Corresponding author: John Odindi ( email@example.com )
Academic editor: Michael Kleyer
© 2016 M.M. Ojoyi, John Odindi, O. Mutanga, E.M. Abdel-Rahman.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation: Ojoyi MM, Odindi J, Mutanga O, Abdel-Rahman EM (2016) Analysing fragmentation in vulnerable biodiversity hotspots in Tanzania from 1975 to 2012 using remote sensing and fragstats. Nature Conservation 16: 19-37. https://doi.org/10.3897/natureconservation.16.9312
Habitat fragmentation is a threat to conservation of biodiversity hotspots in the Morogoro region, Tanzania. However, on-going research on fragmentation has not kept pace with temporal lapses and how individual species respond to habitat transformation and heterogeneity. This study sought to model spatial and temporal fragmentation patterns. Cloud free multi-temporal Landsat imagery with similar spectral resolution were acquired in the same season in 1975, 1995 and 2012. The images were used to characterize the biophysical landscape characteristics and a range of metrics used to quantify the magnitude of fragmentation. Patches and classes in the landscape were assessed using Fragstats, a spatial statistics program useful in computing landscape metrics. Results show that patch number was higher in dense forest and woodland than in less dense forest and grassland in 1975, 1995 and 2012 while the interspersion Juxtaposition Index (IJI) ranged between 0 (for clumped patches) and 100 (for grassland). In 1975 and 1995, the grassland habitat had the highest IJI while in 2012 less dense forest had the highest IJI. The Games-Howell test showed a significant fragmentation trend in less dense forests class (p≤0.05). Generally, the study indicates a high fragmentation pattern in the vulnerable tropical eastern arc mountain region of East Africa. This finding demonstrates the value of remotely sensed data in understanding the impact of anthropogenic processes on natural landscape transformation. Furthermore, the study provides a basis for informed conservation policy design and implementation in the region.
habitat, fragmentation, fragstats, remote sensing, Tanzania
Habitat fragmentation, an indication of habitat transformation, degradation and loss is a great concern globally (
Habitat fragmentation is an explicit challenge to conservation in the tropics (
Ecosystems in Morogoro region, Tanzania contribute to the world’s climate regulation through large carbon stores (
Similar to this case study, most rich biodiversity hotspots in Tanzania are geographically located in the Eastern Arc Mountains (
Landsat MSS (20/08/1975), Landsat TM (30/09/1995) and Landsat ETM+ (20/07/2012) imagery with better visualization (less than 15% cloud cover) from the Global Landcover Facility (http://www.landcover.org) were selected for the study. Datum was set to WGS 84 and referenced to Universal Transverse Mercator (UTM) zone 37 South. All images were orthorectified using ground validation points, Digital Elevation Model (DEM) and aerial photos as a reference. Landsat images were resampled to a common resolution pixel (30 × 30 m) using bilinear resampling to ensure consistency in all image scenes. First order polynomial transformation was applied at image registration to correct for any shifts. It was deemed necessary to simulate atmospheric interactions between the sun and sensor pathways for the imagery used. Therefore, a radiative transfer model in Atmospheric and Topographic Correction (ATCOR) module in Erdas Imagine 2013 was used for atmospheric correction. ATCOR masks haze, cloud, water and enhances pixel visibility. In this study, we used the MODerate resolution atmospheric TRANsmission (MODTRAN) code to retrieve the atmospheric parameters for ATCOR from the look-up table as ground-based reflectance and atmospheric data were unavailable. Digital number values were then converted to reflectance based on metadata provided with the Landsat images (
A supervised maximum likelihood classifier was adopted for classification (
Fragstats metrics were extracted from all processed Landsat images. Fragstat metrics offer a distinct capacity to determine a landscape’s spatial configuration, hence valuable in understanding landscape change arising from fragmentation (
Fragmentation Indices used in the current study.
|Patch Density (PD)||Number of patches of the corresponding patch type.|
|Largest Patch Index (LPI)||An index used to quantify the percentage of total landscape area characterized by the largest patch.|
|Edge density (ED)||Used to assess edge length per unit area.|
|Patch Number (NP)||A measure of the magnitude of fragmentation of patches|
|Interspersion Juxtaposition Index (IJI)||A measure of adjacency of patches determined by dividing the length between patch edge by the number of patches within a landscape. Values approaching 0% indicate that a patch is adjacent to only one other patch and 100% indicate that a patch is in similar proximity to multiple patches within a landscape.|
|Patch Area (MN)||The sum across all patches in the landscape of the corresponding patch metric values, divided by the total number of patches. Expressed in hectares.|
|Perimeter Area Ratio (PARA)||Refers to the ratio of the patch perimeter (m) to area (m2).|
|Total Area (CA)||Refers to the sum of areas (m2) of all patches for the patch type.|
|Percentage of Landscape (PLAND)||Useful in computing the proportional abundance for each of the patch type across the landscape.|
The overall accuracy for 1975, 1995 and 2012 image scenes was 78.26%, 84% and 76.54% respectively (Table
Accuracy assessment tests (Producer’s Accuracy - PA, User’s Accuracy - UA).
|PA (%)||UA (%)||PA (%)||UA (%)||PA (%)||UA (%)|
|Less Dense Forest||66.67||100||100||100||100.00%||60.00%|
The study findings showed substantial land modification in most of the cover types during the study period i.e. decline in dense forest (31, 675.70 hectares) and less dense forest (by 11, 267.38 hectares) and increase in grassland (21, 230.01 hectares). However, changes in areas covered by woodland were inconsistent, i.e. increase by 15,884.46 hectares between 1975 and 1985 and decline by 8, 182.03 between 1985 and 2012) – Figure
Dynamic fragmentation trends were observed (Table
Temporal patterns of total area coverage (A), percentage of landscape (B) and edge density (C).
Patch area compared by Mann-Whitney Tests.
|Prob > |z|||
|Prob > |z||
|Less dense forest||1975||16.728***||0||-8.268***||0|
Study findings indicated a higher probability of dispersion linked to woodland and less dense forest. Interspersion Juxtaposition Index (IJI) ranged between 0 (for clumped patches) and 100 (for grassland). In 1975 and 1995, the grassland habitat had the highest IJI while in 2012, less dense forest had the highest IJI. The interspersion juxtaposition index (IJI), was useful in characterizing the degree of adjacency for each patch type e.g.
Mann-Whitney tests were applied to the data. Mann-Whitney test results showed distinct differences in patch area (p<0.01) as summarized in (Table
Game-Howell test is ideal for unequal sample sizes charaterised by heterogeneity and has been widely used in vegetation mapping that include taxonomic profiles in the Atlantic and Caatinga biomes of northestern Brazil (
Games-Howell tests for the mean parameter area ratio (PARA) in 1975, 1995, 2012.
|1975||1995||2012||1975 vs 1995||1975 vs 2012||1995 vs 2012|
|Less dense forest||496.29||563.06||529.5||0.0001||0.0001||0.0001|
This study showed a progressive fragmentation at both spatial and temporal domains. Variability in responses to fragmentation was also noted for different habitats. Fragmentation in the area is not only dependent on topography but also adjacency to land for agriculture, urbanization/settlement and infrastructure development, which are considered key drivers of landscape transformation in the region. All these anthropogenic activities contribute to habitat losses and species decline. Implications on the landscape are presented with a reflection on policy and future management.
There was a transformation in habitat extents within the study area. Significant losses were recorded for dense forest (31, 675.70 hectares) and less dense forest (by 11, 267.38 hectares), however, there was a steady increase in areas covered by grassland. Based on field study observations, these changes can be attributed to expanding agricultural fields and increased exploitation of timber and non-timber products to meet the increasing urbanization demand in Morogoro district. This finding is in agreement with
As aforementioned, there was a general decrease in area covered by dense and less dense forest habitat. A decreasing trend in the extent of total habitat coverage relates to deleterious fragmentation as effects of habitat fragmentation are dependent on habitat size (
Similarly, a distinct variation in patch number was observed. Woodland and less dense forest had the highest patch number across the years. This can be attributed to the great extent of fragmentation resulting from natural resource exploitation. Furthermore, their vicinity to Morogoro town and management by local authorities may be possible drivers increasing their susceptibility to fragmentation (
Dense forest and woodland had the greater edge density. This could be attributed to increased exposure to farmlands and settlements prevalent in the area. Edge effects characterize the biophysical state of ecosystems at the periphery or in the neighborhood. This is because increased habitat fragmentation exposes habitat to edge effects, compromising the ability of an ecosystem to provide relevant goods and services (
Games-Howell test results showed a significant level in the perimeter area relationship (p≤0.05). This could be explained by the fact that less dense forest adjoins dense forest, taking up regions dominated by woodland. It is also possible that the on-going fragmentation is a major driver of conversion of dense forest and woodland to less dense forest. Potential socio-economic drivers could be a result of the expanding Morogoro town and increasing agricultural fields in the adjacent local regions. Similarly, other studies showed how adjoining activities influence intact habitat ecosystems as a result of their structural configuration (
Anthropogenic activities significantly influence habitat fragmentation in the region. For instance, extensive farming and urban growth are possible drivers to habitat modification and fragmentation. The area has a conducive montane climate that supports subsistence farming, a prevalent socio-economic activity in the region (
Drivers to fragmentation, note the settlements in the valley and cleared forest in the background and fore ground for crop farming and grazing, respectively (A) and small scale maize and banana fields within the forest in (B).
To forestall some of the problems earlier highlighted, the study area, identified as biodiversity hotspots with important ecological functions such as groundwater recharge, surface flow and animal habitat need to be protected from the impacts of land modification and fragmentation. Implications of habitat modification and fragmentation in Morogoro region can be better deciphered through the impact on habitat structure and species losses. The increased habitat losses, mainly attributed to anthropogenic factors may negatively influence genetic diversity and lead to losses of potentially useful genes originally accommodated in intact areas (
Distinct differences in magnitude of fragmentation were evident across the four habitat categories. The study findings show that fragmentation was highest in less dense forest. Subsistence farming, increasing human population and urban growth are thought to be key drivers to habitat modification and fragmentation, hence it is concluded that anthropogenic processes are the major drivers to habitat fragmentation in the area. The fragmenting landscape is expected to significantly influence floral and faunal vulnerability, likely to compromise the area’s ability to among others assimilate organic carbon and to supply socio-economic and environmental goods and services. It is therefore necessary that the study area, and indeed the entire eastern arc mountains region be protected from the impacts of land modification and fragmentation. The study further underscores the value of satellite imagery in concert with relevant reference data in understanding spatio-temporal transformation of vulnerable landscapes arising from anthropogenic processes.
The current study was financed by UNESCO L’Oreal Foundation for Women in Science, The International Development Research Centre (IDRC), Ontario Canada and University of KwaZulu-Natal Post-Graduate sponsorship. Training by the University of KwaZulu-Natal, South Africa and Faculty of Geo-Information Science and Earth Observation, University of Twente, Netherlands is greatly appreciated. Field support provided by Sokoine University of Agriculture, forestry department, Tanzania Government Forestry departments in Morogoro region and the Wami/Ruvu Basin Office is highly acknowledged.