Integrating habitat risk assessment and connectivity analysis in ranking habitat patches for conservation in protected areas

Ghehi, Nasim Kheirkhah; Malekmohammadi, Bahram; Jafari, Hasan

doi:10.1016/j.jnc.2020.125867

Cited by 18 publications

(9 citation statements)

References 59 publications

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“…These small outcrops are essential, both because they represent exclusive habitats for endemic species (Hopper et al 2021) and because they contribute to connectivity, serving as connection points (i.e., stepping stones) for the ecological flow between areas (Chetkiewicz et al 2006). Thus, our results may also support conservation strategies based on studies involving spatial dynamics in plant populations and landscape ecology, including connectivity analysis (Salles et al 2019, Ghehi et al 2020.…”

Section: Discussionmentioning

confidence: 60%

Mapping threatened canga ecosystems in the Brazilian savanna using U-Net deep learning segmentation and Sentinel-2 images: a first step toward conservation planning

Pereira¹,

Wagner²,

Kamino³

et al. 2023

Biota Neotrop.

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Canga ecosystems are iron-rich habitats and pose a challenge for conservation and environmental governance in Brazil. They support high levels of biodiversity and endemism and, at the same time, have suffered intense losses and degradation due to large-scale iron ore mining. The Peixe Bravo River Valley in the Brazilian savanna is one of the last natural canga areas that has yet to face the irreversible impacts of mining. However, there are vast gaps in data on the vegetation cover, location, spatial distribution, and area of occurrence of this ecosystem. Therefore, more information is needed on the appropriate scale, without which it is difficult to establish conservation planning and strategies to prevent, mitigate or compensate for impacts on canga ecosystems. In this study, we provide the first map of canga ecosystems in Brazil using the U-Net deep learning model and Sentinel-2 images. In addition, we estimate the degree of direct threat faced by ecosystems due to the spatial overlap of the mapped cangas and the location of mining concession areas for iron ore exploitation. The deep learning algorithm identified and segmented 762 canga patches (overall accuracy of 98.5%) in an area of 30,000 ha in the Peixe Bravo River Valley, demonstrating the high predictive power of the mapping approach. We conclude that the direct threat to canga ecosystems is high since 99.6% of the observed canga patches are included in mining concession areas. We also highlight that the knowledge acquired about the distribution of cangas through the application of an effective method of artificial intelligence and the use of open-source satellite images is especially important for supporting conservation strategies and environmental public policies.

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Section: Discussionmentioning

confidence: 60%

Mapping threatened canga ecosystems in the Brazilian savanna using U-Net deep learning segmentation and Sentinel-2 images: a first step toward conservation planning

Pereira¹,

Wagner²,

Kamino³

et al. 2023

Biota Neotrop.

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“…Previous applications of the InVEST HRA leveraged diverse data sources and even used the tool in combination with other methodologies, e.g., ES and coastal zone management in Belize coast (Arkema et al, 2014); marine spatial planning in the Northeast and Mid-Atlantic Ocean regions in the United States (Wyatt et al, 2017); statistical analysis of ecology and conservation policies interactions in South Korea coastal area (Chung et al, 2015); InVEST HRA and coastal vulnerability models in Brazil (Elliff and Kikuchi, 2017); Marxan habitat prioritization software in California coastal area (Studwell et al, 2021); spatially explicit criteria based on marine biodiversity habitat suitability maps (Verutes et al, 2020) or habitat connectivity metrics in Iran inland (Ghehi et al, 2020). All these approaches confirm the diversity of options when applying the InVEST HRA tool.…”

Section: Extensions Of the Invest Hramentioning

confidence: 99%

A spatial explicit vulnerability assessment for a coastal socio-ecological Natura 2000 site

et al. 2023

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In line with the global trend, the Ria de Aveiro coastal lagoon is subjected to multiple co-occurring pressures threatening vital benefits flowing from nature to people. The main objective of this research was to assess the status of habitats important for ecosystem services in the Ria de Aveiro by identifying vulnerable areas to anthropogenic threats. The pressures from seven relevant human activities (recreation, services, aquaculture, agriculture, commercial development, unintended impacts from management, and invasive alien species) were analysed based on their spatiotemporal distribution (exposure) and impact over the EUNIS habitats (EUNIS codes A2.2, A2.22 – sand flats and beaches; A2.3 – mud flats; A2.61 – seagrasses; A2.5, A2.53C, A2.535, A2.545, A2.554 – salt marshes; and, X10 – ‘Bocage,’ a landscape of small-hedged fields) in seven distinct landscape units. A prospective scenario, co-developed for the year 2030, was evaluated using a map-based risk assessment tool and brought forward the near-term vulnerability of the seagrass biotope. The highest risks posed to intertidal habitats (mud flats and salt marshes) were driven mainly by environmental management activities that support critical socio-economic sectors. Our methodology evaluated plausible threats to habitats in the near term, established baseline knowledge for the adaptive management process in Ria de Aveiro Natura 2000 site, and showcased how future assessments can inform the operationalization of ecosystem-based management as new information becomes available.

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“…As such, the HRA model was run using the InVEST software version 3.9.0. The HRA model allows the assessment of the cumulative risk posed by anthropogenic activities to habitats and/or species as well as the consequences for the provision of ecosystem services and biodiversity (Cabral et al, 2014;Caro et al, 2020;Ghehi et al, 2020;Studwell et al, 2021). Table 2 presents the data requirements of the model and how they were generated and used.…”

Section: Identi Cation Of the Stressorsmentioning

confidence: 99%

“…For this study, the Euclidean risk equation with linear decay was used (see Eq.3). This approach combines the exposure and the response values to generate a risk value for each stressor-habitat combination in each grid cell (Ghehi et al, 2020). Euclidean risk calculation considers the risk to habitat j caused by stressor k in each location (cell precisely) and calculates it as the Euclidean distance from the origin in the exposure-consequence.…”

Section: Identi Cation Of the Stressorsmentioning

confidence: 99%

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Is the Co-management Approach Effective for Mangrove Conservation in West Africa?

Gnansounou¹,

Sagoe

Mattah

et al. 2021

Preprint

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The conservation management literature has recently documented the increasing use of co-management approach to effectively conserve natural resources. Although most research qualify the co-management as highly effective, some authors also reported a number of uncertainties associated with the use of this conservation approach. Using the Mono Transboundary Biosphere Reserve (MTBR) as a case study, this work assessed the effectiveness of the co-management towards mangroves conservation in West Africa. Data were collected in two protected sites of the reserve (one in Togo and the other in Benin). Exploratory sequential mixed method via in-depth interviews (n=17), focus group discussions (n=14), household survey (n=274) and expert-based survey (n=10) were carried out, and subjected to the InVEST-based Habitat Risk Assessment (HRA) model, chi-square test and simple probability of likelihood. Results indicated that under the current co-management regimes, the anthropogenic stressors recorded in the reserve put the entire surface area of mangroves in Benin (100%) under low risk. Contrarily, 42% of the mangrove cover are under low risk and 58% under medium risk in Togo. Local perception also portrayed a large reduction of mangrove degradation in the study sites following the adoption of the co-management approach in the two countries. This study suggests that the implementation of the co-management approach has lowered anthropogenic stressors to mangroves in the reserve. However, there are some peculiar challenges (e.g., financial support provision, regular community engagement), which need to be thoroughly researched and addressed for a more effective conservation of mangroves in the MTBR.

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Integrating habitat risk assessment and connectivity analysis in ranking habitat patches for conservation in protected areas

Cited by 18 publications

References 59 publications

Mapping threatened canga ecosystems in the Brazilian savanna using U-Net deep learning segmentation and Sentinel-2 images: a first step toward conservation planning

Mapping threatened canga ecosystems in the Brazilian savanna using U-Net deep learning segmentation and Sentinel-2 images: a first step toward conservation planning

A spatial explicit vulnerability assessment for a coastal socio-ecological Natura 2000 site

Is the Co-management Approach Effective for Mangrove Conservation in West Africa?

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