat. A narrow definition will contain one or two habitat or threat components, but a broad definition contains multiple components. If your goal is to optimize a farmland habitat, a grassland is a threat as it can encroach onto the farm. The definition of habitat threat is a factor that impacts farmland in this case. “Factor” describes the threat and “farmland” describes the habitat. This definition is narrow. However, if your goal is to conserve the natural environment, the farmland is a threat to the grassland. The definition can be an activity or event that impacts the environment where “activity” and “event” describe the threat and “environment” describes multiple habitats making it a broad definition.

Investigate: The first step of the investigation is to decide if the goal is to analyze a specific habitat within a region or the entire region. If analyzing a specific habitat, the location of the habitat within the area of interest needs to be identified. The next step is to define habitats threats for the area and then obtain spatial datasets. Investigating involves using available data to determine what threats are present and where they are occurring within a target area. The user chooses data to investigate based on how they define habitat threats.

Estimate: After obtaining the data, each dataset is analyzed. If no threat is found in one dataset, the next dataset is analyzed until all are. When threats are found, the threat severity or weight of each threat is estimated. Severity can be estimated from values found in the literature. A table of threat weights is included in the main paper’s Supplementary Materials. Once all threats are identified, and their severity scores are estimated, then you estimate the severity of the threats to calculate a habitat threat index (HTI).

Map: The first step of the mapping phase involves determining which threat index method is to be used. Threat index methods can be chosen from Table 6. The next step is to choose a mapping method. The framework in Figure 11 divides mapping tools into Python libraries or GIS spatial analysis tools categories.

2. Applying the DIEM Framework to a Case Study

The DIEM framework was demonstrated through a case study.

(D) A broad definition of habitat threat was used. The focus of this study was on the natural environment so, habitat threats were defined as being anthropogenic activities or natural events that put natural habitats at risk.

(I) When investigating, spatial data with the finest resolution and the most recent data were used. Identifying the types of threats in an area requires spatial data for each potential threat. Available data sets include land use/cover maps, wildfire frequency maps, invasive species sighting maps, etc. These data are difficult to find at times. There are also threats that are not straightforward, such as climate change, war, and population pressure. Threats such as these require extensive research. The characteristics of these threats can vary based on region.

(E) Threat characteristics such as the maximum distance a threat can impact (Dmax) and threat weight were estimated. The Dmax value can be, for instance, as low as 0.5 km in Portugal [8] and as high as 10 km in China [9]. Using an average of every value may not be accurate. Using values from regions that are most like the area of interest is advised. The ideal approach is to thoroughly study the chosen region if resources are available.

(M) Weights were used to create overall threat level maps. The method for creating the maps was similar to the equation derived from the literature [4][10], which is as follows:

where fi is the threat frequency of each threat, and αi is the threat severity or weight of each threat. The equation has fi as the threat index value of individual threats. This does not change the results for the four threats where the index was modeled on the basis of presence or absence. Frequency is 1 for each grid cell where the threat occurs, and 0 otherwise. The population-pressure index map had values in the range between 0 and 1, so frequency in this map was a fraction in some cases and did not represent the actual population, but rather a relative value. An alternative to this method is to use the equation derived from Deffense [11], which is as follows:

where Fi is the threat factor score, which is understood as the threat index of individual threats. The rationale for not using this method was that there were grid cells in each threat map that had a value of 0. When maps are multiplied together, the resulting map would be 0. The habitat threat index equation derived from Nie, Yang, and Huang [12] could also be used. It is as follows:

where Nc is the number of grid cells in a land use type; Nl is the number of land uses in the area, and αij is the threat severity of each threat for each land-use type. This equation is used to calculate the HTI for each land use. The threat severity in this equation also needs to account for the sensitivity of land uses to each threat. The advantage of using this equation is that it is not affected by differences in spatial data resolution. The equation can be used with tabular results, and the only spatial data needed are the land use/cover dataset. However, this method requires many more steps than the previous two equations when it comes to general watershed analysis, so it was not used here.

Two overall threat maps were created due to the map resolution being changed when using population data that were coarser than those of other datasets. This caused some data to be lost after resampling. Therefore, a threat map was created where population pressure was not used. The two maps were similar when it came to modeling the distribution of threats.

3. Results of Applying the DIEM Framework

A case study was done on the Choctawhatchee River Watershed. Within the watershed, the natural habitats are open water, barren land, deciduous, coniferous, and mixed forests, shrub lands, grasslands, and both wooded and herbaceous wetlands. Agriculture was the threat that occurs most often, but it had a relatively low average threat weight. Urbanization was the second-most frequent threat, and it had the highest average weight. Mining was the next most frequent, followed by power plants, and both had the same average weight. Population pressure had the lowest average weight and generally occurred where there was development. The greatest threats occurred in the same general areas between maps. Areas with the highest threat value were developed, with the highest values being where urbanization and mining or power plants overlapped. There were a few grid cells on the map that included population pressure where agriculture, population, and mining or power plants overlapped, which yielded a high threat value, but these grid cells were not numerous enough to be recognized by the eye.

Since agriculture and urbanization contributed to the overall threat level the most, management strategies to mitigate the impact of these practices should be considered. These management strategies include agricultural best management practices such as reducing water use and diversion, environmental functional zoning to protect vulnerable habitats from urban growth, and formal mechanisms such as policies created in response to landscape threat assessment models [13][14].

4. Potential Uses of Threat Maps

Spatial imaging is effective in understanding patterns in biodiversity. Mapping processes that threaten biodiversity helps in identifying locations that are at risk of degradation. Decision-makers use threat maps to decide what locations should be prioritized in conservation efforts. The goal of conservation is also to protect species, habitats, and ecosystems within a region. Mapping the occurrence of species, habitats, and ecosystems, and the activities that threaten them is required to design an appropriate plan for conservation. There are various approaches to how maps can be used to identify priority areas for conservation. Threat maps can be used for understanding how threats affect land cover, reduce the number of species in an area, disrupt patterns in endemism, or impact ecological processes [1].
In addition to conservation planning, threat maps can be used to study the effects that those threats have on the environment. Historic LULC and threat maps allow researchers to see how the existence of threats affects habitats over time. It also gives insight into the growing number of threats in an area. Urbanization may have not been a threat to a region historically but may be now. This is often the case in developing countries. Other threats, such as droughts or floods, may become more frequent due to the changing climate. Understanding how threats affect the environment over time is important when attempting to predict how an environment may change due to threats. It can also be used to determine how similar environments would be affected if certain threats ever occur. If it is known how a hydroelectric dam impacts a river and the surrounding habitats, for example, decision-makers can plan mitigation strategies if a hydroelectric dam is planned to be built on another river. Decision-makers could even decide to use an alternative energy-production method if a dam proves to be too destructive to proximal habitats.

5. Advantages of the Framework

The DIEM framework is flexible and can be used in a variety of situations. It is general enough to be used in any region whether the focus is on habitats or threats. It is also a method that can be used with any regional analysis model. The flexible nature of the framework makes it easy to follow. It is also possible to modify the framework for a specific situation while maintaining the idea behind it.
Being able to produce threat maps is advantageous as well. Available threat maps may not be useful in every case. They could be produced using a method that is too broad. They could also have too coarse a resolution to use on small-scale projects. It is also likely that not every threat in a given area is mapped. In cases such as these, it is important to be able to produce maps wherein index calculation and resolution can be controlled.

6. Potential Limitations of the Framework and Future Work

Due to the framework requiring spatial data to work, it is limited by the quality and availability of data. Finding national LULC data at a 30 m resolution for the United States is not difficult. Other regions may only have global LULC data available, which are coarser than NLCD. Data other than LULC are not always available, especially when searching for very specific threats such as population pressure. Natural-threat maps such as wind and flood maps are hard to come by. In addition, threat characteristics are not easily estimated. The average threat characteristic values from the meta-analysis were used in the case study. These values may not be as accurate, as data could be limited. Accurately estimating the characteristics may require additional steps that are not part of the framework. Some of these steps may include different statistical methods or specialized research to accurately assess the severity of threats in an area.

References

  1. Kuemmerlen, M.; Schmalz, B.; Cai, Q.; Haase, P.; Fohrer, N.; Jähnig, S.C. An attack on two fronts: Predicting how changes in land use and climate affect the distribution of stream macroinvertebrates. Freshw. Biol. 2015, 60, 1443–1458.
  2. Eros, T.; Kuehne, L.; Dolezsai, A.; Sommerwerk, N.; Wolter, C. A systematic review of assessment and conservation management in large floodplain rivers—Actions postponed. Ecol. Indic. 2019, 98, 453–461.
  3. Sánchez-Bayo, F.; Wyckhuys, K.A. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 2019, 232, 8–27.
  4. Paukert, C.P.; Pitts, K.L.; Whittier, J.B.; Olden, J.D. Development and assessment of a landscape-scale ecological threat index for the Lower Colorado River Basin. Ecol. Indic. 2011, 11, 304–310.
  5. Thoms, M.; Sheldon, F. Large rivers as complex adaptive ecosystems. River Res. Appl. 2019, 35, 451–458.
  6. Strauss, A.; Hurlbutt, B.; O’Brady, C. Preserving Biodiversity. In Colorado College State of the Rockies Report Card; Colorado College: Colorado Springs, CO, USA, 2006; p. 61.
  7. Mehri, A.; Salmanmahiny, A.; Mikaeili Tabrizi, A.R.; Mirkarimi, S.H.; Sadoddin, A. Integration of anthropogenic threats and biodiversity value to identify critical sites for biodiversity conservation. Geocarto Int. 2019, 34, 1202–1217.
  8. Lemos, A.B. Evaluating Ecosystem Services Trade-Offs Due to Land Use Changes: Transition to an Irrigated Agriculture Landscape in Departamento de Biologia Animal. 2017. Available online: https://repositorio.ul.pt/bitstream/10451/27672/1/ulfc120781_tm_Ana_Lemos.pdf (accessed on 5 October 2021).
  9. Xu, L.T.; Chen, S.S.; Xu, Y.; Li, G.Y.; Su, W.Z. Impacts of Land-Use Change on Habitat Quality during 1985–2015 in the Taihu Lake Basin. Sustainability 2019, 11, 3513.
  10. Shen, Y.; Cao, H.; Tang, M.; Deng, H. The Human Threat to River Ecosystems at the Watershed Scale: An Ecological Security Assessment of the Songhua River Basin, Northeast China. Water 2017, 9, 219.
  11. Deffense, N. Deriving a habitat quality index to inform reef conservation in the Great Barrier Reef. In Faculté des Bioingénieurs; Université Catholique de Louvain: Louvain-la-Neuve, Belgium, 2019; p. 78.
  12. Nie, C.; Yang, J.; Huang, C.H. Assessing the Habitat Quality of Aquatic Environments in Urban Beijing. Procedia Environ. Sci. 2016, 36, 162–168.
  13. Wilcove, D.S.; Rothstein, D.; Dubow, J.; Phillips, A.; Losos, E. Quantifying threats to imperiled species in the United States. Bioscience 1998, 48, 607–615.
  14. Li, F.X.; Wang, L.Y.; Chen, Z.J.; Clarke, K.C.; Li, M.C.; Jiang, P.H. Extending the SLEUTH model to integrate habitat quality into urban growth simulation. J. Environ. Manag. 2018, 217, 486–498.
  15. Nelson, E.; Ennaanay, D.; Wolny, S.; Olwero, N.; Vigerstol, K.; Pennington, D.; Mendoza, G.; Aukema, J.; Foster, J.; Forrest, J.; et al. InVEST 3.8.0. User’s Guide. Sharp, R., Chaplin-Kramer, R., Wood, S., Guerry, A., Tallis, H., Ricketts, T., Eds.; 2020. Available online: https://invest-userguide.readthedocs.io/_/downloads/en/3.8.3/pdf/ (accessed on 7 October 2021).
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