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6.1: Species Diversity as a Surrogate for Global Biodiversity - Biology

6.1: Species Diversity as a Surrogate for Global Biodiversity - Biology


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Global biodiversity is frequently expressed as the total number of species currently living on Earth, i.e., its species richness. In these cases, scientists may accidentally overlook certain species when preparing inventories of biota, causing them to describe and name an already known species.

More significantly, some species are very difficult to identify. For example, taxonomically "cryptic species" look very similar to other species and may be misidentified (and hence overlooked as being a different species). Thus, several different, but similar-looking species, identified as a single species by one scientist, are identified as completely different species by another scientist. For further discussion of cryptic species, with specific examples of cryptic frogs from Vietnam, see Inger (1999) and Bain et al., (in press).

Scientists expect that the scientifically described species represent only a small fraction of the total number of species on Earth today. Many additional species have yet to be discovered, or are known to scientists but have not been formally described. Scientists estimate that the total number of species on Earth could range from about 3.6 million up to 117.7 million, with 13 to 20 million being the most frequently cited range (Hammond, 1995; Cracraft, 2002).

The estimation of total number of species is based on extrapolations from what we already know about certain groups of species. For example, we can extrapolate using the ratio of scientifically described species to undescribed species of a particular group of organisms collected from a prescribed area. However, we know so little about some groups of organisms, such as bacteria and some types of fungi, that we do not have suitable baseline data from which we can extrapolate our estimated total number of species on Earth. Additionally, some groups of organisms have not been comprehensively collected from areas where their species richness is likely to be richest (for example, insects in tropical rainforests). These factors, and the fact that different people have used different techniques and data sets to extrapolate the total number of species, explain the large range between the lower and upper figures of 3.6 million and 117.7 million, respectively.

While it is important to know the total number of species of Earth, it is also informative to have some measure of the proportional representation of different groups of related species (e.g. bacteria, flowering plants, insects, birds, mammals). This is usually referred to as the taxonomic or phylogenetic diversity. Species are grouped together according to shared characteristics (genetic, anatomical, biochemical, physiological, or behavioral) and this gives us a classification of the species based on their phylogenetic, or apparent evolutionary relationships. We can then use this information to assess the proportion of related species among the total number of species on Earth. Table contains a selection of well-known taxa.


Biodiversity hotspots for conservation priorities

Conservationists are far from able to assist all species under threat, if only for lack of funding. This places a premium on priorities: how can we support the most species at the least cost? One way is to identify ‘biodiversity hotspots’ where exceptional concentrations of endemic species are undergoing exceptional loss of habitat. As many as 44% of all species of vascular plants and 35% of all species in four vertebrate groups are confined to 25 hotspots comprising only 1.4% of the land surface of the Earth. This opens the way for a ‘silver bullet’ strategy on the part of conservation planners, focusing on these hotspots in proportion to their share of the world's species at risk.


Introduction

Although fresh waters cover only 2.3% of the Earth’s surface 1 , the number of described species per area is much higher than that of terrestrial and marine ecosystems 2 . Further, they support approximately 10% of all known species, which includes 40% of global fish species and

33% of global vertebrate species 3,4 . However, declines in biodiversity are far greater in fresh waters than the most affected terrestrial ecosystems 5 . Freshwater ecosystems are the most globally threatened they also concentrate human populations that have led to widespread habitat degradation, pollution, flow regulation, water extraction, unsustainable fisheries, alien species introductions, changing climates, infectious diseases, harmful algal blooms and expanding hydropower 6,7,8 . As a result, nearly one-third of species in fresh waters have been classified as ‘Endangered’ in the International Union for Conservation of Nature (IUCN) Red List of Threatened Species 9 .

Monitoring and managing all aspects of biodiversity is challenging, and ‘shortcuts’, such as a surrogate species (i.e., indicator, umbrella and flagship species), whereby resources are focused on a limited number of focal species for broader benefit 10 . Depending on the conservation goals, several concepts of surrogate species have been distinguished 11 . ‘Indicator species’ have been used to assess the magnitude of anthropogenic disturbance and changes in habitat (health indicators), to locate areas of high regional biodiversity (biodiversity indicators), and to monitor population trends in other species (population indicators) 12 ‘umbrella species’ have been proposed as a way to manage entire communities by focusing on the requirements of the most widespread species 13 ‘flagship species’ have been employed to attract public attention and support for nature at a global or national levels, and potentially attract funding for larger environmental issues 14 . Regardless of their underlying assumptions, the expectation is that the presence and/or abundance of a surrogate species is a means to understanding the composition, state and function of a more complex community, and the use of them has been thought to have merit for conservation and management of natural environments 10,15,16 . Many species have been proposed as surrogates in terrestrial (large mammals or birds) 17,18 and aquatic ecosystems (mammals or fish) 19,20,21 , but the assumed functional or distributional relationships between these and other taxa are rarely tested 22 . Although freshwater surrogate species, including migratory species, have been considered conceptually, they are rarely used in aquatic ecosystems 16,23 .

Recently, it was proposed that catadromous eels (genus Anguilla), including 16 species that spawn in the open ocean and grow in continental waters, are promoted as a flagship species for aquatic conservation 24 . This was proposed on the grounds that (1) stocks of some anguillid eels (hereafter referred to as eels) have experienced remarkable declines in recent decades, which has led to 10 of 16 species assessed now being listed as ‘Threatened’ or ‘Nearly Threatened’ in the IUCN Red List of Threatened Species 25 (2) they have catadromous life-cycles involving extended migrations through both marine and freshwater environments in more than 150 countries 26 (3) threats such as climate change, barriers to migration, pollution, habitat loss, and unsustainable exploitation and trade threaten eels globally 27 , all of which will have significant impacts on thousands of other aquatic species that are resident in both marine and freshwater ecosystems 24 . Given the catadromous life-cycles of eels that have long fascinated researchers and their global commercial and cultural importance, they have the potential to stimulate public interest and support for conservation. Moreover, considering their ecological characteristics, broad habitat use extending from saline bays to upland headwaters 28 as well as polytrophic feeding habits 29,30,31,32,33 , they have potential to be not only a flagship species, but also indicator and umbrella species for freshwater biodiversity. This could have huge benefits for other aquatic flora and fauna, many of which are even more poorly understood than eels 24 .

Here, we demonstrate that eels can be a surrogate species for conservation of freshwater biodiversity using data relating to aquatic species including both the Japanese eel (A. japonica) and the giant mottled eel (A. marmorata). The research was conducted in rivers in Japan, a part of islands formed by the accretionary prism 34,35 . It has been known that migratory diadromous species are generally predominant in rivers of the regions formed by the accretionary prism 36 , because many small rivers are formed in these regions as a result of having many mountainous areas compared to the eroded regions and the craton 34 . Since diadromous species recruit between marine and freshwater environments, trans-river structures such as dams and weirs have a critical impact on declines of population for diadromous species including eels 37 . Thus, such islands offer suitable sites to investigate possible effects of river-ocean connectivity on freshwater biodiversity.

The potential of using eels as surrogate species for conservation of freshwater faunal diversity was evaluated by testing (1) whether eels were the widest topographically-distributed species in freshwater ecosystems, (2) whether eels were appropriate indicator of river-ocean connectivity and (3) whether eels were a high-order consumer in freshwater ecosystems. Finally, we discussed the potential of eels to act as a comprehensive symbol for freshwater conservation by synthesizing the results of the present study and previous reports showing the commercial and cultural importance of eels.


3. RESULTS

3.1. Characteristics of the inselberg flora

We recorded 1,060 species from 92 families from the 478 plots. The Fabaceae (112 species), Myrtaceae (75), Asteraceae (77), Proteaceae (67), Cyperaceae (61), Orchidaceae (55), Poaceae (49), Asparagaceae (36), Ericaceae (34), and Stylidiaceae (32) were the ten most species‐rich families, but their relative abundance varied among the three habitats (Table ​ (Table2 2 ).

Table 2

The ten most species‐rich families in the Southwest Australian Floristic Region compared to those for three habitats, herbaceous vegetation on outcrops (HO), woody vegetation on outcrops (WO), and woody vegetation at base of outcrops (WOB) on 16 granite inselbergs, sorted by the number of species (n) recorded in our plots per family

HO nWO nWOB nTotal SWAFR a n
Asteraceae47Myrtaceae75Fabaceae79Myrtaceae1,436
Orchidaceae31Fabaceae75Myrtaceae72Fabaceae1,156
Poaceae24Asteraceae49Asteraceae51Proteaceae914
Cyperaceae14Orchidaceae46Proteaceae49Orchidaceae422
Stylidiaceae13Proteaceae45Cyperaceae43Ericaceae362
Droseraceae13Poaceae40Poaceae37Asteraceae330
Centrolepidaceae12Cyperaceae40Orchidaceae30Cyperaceae262
Asparagaceae9Ericaceae27Asparagaceae28Goodeniaceae231
Araliaceae8Asparagaceae22Goodeniaceae19Stylidiaceae227
Geraniaceae7Goodeniaceae19Ericaceae18Malvaceae196

There were large numbers of rarely sampled species in our dataset, with 267 species (25.2%) recorded in a single plot across all plots and 544 species (51.3%) recorded in plots on a single inselberg (Table ​ (Table3). 3 ). Sixteen life𠄏orm groups were recorded, with shrubs (37.4%), annual herbs (14%), perennial graminoids (12.3%), perennial herbs (11.3%), and perennial geophytes (7.2%) being most common (Table ​ (Table3). 3 ). Eight of the life𠄏orm groups each included less than 2% of the taxa recorded.

Table 3

Life𠄏orm spectra for the total flora from 478 plots on 16 granite inselbergs in the Southwest Australian Floristic Region, and species occurring in only one single plot (on all inselbergs)or to plots on a single inselberg

Life𠄏ormNo speciesTotal flora (%)Single plotSingle plot (%)Single inselbergSingle inselberg (%)
Annual fern10.110.410.2
Annual graminoid484.583.0132.4
Annual herb14814.04115.46211.4
Annual succulent herb121.120.730.6
Perennial climber/epiphyte262.551.9101.8
Perennial climber/epiphyte ‐parasite50.500.000.0
Perennial fern131.231.161.1
Perennial graminoid13012.32910.97012.9
Perennial herb12011.3269.76211.4
Perennial herb�rnivore40.410.440.7
Perennial herb geophyte777.3176.4315.7
Perennial herb geophyte�rnivore141.331.171.3
Perennial succulent herb20.200.000.0
Shrub39637.412044.924344.7
Tree545.183.0275.0
Shrub or tree parasite100.931.150.9
N1,060 267 544 

Alien species made up approximately 7% of the total flora with annual herbs being the most common life𠄏orm (39 species, 26% of annual herbs) followed by annual graminoids (19 species, 40% of annual graminoids), perennial herbs (nine species, 8% of perennial herbs), annual succulent herbs (three species, 25% of annual succulent herbs), and perennial graminoids (two species, ρ% of perennial graminoids).

3.2. Diversity of the inselberg flora

At the plot level, local variables were significantly correlated (p <਀.05) with species richness in HO and WO plots. Mean species richness in HO plots ranged from 7.8 to 18.0 species per plot and increased with increasing saga wetness index (t 15,148 =਄.94, p <਀.001, r 2  =਀.49). Mean species richness in WO plots ranged from 13.5 to 26.3 species per plot and increased with soil depth (t 15,151 =ਃ.3, p <਀.01) and decreased with increasing TPI (t 15,151 = 𢄢.2, p <਀.05) with the model including both variables having an r 2  =਀.29. Mean species richness in WOB habitats varied from 14.3 species to 57.0 species per plot but was not correlated with any of the soil depth or topographic variables.

At the outcrop level, species richness in HO plots was relatively constant across inselbergs and not correlated with outcrop area, isolation, or any of the four bioclimatic variables. Total species richness in WO and WOB plots was not correlated with inselberg area or isolation, nor with three of the bioclimatic variables. However, species richness increased significantly with precipitation of the driest quarter for WO plots (Wald χ 1 , 14 2  =ꀑ.83, p <਀.001, r 2  =਀.45) and decreased significantly with precipitation of the driest quarter for WOB plots (Wald χ 1 , 14 2  =ਇ.28, p <਀.01, r 2  =਀.31).

3.3. Contribution of environment and geographic space to species turnover

Four variables were retained as predictors of species turnover in the GDM for HO plots (Table ​ (Table4). 4 ). Precipitation of the driest quarter was relatively the most important variable followed by precipitation of the wettest quarter, interplot geographic distance, and aspect‐northness. The four variables accounted for 17.1% of the total explained model deviance (Figure ​ (Figure3, 3 , Table ​ Table5). 5 ). Six variables were retained as predictors of species turnover in the GDM for WO plots (Table ​ (Table4). 4 ). Interplot geographic distance was relatively the most important variable followed by mean temperature of the driest quarter, precipitation of the driest quarter, precipitation of the wettest quarter, soil depth, and topographic ruggedness index. The six variables accounted for 39.9% of the total explained model deviance (Figure ​ (Figure3, 3 , Table ​ Table5). 5 ). Three variables were retained as predictors of species turnover in the GDM for WOB plots (Table ​ (Table4). 4 ). Interplot geographic distance was relatively the most important variable followed by precipitation of the wettest quarter and mean temperature of the driest quarter. The three variables accounted for 65.8% of the total explained model deviance (Figure ​ (Figure3, 3 , Table ​ Table5 5 ).

Table 4

Relative importance of the significant predictor variables used for modeling the compositional dissimilarity of plant communities in three habitats across 16 granite inselbergs in the Southwest Australian Floristic Region

GradientHOWOWOB
Interplot geographic distance0.341.212.02
Mean temperature wettest quarter
Mean temperature driest quarter0.911.81
Precipitation wettest quarter0.470.861.40
Precipitation driest quarter0.781.11
Soil depth0.46
Aspect�stness
Aspect‐northness0.15
Curvature
Topographic position index
Topographic wetness index
Topographic ruggedness index0.39
Saga wetness index

Habitats are herbaceous vegetation of soil𠄏illed depressions on the outcrop (HO) woody vegetation of soil𠄏illed depressions on the outcrop (WO) and woody vegetation on deeper soils at the base of outcrops (WOB). Relative importance is determined by summing the coefficients of the I‐splines (each I‐spline has three coefficients) from generalized dissimilarity modeling (see Figure ​ Figure4 4 ).

Partitioning of generalized dissimilarity model deviance explained in plant species turnover on 16 granite inselbergs across the Southwest Australian Floristic Region for three habitats: (a) herbaceous vegetation of soil𠄏illed depressions on the outcrop (HO) (b) woody vegetation of soil𠄏illed depressions on the outcrop (WO) and (c) woody vegetation on deeper soils at the base of outcrops (WOB). Three sets of explanatory variables were used: climatic, geographical distance, and site variables. For full details of the unique and shared contributions to explained deviance, see Table ​ Table5. 5 . The Venn diagram was drawn in eulerAPE 3 (Micallef & Rodgers, 2014)

Table 5

Generalized dissimilarity model deviance in plant species turnover on 16 granite outcrops explained by selected sets of variables (climate, geographical distance, and site) partitioned into corresponding unique (a,b,c) and shared (ab, ab, bc, abc) contributions as annotated on Figure ​ Figure3 3

Explanatory setHabitat
HOWOWOB
Climate (a)6.57.25.7
Geographical distance (b)1.02.96.1
Site (c)0.351.80.0
Climate ∩ geographical distance (ab)9.327.054
Climate ∩ site (ac)0.00.40.0
Site ∩ GD (bc)0.050.30.0
Climate ∩ site ∩ geographical distance (abc)0.050.30.0
Total (climate ∪ site ∪ GD)17.239.965.8

HO, herbaceous vegetation of soil𠄏illed depressions on the outcrop (HO) WO, woody vegetation of soil𠄏illed depressions on the outcrop (WO) WOB, woody vegetation on deeper soils at the base of outcrops (WOB).

The I‐spline fitted functions describing the magnitude and rate of compositional differences in the three habitats were nonlinear for most variables (Figure ​ (Figure4), 4 ), with rates of turnover varying with position along the aridity gradient, being greatest at low levels of precipitation of the wettest quarter (Figure ​ (Figure4b). 4 b). Additionally, in WO plots rates of species turnover increased with soil depth (Figure ​ (Figure4e) 4 e) and low values of TRI (Figure ​ (Figure4f), 4 f), while in HO plots rates of species turnover increased at a greater rate in plots with a more southerly aspect (Figure ​ (Figure4 4 g).

Generalized dissimilarity model𠄏itted I‐splines for variables found to be significantly associated with patterns of beta diversity across 16 granite outcrops in the Southwest Australian Floristic Region divided into three habitat types: herbaceous vegetation of soil𠄏illed depressions on the outcrop (HO) woody vegetation of soil𠄏illed depressions on the outcrop (WO) and woody vegetation on deeper soils at the base of outcrops (WOB). The maximum height reached by each curve indicates the total amount of compositional turnover associated with that variable, holding other variables constant and its shape indicates the rate of compositional turnover variation along the gradient

3.4. Relative contribution of spatial turnover and nestedness to compositional differences

Beta diversity, as measured by Baselga's multiple site dissimilarity measure (β SOR), ranged from 0.892 to 0.935 for the three habitat types on the inselbergs. This was almost entirely due to spatial species turnover (β SIM range 0.875𠄰.910) with a very low component due to nestedness (β NES 0.017𠄰.025 Table ​ Table6). 6 ). A similar pattern occurred for the whole outcrop flora.

Table 6

Baselga's multiple site dissimilarity measure (β SOR) and its components related to species replacement (β SIM) and nestedness (β NES) across granite inselbergs in the Southwest Australian Floristic Region

Values are given for plots from three habitat types on the inselbergs, herbaceous vegetation of soil𠄏illed depressions on the outcrop (HO) woody vegetation of soil𠄏illed depressions on the outcrop (WO) and woody vegetation on deeper soils at the base of outcrops (WOB).


Introduction

Species extinction is one of the most significant environmental challenges humanity faces (Ceballos et al. 2017 ) rates are up to 1000 times higher than what is considered natural (De Vos et al. 2015 ). In response to this challenge, most countries have ratified the Convention on Biological Diversity (CBD) and the 2020 Strategic Plan for Biodiversity (United Nations Convention on Biological Diversity 2010). These plans commit all signatories to prevent extinction of known threatened species and improve their conservation status by 2020. A critical factor of success in achieving these plans is financing, yet globally there is inadequate investment in conservation (Waldron et al. 2017 ). With an increasing species extinction crisis (IPBES 2018 ), a looming CBD deadline (United Nations Convention on Biological Diveristy 2010), and limited conservation funding globally (Waldron et al. 2017 ), better methods to prioritize investment of resources in species recovery are needed.

We used decision-analysis methods for targeting investment in management of threatened species (e.g., Joseph et al. 2009 Carwardine et al. 2012 Chadés et al. 2015 ) to quantify the efficiency of prioritizing species’ management while considering geographic range and threat overlaps. We calculated efficiency based on the number of species that could be managed under a strict budget and prioritized species management based on those that are the most umbrella effective (i.e., species that can simultaneously benefit many other flora and fauna from the management of itself). We tested this method on the Australian federal government's recent plan for prioritizing 73 threatened species, which is part of a wider effort to protect and recover all plants and animals at risk of extinction. We believe that our problem-based prioritization approach to choose umbrella species is applicable to other regions that contain many imperiled species.

Australia has approximately 1828 threatened species and one of the highest extinction rates on Earth (Woinarski et al. 2011 ). The prioritized species were chosen by the federal government because they were thought to be important umbrella species (Australian Government 2015 ), but it is unknown how effectively they represent all threatened flora and fauna.


Abstract

The use of biodiversity surrogates is an increasingly popular tool, because it provides strong results while reducing the costs of conservation studies. Here, we hypothesize that cuckoo (Cuculus canorus) occurrence may correlate with high bird species richness based on the assumption that their presence should mirror the richness of their potential avian hosts and the overall bird community. Specifically, we assessed the association between species occurrence and taxonomic diversity patterns on a multi-spatial scale using datasets from seven European countries. Our results show that high bird species richness is a good proxy for cuckoo occurrence, and the best results were based on data from point counts. The species was almost absent at sites with low species richness, suggesting that the presence of cuckoo is an appropriate surrogate of bird biodiversity. The accuracy of the models ranged from 0.68–0.71 (for large spatial scale) to 0.86 (for local spatial scale) and provided valuable indications of bird taxonomic diversity distribution on all different types of environments monitored in each country. These associations are possibly related to co-evolutionary relationships with host species (correlated with overall species richness) and the cuckoo’s preference for sites that are attractive to many other bird species, due to high habitat diversity or abundant food resources. Our findings highlight how conservation planners can use cuckoo occurrence as a surrogate to maximize efficiency when studying bird species richness patterns. These results also demonstrate the advantages of using the cuckoo rather than top predators as a potential surrogacy tool for citizen scientist programs.


Methods

Katydid Red Listing

Over two decades, PN visited global museum collections, identified specimens and recorded locality data and measurements into his MANTIS database [31]. Using MANTIS and OSF [32], a list of 167 katydid species known to occur in South Africa, Lesotho and Swaziland was compiled. Of the full list, 133 species (79.64%) were assessed for the IUCN’s Red List [33]. Taxa which could not be assessed (n = 34 20.35%) included members of large genera in great need of scientific revision (e.g. Ruspolia spp.) and subspecies of questionable validity (e.g. Hetrodes pupus subspp.).

For Red List assessment, CSB first calculated extent of occurrence (EOO) and area of occupancy (AOO) in ArcGIS 9.2 [34] on the basis of collection records stored in MANTIS. Species were then assessed in accordance with IUCN assessment criteria [35] using either Criterion B (geographic range in the form of EOO and/or AOO) or Criterion D (very small or restricted population) into one of six statuses: Critically Endangered (CR), Endangered (EN), Vulnerable (VU), Least Concern (LC), or Data Deficient (DD). Assessment text was written by CSB and PN and all assessments were published by the IUCN in 2014 [33]. DD species (n = 16) were excluded from further analyses.

Katydid scoring and diversity measures

Each species was scored for several traits (Table 1). Threat status (T) was scored a value between 0–3 in ascending order of threat. Distribution (D) was scored from 0–3 by decreasing distribution range size (the narrower the species’ range, the higher its score). Life history (LH) was scored as the sum of two separate scores: mobility (M) was scored from 0–2 in descending order of mobility (e.g. 2 = flightless) and trophic level (Tr) was scored from 0–3 in ascending order of food specialization (e.g. 3 = single host herbivore). Combinations of these elements were summed and their spatial distribution mapped. When all elements were summed, the total maximum score was 9, and the higher this value, the more threatened, endemic, and host specialized the species. This scoring system is similar to the Dragonfly Biotic Index [12, 13] and allows for species traits to be taken into account in diversity analyses. Since species scores were integers which ranged from 0–9, their residuals were not normally distributed (Shapiro Wilk’s W = 0.96, p = 0.001) so species traits were compared among threat categories using Kruskal-Wallis nonparametric tests in R 3.0.2 [36] and Tukey-Kramer-Nemenyi post-hoc tests in package PMCMR [37].

Mapping

South Africa was divided into equal sized grid squares of 1° longitude by 1° latitude in QGIS [38]. This grid cell size divided South Africa into 150 cells, 28 (19%) of which did not contain any katydid collection points. While this is a very coarse scale division, it was the most appropriate for this study because it has been used for similar studies on a global scale for birds [6] and due to the relatively low number of total collecting records in South Africa (N = 1075 records of LC, VU, EN and CR species S1 Table), this division of South Africa resulted in an average of 8.81 ± 0.31 (s.e.) species per grid cell. If we had used smaller grid cells, there would necessarily be fewer collection points per grid cell, compromising the possible analyses of the data. Grid cells were clipped to the coastline, and land area within a grid cell was taken into account in analyses to account for variation in size of cropped grid cells.

Several metrics were calculated per grid cell: total, threatened (number of CR, EN and VU species), and sensitive species richness (number of species with LH score = 3). Endemic species richness was calculated as the number of species in a cell which had EOO < 5000 km 2 . This criteria was selected for three reasons: (1) in the IUCN Red List Criterion B, this is the cut-off for a species to be classified as EN (2) 25.44% of species (29 species all of which are threatened) were included in this classification which is similar to the 25% of species cut-off used by similar studies [6] and (3) there is a natural break in the dataset in that, at EOO < 5000 km 2 , there are much larger gaps between successive EOO values than at EOO > 5000 km 2 (S1 Fig).

Six combinations of the katydid species trait scores were also averaged per grid cell: threat + distribution (T+D) threat + life history (T+LH) distribution + life history (D+LH) threat + distribution + mobility (T+D+M) threat + distribution + trophic level (T+D+Tr) threat + distribution + life history (T+D+LH). The scores for all species present in a grid cell were averaged to give each grid cell a mean value per metric.

Statistical analysis

By species analysis.

We tested for covariance among the species score components by using a phylogenetic least squares analysis (PGLS) in R 3.0.2 [39]. Our data points violated the assumption of independence necessary for linear regression models since we assumed that more closely related species would be more similar in terms of their threat, distribution, and life history traits. In PGLS we first constructed a phylogenetic tree to the species (S2 Fig). Higher taxon (subfamily) relationships were determined according to Mugleston et al. (2013) [40]. For paraphyletic subfamilies (Tettigoniinae, Pseudophyllinae, Mecopodinae and Meconematinae) we did the following: because no subfamily in our study was represented by > 20 species and because all of the representatives in our study appeared similar morphologically, in terms of their tribal assignment, and in terms of their South African distribution, we considered them monophyletic for the purposes of this study. They were placed on the branch of the tree from Mugleston et al. (2013) which corresponded to their closest relative. Since we lacked information on evolutionary relationships within subfamilies, genera and subgenera were assumed to be monophyletic. All species within a subgenus were assigned equal branch lengths, subgenera within a genus were assigned equal branch lengths, and all genera within a subfamily were also assigned equal branch lengths, such that two species from the same subgenus were considered more closely related evolutionarily than two species from different subgenera within the same genus, but no further ranking was assigned at species, subgenus or genus level. All branch lengths were kept equal to one to construct a conservative tree, and the tree was unrooted. The only species which may fall significantly in the wrong place is a Pseudophyllinae species from the coastal forests of the Eastern Cape which has yet to be described, and which appears to be of a different evolutionary origin than other South African members of this subfamily. Within the genus Brinckiella, evolutionary relationships between species pairs B. wilsoni–B. arboricola and B. karooensis–B. mauerbergerorum were assumed on the basis of recent morphological evidence [30].

In PGLS we constructed a series of models to test the relationship of T (dependent variable) with D, LH, M, Tr and their interaction terms (independent variables), and D (dependent) with LH (independent). Ordinary least square models (OLS) and phylogenetic equivalents (PGLS) were constructed for each pair of variables and their strength was compared using Akaike Information Criteria (AIC) to select the best performing model [41]. PGLS models also produced an estimate of phylogenetic covariance (λ), which indicated the strength of the phylogenetic effect [39].

By grid cell analysis.

In order to compare the information provided by each of the diversity measures per grid cell, we constructed a spatial generalized linear mixed effects model (GLMM) in R 3.0.2. We could not calculate traditional pair-wise correlations between the diversity measures because we expected a large degree of spatial autocorrelation which would violate the assumption of independence among the data points (grid cells). We first calculated the degree of spatial autocorrelation in fitted general linear models (function glm in R 3.0.2) of each pair of diversity measures [42]. Moran’s I was calculated using package ncf in R [43]. We then calculated GLMM using the function glmmPQL in package MASS [44] by using Poisson errors with predictor diversity measure and land area within a grid cell as fixed effects and spatial structure modeled as an exponential correlation structure [6, 42]. Estimates of model fit were calculated using marginal r 2 since this is appropriate for models with no random effects [45]. Here, we present results for species richness based diversity measures and for the T+D+LH diversity measure which takes species identity into account. Other combinations of katydid species trait scores are excluded because they are collinear with T+D+LH since they are constructed from individual elements of the full measure.

We then compared overlap of katydid hotspots with South African biodiversity hotspots. We first classified the grid cells according to whether they fell within a biodiversity hotspot or not. We tested four inclusion rules: a grid cell was considered to be within a biodiversity hotspot if > 25% (N = 62, 50.8% of cells), > 50% (N = 57, 46.7% of cells), > 75% (N = 47, 38.5% of cells), or 100% (N = 39, 32.0% of cells) of the area of the cell fell within a biodiversity hotspot. There was no significant difference between the four possible inclusion rules in the difference between the hotspot minus non-hotspot values for any of the diversity measures (Kruskal-Wallis χ 2 3 = 0.22, p = 0.98). Therefore, we chose to use 50% inclusion throughout all analyses as this is conservative but includes enough grid cells to allow for more robust analyses.

All three of the biodiversity hotspots are located along South Africa’s coastline. Sampling density was higher along coastlines (i.e. in the hotspots) than in South Africa’s interior. However, since much of our raw data were derived from historical museum records, it was impossible to know whether this was due to increased sampling along the coastlines due to easier access or whether more specimens were collected along the coastlines because there were more specimens along the coastlines. We compared whether sampling effort was equivalent and sufficient between the hotspot and non-hotspot grid cells using species accumulation curves (SACs) calculated in EstimateS [46]. Hotspot and non-hotspot grid cells were compared for each of the diversity measures using Mann-Whitney non-parametric tests in R 3.0.2.

Frequency histograms were constructed to identify a usable definition of katydid count-based and score-based hotspots. We then ran a series of chi-squared tests in R 3.0.2 to test whether individual grid cells which fell within a katydid count or score-based hotspot were more likely to also fall within a biodiversity hotspot than what would be predicted on the basis of chance alone.


Quantifying the relative irreplaceability of important bird and biodiversity areas

Address for correspondence: Centre for Biodiversity and Conservation Science, Goddard bld 8, The University of Queensland, 4072, Brisbane, Queensland, Australia, email [email protected] Search for more papers by this author

International Union for Conservation of Nature, 28 rue Mauverney, 1196 Gland, Switzerland

World Agroforestry Center (ICRAF), University of the Philippines Los Baños, Laguna, 4031 Philippines

School of Geography and Environmental Studies, University of Tasmania, Hobart TAS, 7001 Australia

International Union for Conservation of Nature, Sheraton House Castle Park, Cambridge, CB3 0AX United Kingdom

BirdLife International, Wellbrook Court, Girton Road, Cambridge, CB3 0NA United Kingdom

Global Mammal Assessment Program, Department of Biology and Biotechnologies, SapienzaUniversità di Roma, viale dell’ Università 32, 00185 Rome, Italy

Durrell Institute of Conservation and Ecology, School of Anthropology and Conservation, University of Kent, Canterbury, CT2 7NR United Kingdom

The Biodiversity Consultancy Ltd, 3E King's Parade, Cambridge, CB2 1SJ United Kingdom

BirdLife International, Wellbrook Court, Girton Road, Cambridge, CB3 0NA United Kingdom

CSIRO Land and Water Flagship, GPO Box 1700, Canberra, ACT, 2601 Australia

Sovon, Dutch Centre for Field Ornithology, P.O. Box 6521, 6503 GA Nijmegen, The Netherlands

European Bird Census Council, P.O. Box 6521, 6503 GA, Nijmegen, The Netherlands

Radboud University, Institute for Water and Wetland Research, Department of Animal Ecology and Ecophysiology, P.O. Box 9100, 6500 GL, Nijmegen, The Netherlands

Microsoft Research, Redmond, Washington, U.S.A

United Nations Environment Programme-World Conservation Monitoring Centre (UNEP-WCMC), 219 Huntingdon Road, CB3 0DL, Cambridge, United Kingdom

ARC Centre of Excellence for Environmental Decisions, Centre for Biodiversity and Conservation Science, The University of Queensland, 4072 Brisbane, Queensland, Australia

Department of Life Sciences, Imperial College London, Buckhurst Road, Ascot, Berkshire, SL5 7PY United Kingdom

Department of Botany, Nelson Mandela Metropolitan University, P.O. Box 77000, Port Elizabeth, 6031 South Africa

National Fish and Wildlife Foundation, Washington, D.C., 20005 U.S.A

School of Life Sciences, Arizona State University, P.O. Box 874601, Tempe, Arizona, 85287-4601 U.S.A

BirdLife International, Wellbrook Court, Girton Road, Cambridge, CB3 0NA United Kingdom

ARC Centre of Excellence for Environmental Decisions, Centre for Biodiversity and Conservation Science, The University of Queensland, 4072 Brisbane, Queensland, Australia

Department of Life Sciences, Imperial College London, Buckhurst Road, Ascot, Berkshire, SL5 7PY United Kingdom

Microsoft Research, Redmond, Washington, U.S.A

Global Conservation Program, Wildlife Conservation Society, 2300 Southern Boulevard, Bronx, New York, 10460 U.S.A

WCPA-SSC Joint Task Force on Biodiversity and Protected Areas, International Union for the Conservation of Nature (IUCN), 64 Juniper Road, Chelsea, Quebec, J9B 1T3 Canada

Global Mammal Assessment Program, Department of Biology and Biotechnologies, SapienzaUniversità di Roma, viale dell’ Università 32, 00185 Rome, Italy

ARC Centre of Excellence for Environmental Decisions, Centre for Biodiversity and Conservation Science, The University of Queensland, 4072 Brisbane, Queensland, Australia

School of Geography, Planning and Environmental Management, The University of Queensland, 4072 Brisbane, Queensland, Australia

Address for correspondence: Centre for Biodiversity and Conservation Science, Goddard bld 8, The University of Queensland, 4072, Brisbane, Queensland, Australia, email [email protected] Search for more papers by this author

International Union for Conservation of Nature, 28 rue Mauverney, 1196 Gland, Switzerland

World Agroforestry Center (ICRAF), University of the Philippines Los Baños, Laguna, 4031 Philippines

School of Geography and Environmental Studies, University of Tasmania, Hobart TAS, 7001 Australia

International Union for Conservation of Nature, Sheraton House Castle Park, Cambridge, CB3 0AX United Kingdom

BirdLife International, Wellbrook Court, Girton Road, Cambridge, CB3 0NA United Kingdom

Global Mammal Assessment Program, Department of Biology and Biotechnologies, SapienzaUniversità di Roma, viale dell’ Università 32, 00185 Rome, Italy

Durrell Institute of Conservation and Ecology, School of Anthropology and Conservation, University of Kent, Canterbury, CT2 7NR United Kingdom

The Biodiversity Consultancy Ltd, 3E King's Parade, Cambridge, CB2 1SJ United Kingdom

BirdLife International, Wellbrook Court, Girton Road, Cambridge, CB3 0NA United Kingdom

CSIRO Land and Water Flagship, GPO Box 1700, Canberra, ACT, 2601 Australia

Sovon, Dutch Centre for Field Ornithology, P.O. Box 6521, 6503 GA Nijmegen, The Netherlands

European Bird Census Council, P.O. Box 6521, 6503 GA, Nijmegen, The Netherlands

Radboud University, Institute for Water and Wetland Research, Department of Animal Ecology and Ecophysiology, P.O. Box 9100, 6500 GL, Nijmegen, The Netherlands

Microsoft Research, Redmond, Washington, U.S.A

United Nations Environment Programme-World Conservation Monitoring Centre (UNEP-WCMC), 219 Huntingdon Road, CB3 0DL, Cambridge, United Kingdom

ARC Centre of Excellence for Environmental Decisions, Centre for Biodiversity and Conservation Science, The University of Queensland, 4072 Brisbane, Queensland, Australia

Department of Life Sciences, Imperial College London, Buckhurst Road, Ascot, Berkshire, SL5 7PY United Kingdom

Department of Botany, Nelson Mandela Metropolitan University, P.O. Box 77000, Port Elizabeth, 6031 South Africa

National Fish and Wildlife Foundation, Washington, D.C., 20005 U.S.A

School of Life Sciences, Arizona State University, P.O. Box 874601, Tempe, Arizona, 85287-4601 U.S.A

BirdLife International, Wellbrook Court, Girton Road, Cambridge, CB3 0NA United Kingdom

ARC Centre of Excellence for Environmental Decisions, Centre for Biodiversity and Conservation Science, The University of Queensland, 4072 Brisbane, Queensland, Australia

Department of Life Sciences, Imperial College London, Buckhurst Road, Ascot, Berkshire, SL5 7PY United Kingdom

Microsoft Research, Redmond, Washington, U.S.A

Global Conservation Program, Wildlife Conservation Society, 2300 Southern Boulevard, Bronx, New York, 10460 U.S.A

WCPA-SSC Joint Task Force on Biodiversity and Protected Areas, International Union for the Conservation of Nature (IUCN), 64 Juniper Road, Chelsea, Quebec, J9B 1T3 Canada

Abstract

World governments have committed to increase the global protected areas coverage by 2020, but the effectiveness of this commitment for protecting biodiversity depends on where new protected areas are located. Threshold- and complementarity-based approaches have been independently used to identify important sites for biodiversity. We brought together these approaches by performing a complementarity-based analysis of irreplaceability in important bird and biodiversity areas (IBAs), which are sites identified using a threshold-based approach. We determined whether irreplaceability values are higher inside than outside IBAs and whether any observed difference depends on known characteristics of the IBAs. We focused on 3 regions with comprehensive IBA inventories and bird distribution atlases: Australia, southern Africa, and Europe. Irreplaceability values were significantly higher inside than outside IBAs, although differences were much smaller in Europe than elsewhere. Higher irreplaceability values in IBAs were associated with the presence and number of restricted-range species number of criteria under which the site was identified and mean geographic range size of the species for which the site was identified (trigger species). In addition, IBAs were characterized by higher irreplaceability values when using proportional species representation targets, rather than fixed targets. There were broadly comparable results when measuring irreplaceability for trigger species and when considering all bird species, which indicates a good surrogacy effect of the former. Recently, the International Union for Conservation of Nature has convened a consultation to consolidate global standards for the identification of key biodiversity areas (KBAs), building from existing approaches such as IBAs. Our results informed this consultation, and in particular a proposed irreplaceability criterion that will allow the new KBA standard to draw on the strengths of both threshold- and complementarity-based approaches.

Abstract

Cuantificación del Carácter Relativamente Irreemplazable de las Áreas Importantes para la Conservación de Aves

Resumen

Los gobiernos del mundo se han comprometido a incrementar la cobertura de las áreas protegidas para el año 2020, pero la efectividad de este compromiso con la protección de la biodiversidad depende de la ubicación de las nuevas áreas protegidas. Las estrategias basadas en umbrales y en la complementareidad se han utilizado independientemente para identificar sitios importantes para la biodiversidad. Juntamos estas estrategias al realizar un análisis basado en la complementareidad del carácter irremplazable de las áreas importantes para la conservación de aves (AICA), que son sitios identificados con el uso de una estrategia basada en umbrales. Determinamos si los valores de irreemplazabilidad son más altos dentro que fuera de una AICA y si alguna diferencia observada depende de las características conocidas del área. Nos enfocamos en tres regiones con inventarios completos de AICA y en atlas de distribución de aves: Australia, el sur de África y Europa. Los valores de irreemplazabilidad fueron significativamente más altos dentro de las AICA que fuera, aunque las diferencias fueron mucho menores en Europa que en otro lado. Los valores más altos de irreemplazabilidad en las AICA estuvieron asociados con la presencia y el número de especies con extensión restringida el número de criterios bajo los que se identificó el sitio y el tamaño promedio de la extensión geográfica de las especies para las cuales se identificó el sitio (especies de activación). Además, las AICA se caracterizaron por valores de irreemplazabilidad más altos cuando se usaron objetivos de representación proporcional de especies en lugar de objetivos fijos. Hubo resultados comparables de manera general cuando se midió la irreemplazabilidad de las especies de activación y cuando se consideró a todas las especies de aves, lo que indica un buen efecto de sustitución de las anteriores. La Unión Internacional para la Conservación de la Naturaleza convocó recientemente a una consulta para consolidar los estándares globales para la identificación de áreas clave de biodiversidad (ACB), a partir de las estrategias existentes como las AICA. Nuestros resultados brindaron información a esta consulta y en particular a la propuesta de un criterio de irreemplazabilidad que permitirá al nuevo estándar de las ACB recurrir a las fortalezas de las estrategias basadas en umbrales y de las basadas en la complementareidad.


Acknowledgements

We thank the authors of the original data sets, both published and unpublished, who shared data with us. We thank the National Center for Ecological Analysis and Synthesis (NCEAS) for funding the workshop “Biodiversity and the Functioning of Ecosystems: Translating Results from Model Experiments into Functional Reality”. Support for NCEAS comes from University of California Santa Barbara and the National Science Foundation. J.E.K.B., E.C.A. and M.I.O. had NCEAS post-doctoral fellowships, J.E.D. had support from NSF OCE-1031061 B.J.C. had support from NSF DEB-1046121 L.G. was supported by grant 621-2009-5457 from the Swedish Research Council VR A.G. is supported by the Canada Research Chair Program and NSERC.


Open Research

The data and R code used to produce the main results presented in the article, in addition to the link to an online interactive map displaying the location of the plots, are available at: https://dx.doi.org/10.6084/m9.figshare.14191268

Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.


Watch the video: Calculate diversity indices and species richness with DIVERSITY function using R package VEGAN (May 2022).


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