Use of optimization algorithms to prioritize protected areas in Mazandaran Province of Iran

Mehri, Azade; Salmanmahiny, Abdolrassoul; Mirkarimi, Seyed Hamed; Rezaei, Hamid Reza

doi:10.1016/j.jnc.2014.05.002

Cited by 18 publications

(9 citation statements)

References 20 publications

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“…Although preference of males and juveniles was not supported at fine scales, loss of a single male or juvenile by a household can undermine the family income relying on breeding and replenishment of less productive cattle. As the Hyrcanian forest is a biodiversity hotspot and a key area for leopard conservation (Ebrahimi et al., ; Farashi & Shariati, ; Farashi et al., ) which needs better protection (Mehri et al., ), we hope that this practice will reduce conflicts and improve conservation status of this region.…”

Section: Discussionmentioning

confidence: 99%

“…It spans throughout the provinces of Golestan, Mazandaran, and Gilan on the northern slopes of the Alborz Ridge fringing the southern coast of the Caspian Sea and is part of the Caucasus–Anatolian–Hyrcanian temperate forest ecoregion and the Caucasus biodiversity hotspot (Marchese, ; Olson & Dinerstein, ; http://www.globalspecies.org). The existing network of protected areas is inadequate to achieve conservation goals, and anthropogenic threats such as infrastructure development, grazing, logging, wildlife poaching, and wood collection are widespread (Mehri, Salmanmahiny, Mirkarimi, & Rezaei, ; Noack, Manthey, Ruitenbeek, & Mohadjer, ; Sadeghian, ; Soofi et al., ). As a result, the coverage of the Hyrcanian forest has halved during the past decades from 3.6 to 1.8 million hectares (Akhani et al., ; Sagheb‐Talebi et al., ).…”

Section: Methodsmentioning

confidence: 99%

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Cattle selectivity by leopards suggests ways to mitigate human–leopard conflict

Khorozyan

Ghoddousi²,

Soufi

et al. 2018

Ecology and Evolution

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Addressing widespread livestock losses to carnivores requires information on which livestock categories are preferentially selected. We analyzed an individual‐based database of cattle grazing in forest (n = 932) and having been killed (n = 70) by leopards (Panthera pardus) in the Hyrcanian forest, Iran. We calculated Jacobs’ selectivity index for cattle age, sex, and coloration across four scales: the study area as a whole, three sites, nine villages, and 60 cattle owners. Naturally colored cattle were significantly preferred by leopards at all scales in comparison with black and black‐and‐white cattle, and there was also a preference for males and juveniles at the study area level. More research is needed to see whether cattle losses would decrease if the share of naturally colored individuals in local holdings was reduced and males and juveniles had limited access to forest. We conclude that phenotypic and biologic characteristics of livestock can affect depredation and appeal for more research in this direction, particularly within the predator–prey framework.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Cattle selectivity by leopards suggests ways to mitigate human–leopard conflict

Khorozyan

Ghoddousi²,

Soufi

et al. 2018

Ecology and Evolution

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“…In the raw-data approach, we selected the conservation area on the basis of complementarity by using the greedy algorithm. The greedy algorithm is a heuristic algorithm that uses stepwise analysis to select conservation area [32]. In the first step, the cell with the largest species richness is selected, and then additional cells that add the most additional species are selected step by step.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the ecological niche model approach in spatial conservation prioritization

et al. 2019

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Ecological niche models (ENMs) are widely used in spatial prioritization for biodiversity conservation (e.g. selecting conservation areas). However, it is unclear whether ENMs are always beneficial for such purposes. We quantified the benefit of using ENMs in conservation prioritization, comparing the numbers of species covered by conservation areas selected on the basis of probabilities estimated by ENMs (ENM approach) and those selected on the basis of raw observation data (raw-data approach), while controlling survey range, survey bias, and target size of conservation area. We evaluated three ENM algorithms (GLM, GAM, and random forests). We used virtual community data generated by simulation for the evaluation. ENM approach was effective when survey bias is strong, survey range is narrow, and target size of conservation area is moderate. The percentage of cases in which the ENM approach outperformed the raw-data approach ranged from 0.0 to 33% (GLM), 31% (GAM), and 75% (random forests) depending on conditions. The number of rare species (< 20 presence records) included in the conservation area based on the ENM approach was less than, or the same as, that of the raw-data approach. The unexpectedly limited cases in which the ENM approach was effective in the present research may depend on the conservation target we used (to cover as many species as possible in conservation area). Our results highlight urgent need for evaluating ENM’s effectiveness under other conservation targets for wise use of ENM in conservation prioritization.

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“…It does not produce a single optimal (definitive) result, but is rather a scenario generator that detects a series of suitable solutions for multiple objectives by exploring possible combinations within a reasonable time (Li et al 2009). Studies using this optimization approach have examined realigning landuse to respond to a single climate impact (Reichold et al 2010, Zhang and Huang 2014, Zhang and Huang 2015, Yoon et al 2017 or repositioning a single landuse (Caparros-Midwood et al 2015, Li et al 2009, Neema and Ohgai 2010, Reichold et al 2010, Mehri et al 2014. However, landuse conversions in specific areas may lead to other conversions to maintain the current landuse proportions, or produce changes in regional resilience.…”

Section: Introductionmentioning

confidence: 99%

Modeling spatial climate change landuse adaptation with multi-objective genetic algorithms to improve resilience for rice yield and species richness and to mitigate disaster risk

Yoon

Thorne

Park

et al. 2019

Environ. Res. Lett.

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As climate change is ongoing, many studies have recently focused on adaptation to climate change from a spatial perspective. However little is known about how changing the spatial composition of landuse could improve climate change resilience. Consideration of climate change impacts when spatially allocating landuse could be a useful and fundamental long term adaptation strategy, particularly for regional planning. Here, we identify climate adaptation scenarios based on existing extents of three landuse classes using multi-objective genetic algorithms for a 9982 km 2 region with 3.5 million inhabitants in South Korea. We selected five objectives for adaptation based on predicted climate change impacts and regional economic conditions: minimization of disaster damage and existing landuse conversion, maximization of rice yield, protection of high species richness areas, and economic value. We generated 17 Pareto landuse scenarios by six weighted combinations of the adaptation objectives. Most scenarios, although varying in magnitude, showed better performance than the current spatial landuse composition for all adaptation objectives, suggesting that some alteration of current landuse patterns could increase overall climate resilience. Given the flexible structure of the optimization model, we expect that regional stakeholders could efficiently generate other scenarios by adjusting model parameters (weighting combinations) or replacing input data (impact maps), and selecting a scenario depending on preference or a number of problem-related factors.

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Use of optimization algorithms to prioritize protected areas in Mazandaran Province of Iran

Cited by 18 publications

References 20 publications

Cattle selectivity by leopards suggests ways to mitigate human–leopard conflict

Cattle selectivity by leopards suggests ways to mitigate human–leopard conflict

Evaluation of the ecological niche model approach in spatial conservation prioritization

Modeling spatial climate change landuse adaptation with multi-objective genetic algorithms to improve resilience for rice yield and species richness and to mitigate disaster risk

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