Complementarity-based Algorithms Research Articles

Investigating the planning efficiency of species richness- and complementarity-based algorithms in data deficient areas

Efficient conservation planning is a necessary approach for protecting endangered species with deficient distribution data and reducing biodiversity loss. Richness- and complementarity-based algorithms can improve conservation planning efficiency to different degrees, but differences in planning efficiency caused by data availability and algorithms are often disregarded and require clarification. Here, we classified endangered, endemic, and national key protected plant species based on their occurrence data availability for species distribution modeling. We implemented species richness- (SRA), species number complementarity- (SNCA) and species value complementarity-based algorithms (SVCA) to identify priority conservation areas (PCAs) in the Southeast Himalaya Biodiversity Priority Conservation Area. We established six scenarios and compared their planning efficiencies. The spatial distribution of PCAs and their conservation efficiency varied depending on data availability and optimization algorithms. The 17%, 30%, and 45% PCAs identified in the six scenarios differed in geographical pattern, whereas differences in species occurrences had less effect on their conservation efficiency. Specifically, the PCAs identified by using species with sufficient data as surrogates captured species with high conservation value. Additionally, SNCA was better suited for capturing species number, and SVCA was more cost-effective for protecting species area and species of high conservation value, but SRA distribution pattern presented less fragmented and well-connected. Nevertheless, 50.11% area of the optimized PCAs remained uncovered by existing protected areas. Our analysis reveals the influence of species occurrence data availability, and algorithms on planning efficiency, and provides a cost-effective solution for improving planning efficiency and optimizing protected area network in data deficient areas.

Comparing the efficiencies of individual-scoring and complementarity-based algorithms in prioritizing existing protected areas

Evaluation of conservation priorities in existing protected areas (PAs) is critical for effective allocation of limited conservation resources and optimization of PA structure. Traditional approaches of prioritizing within existing PAs are based on individual-scoring and complementarity-based algorithms, and are often guided by individual natural features rather than complementarity among PAs. However, the efficiencies of synthesizing individual-scoring and complementarity-based algorithms in prioritizing PAs are still unknown. Here, we implemented three algorithms for prioritizing 42 Nature Reserves (NRs) in Yunnan Province, China, including an individual-scoring algorithm (ISA), species-number complementarity algorithm (SNCA), and a new species-value complementarity algorithm (SVCA) which synthesized the former two algorithms. Subsequently, the species number and species conservation value evaluated the efficiencies of three algorithms and explored their implications in conservation planning. We found that the spatial distribution of priority levels of NRs varied in different biogeographical regions, and that most of the national NRs received higher conservation priorities than provincial NRs in all three algorithms. Additionally, the average species number conservation efficiency in SNCA was 3.45 and 1.61% higher than in ISA and SVCA, respectively, while the average species conservation value efficiency in SVCA was 1.83 and 3.37% higher than in SNCA and ISA. These results indicated that SNCA could protect more species at a minimum cost if conservation planning was aimed only at protecting the number of species. However, when species conservation value is the focus of conservation planning, SVCA was more cost-effective. Our analysis supports efforts at efficient allocation of conservation resources in individual PAs and entire PA network at different biogeographical scales, and informs decision-makers on leveraging cost-effective conservation efforts in the future.

Use of Habitats as Surrogates of Biodiversity for Efficient Coral Reef Conservation Planning in Pacific Ocean Islands

Marine protected areas (MPAs) have been highlighted as a means toward effective conservation of coral reefs. New strategies are required to more effectively select MPA locations and increase the pace of their implementation. Many criteria exist to design MPA networks, but generally, it is recommended that networks conserve a diversity of species selected for, among other attributes, their representativeness, rarity, or endemicity. Because knowledge of species' spatial distribution remains scarce, efficient surrogates are urgently needed. We used five different levels of habitat maps and six spatial scales of analysis to identify under which circumstances habitat data used to design MPA networks for Wallis Island provided better representation of species than random choice alone. Protected-area site selections were derived from a rarity-complementarity algorithm. Habitat surrogacy was tested for commercial fish species, all fish species, commercially harvested invertebrates, corals, and algae species. Efficiency of habitat surrogacy varied by species group, type of habitat map, and spatial scale of analysis. Maps with the highest habitat thematic complexity provided better surrogates than simpler maps and were more robust to changes in spatial scales. Surrogates were most efficient for commercial fishes, corals, and algae but not for commercial invertebrates. Conversely, other measurements of species-habitat associations, such as richness congruence and composition similarities provided weak results. We provide, in part, a habitat-mapping methodology for designation of MPAs for Pacific Ocean islands that are characterized by habitat zonations similar to Wallis. Given the increasing availability and affordability of space-borne imagery to map habitats, our approach could appreciably facilitate and improve current approaches to coral reef conservation and enhance MPA implementation.

Systematic conservation assessment for the Mesoamerica, Chocó, and Tropical Andes biodiversity hotspots: a preliminary analysis

Using IUCN Red List species as biodiversity surrogates, supplemented with additional analyses based on ecoregional diversity, priority areas for conservation in Mesoamerica, Choco, and the Tropical Andes were identified using the methods of systematic conservation planning. Species’ ecological niches were modeled from occurrence records using a maximum entropy algorithm. Niche models for 78 species were refined to produce geographical distributions. Areas were prioritized for conservation attention using a complementarity-based algorithm implemented in the ResNet software package. Targets of representation for Red List species were explored from 10 to 90% of the modeled distributions at 10% increments; for the 53 ecoregions, the target was 10% for each ecoregion. Selected areas were widely dispersed across the region, reflecting the widespread distribution of Red List species in Mesoamerica, Choco, and the Tropical Andes, which underscores the region’s importance for biodiversity. In general, existing protected areas were no more representative of biodiversity than areas outside them. Among the countries in the region, the protected areas of Belize performed best and those of Colombia and Ecuador worst. A high representation target led to the selection of a very large proportion of each country except Colombia and Ecuador (for a 90% target, 83–95% of each country was selected). Since such large proportions of land cannot realistically be set aside as parks or reserves, biodiversity conservation in Mesoamerica, Choco, and the Tropical Andes will require integrative landscape management which combines human use of the land with securing the persistence of biota.

Irreplaceability of river networks: towards catchment‐based conservation planning

1 This study has adapted a complementarity-based area-selection method to estimate conservation value/irreplaceability for river systems. Irreplaceability represents the likelihood that an area will be required as part of a conservation system that achieves all conservation targets. We adapt this measure – often used in marine or terrestrial planning – to consider whole-of-catchment protection in a riverine setting. 2 After dividing the Australian state of Victoria into 1854 subcatchments, we successfully modelled distributions of 400 benthic macroinvertebrate taxa using generalized additive models. We calculated the minimum area required to protect all taxa using three different heuristic selection algorithms. The algorithms were modified to consider the entire upstream catchment for any subcatchment. 3 A summed rarity algorithm, corrected for upstream area, proved to be the most efficient, requiring 100 000 hectares less total catchment area to represent all taxa than the second most efficient algorithm. We calculated irreplaceability by running the algorithm 1000 times and randomly removing 90% of the catchments in each run. From this analysis, we estimated two metrics: Fs (the frequency of selection) and average c (average contribution to conservation targets). 4 Four groups of catchments were identified: (i) catchments that have high contributions and are always or very frequently selected; (ii) catchments that have high contributions and are infrequently selected; (iii) catchments that are always or very frequently selected but contribute few taxa; and (iv) catchments that are infrequently selected and contribute few taxa. 5 Synthesis and applications. For the first time, a complementarity-based algorithm has been adapted to a riverine setting. This algorithm acknowledges the connected nature of rivers by considering not only the local assemblages, but also upstream areas that need to be protected. We demonstrated that using standard algorithms in these connected systems would lead to two mistakes, namely: (i) not all taxa would be covered by reserves that were buffered from potential human disturbances upstream; and (ii) the standard algorithms would not lead to the most efficient solution, potentially costing additional millions of dollars to any conservation scheme. We therefore recommend the use of our algorithm or a similar riverine adaptation of reserve design algorithms to ensure adequate and efficient conservation planning.

But is it progress? On the alleged advances of conservation biology over ecology

As conservation biology has developed as a distinct discipline from ecology, conservation guidelines based on ecological theory have been largely cast aside in favor of theory-independent decision procedures for designing conservation reserves. I argue that this transition has failed to advance the field toward its aim of preserving biodiversity. The abandonment of island biogeography theory in favor of complementarity-based algorithms is a case in point. In what follows, I consider the four central objections raised against island biogeographic conservation guidelines, arguing that they fail to undermine the credibility of this framework as a conservation tool. At best, these objections call for a more careful application of this framework to conservation problems, not its wholesale abandonment. At the same time, complementarily-based algorithms are biased in favor of networks of small reserves containing non-overlapping species. These conditions threaten to promote inbreeding depression, genetic drift and other factors that increase a population’s risk of extinction. Therefore, recent developments in the field of conservation biology have arguably not contributed to its ultimate aim of preserving the maximum amount of biodiversity in the long run.

Conservation of Freshwater Biodiversity: a Comparison of Different Area Selection Methods

The biodiversity of freshwater systems is endangered, especially in Mediterranean semiarid areas such as the south east of the Iberian Peninsula, whose rich and endemic biota is threatened by the development of surrounding land-crop irrigation. For this reason, the prioritization of areas for biodiversity conservation is an urgent target. In this study we used data records of water beetles from a province of the southeast of Spain for assessing priority areas for freshwater biodiversity conservation. We compare the performance of various area-selection methods, ranging from scoring procedures to complementarity-based algorithms, which are based on different criteria such as richness, rarity and vulnerability. The complementarity approaches were more efficient than methods using scoring or richness and rarity hotspots for representing conservation targets in a given number of areas and for identifying the minimum set of areas containing all species at least once. Within these, the richness-based algorithm was more efficient than rarity-based algorithm. Crucial target habitats for aquatic biodiversity conservation in the area studied are streams at medium altitude, hypersaline streams, and endorreic and karstic complexes.

Selecting small reserves in a human-dominated landscape: A case study of Hong Kong, China

In this case study of a degraded tropical landscape, we examine how the protected area system in Hong Kong, China, should be modified to improve its efficiency in protecting the surprisingly rich biota. The challenge lies in the fine scale of site selection, and the absence of a core area with high species richness and rarity. Site selection was first conducted in 1 km grid units by selecting hotspots and irreplaceable sites using field records for eight groups of species (amphibians, reptiles, mammals, breeding birds, ants, butterflies, dragonflies and rare vascular plants). The habitats of conservation value within the selected grid units were then delineated on the basis of expert knowledge. Recommendations for increasing the total existing protected area by 6% (i.e. an additional 2% of Hong Kong's total land area) were submitted to the Government in August 2000. To test the robustness of the results, site selection was repeated in 2001 using updated data and different selection methods. The numbers of squares selected by complementarity-based algorithms were similar to those by the hotspots and irreplaceable site method. Sites selected for rare species were very sensitive to data completeness, implying that the application of complementarity-based algorithms at fine scales might be limited.

Biodiversity representation in Uganda’s forest IBAs

Prioritising sites for action is essential due to current rates of population and habitat loss and increasing human numbers and demands on natural resources. Many prioritisation methods have been suggested at different spatial scales, including those based on single-taxon criteria. One such scheme is BirdLife International’s system of important bird areas (IBAs). Whilst IBA site qualification criteria are entirely bird-based, the intention is that these sites should conserve wider biodiversity. This idea has yet to be tested quantitatively, but a unique opportunity to do so exists in Uganda, where the Forest Department (FD) has conducted comprehensive inventories of five taxa (birds, butterflies, large moths, small mammals and woody plants) in 50 Ugandan Forest Reserves. Independently, Nature Uganda (the national conservation NGO) identified 30 IBAs, 13 of which were also forest reserves. In this paper we used the FD dataset to evaluate the efficiency with which Uganda’s forest IBAs capture species richness in other groups. In a combined area of 5445 km 2, Uganda’s 13 forest IBAs contained as many species as did an equal-sized set of sites selected by applying a complementarity-based algorithm to all FD data. Modifying this simple greedy algorithm to take account of site area resulted in a priority set that outperformed IBAs, but that consisted of far more sites for similar species representation. Our results illustrate the utility of the IBA network for representing a wide range of biodiversity. A major benefit of IBAs is the low cost of obtaining bird data relative to comprehensive multi-taxon inventories. The general applicability of these results outside tropical forests requires further investigation.

Complementarity-based algorithms for selecting sites to preserve grassland plant species

Complementarity-based algorithms, that delimit sets of sites maximizing the representation of biodiversity, may be useful for targeting management resources to a group of prioritised grassland sites. A heuristic algorithm MSET was applied to select complementarity sets of semi-natural grassland sites in southern Finland. Site selection was carried out for three different data sets (grassland, indicator and rare species) of vascular plant species occurrences, and three target levels of species occurrences (1, 5 or 10 times). The overlap of the derived sets of sites varied between 46% and 88%, suggesting that algorithms can be sensitive to the delimitation of target species set. The results of algorithms based on species-occurrence data only should be critically evaluated with regard to the habitat quality and other species persistence issues. When applied with caution, complementarity-based methods, therefore offer a promising approach in prioritising grasslands.

How much of the vertebrate diversity of sub-Saharan Africa is catered for by recent conservation proposals?

A database documenting the distribution of birds, mammals, amphibians and snakes across 1° latitude and longitude squares of mainland sub-Saharan Africa provides an opportunity to quantify how many of these vertebrates are potentially catered for by recent large-scale conservation proposals. Sets of priority areas proposed by BirdLife International, the World Wildlife Fund (USA), the World Conservation Union and the World Wide Fund for Nature, Conservation International, and the World Resources Institute contain between 45 and 93% of 3752 species of birds, mammals, snakes and amphibians breeding in this area. Gaps in the coverage of vertebrates were found in all large-scale proposals, and these are mapped. Most of the conservation proposals perform better than random selection of similar sized areas of Africa, with the proposals focused on species performing more efficiently than schemes based on large areas of intact habitat or process-related criteria. Four of the schemes approach the performance of a complementarity-based algorithm that aims to maximise the number of species captured within a given area of land, and which has been widely advocated as a tool for conservation planning. The reasons for this are discussed and the relevance of the results for conservation planning at coarse and fine scales are explained.

Robustness of reserve selection procedures under temporal species turnover.

Complementarity-based algorithms for the selection of reserve networks emphasize the need to represent biodiversity features efficiently, but this may not be sufficient to maintain those features in the long term. Here, we use data from the Common Birds Census in Britain as an exemplar data set to determine guidelines for the selection of reserve networks which are more robust to temporal turnover in features. The extinction patterns found over the 1981-1991 interval suggest that two such guidelines are to represent species in the best sites where they occur (higher local abundance) and to give priority to the rarer species. We tested five reserve selection strategies, one which finds the minimum representation set and others which incorporate the first or both guidelines proposed. Strategies were tested in terms of their efficiency (inversely related to the total area selected) and effectiveness (inversely related to the percentage of species lost) using data on eight pairs of ten-year intervals. The minimum set strategy was always the most efficient, but suffered higher species loss than the others, suggesting that there is a trade-off between efficiency and effectiveness. A desirable compromise can be achieved by embedding the concerns about the long-term maintenance of the biodiversity features of interest in the complementarity-based algorithms.