Threats of land use to the global diversity of vascular plants

Aim

Land use is a main driver of biodiversity loss worldwide. However, quantifying its effects on global plant diversity remains a challenge due to the limited availability of data on the distributions of vascular plant species and their responses to land use. Here, we estimated the global extinction threat of land use to vascular plant species based on a novel integration of an ecoregion-level species-area model and the relative endemism richness of the ecoregions.

Location

Global.

Methods

First, we assessed ecoregion-level extinction threats using a countryside species–area relationship model based on responses of local plant richness to land use types and intensities and a high-resolution global land use map. Next, we estimated global species extinction threat by multiplying the relative endemism richness of each ecoregion with the ecoregion-level extinction threats.

Results

Our results indicate that 11% of vascular plant species are threatened with global extinction. We found the largest extinction threats in the Neotropic and Palearctic realms, mainly due to cropland of minimal and high intensity, respectively.

Main Conclusions

Our novel integration of the countryside species–area relationship and the relative endemism richness allows for the identification of hotspots of global extinction threat, as well as the contribution of specific land use types and intensities to this threat. Our findings inform where the development of measures to protect or restore plant diversity globally are most needed.     

Species richness, environmental heterogeneity and area: a case study based on land snails in Skyros archipelago (Aegean Sea, Greece)

Aim To test the performance of the choros model in an archipelago using two measures of environmental heterogeneity. The choros model is a simple, easy‐to‐use mathematical relationship which approaches species richness as a combined function of area and environmental heterogeneity. Location The archipelago of Skyros in the central Aegean Sea (Greece). Methods We surveyed land snails on 12 islands of the archipelago. We informed the choros model with habitat data based on natural history information from the land snail species assemblage. We contrast this with habitat information taken from traditional vegetation classification to study the behaviour of choros with different measures of environmental heterogeneity. R² values and Akaike's information criterion (AIC) were used to compare the choros model and the Arrhenius species–area model. Path analysis was used to evaluate the variance in species richness explained by area and habitat diversity. Results Forty‐two land snail species were recorded, living in 33 different habitat types. The choros model with habitat types had more explanatory power than the classic species–area model and the choros model using vegetation types. This was true for all islands of the archipelago, as well as for the small islands alone. Combined effects of area and habitat diversity primarily explain species richness in the archipelago, but there is a decline when only small islands are considered. The effects of area are very low both for all the islands of the archipelago, and for the small islands alone. The variance explained by habitat diversity is low for the island group as a whole, but significantly increases for the small islands. Main conclusions The choros model is effective in describing species‐richness patterns of land snails in the Skyros Archipelago, incorporating ecologically relevant information on habitat occupancy and area. The choros model is more effective in explaining richness patterns on small islands. When using traditional vegetation types, the choros model performs worse than the classic species–area relationship, indicating that use of proxies for habitat diversity may be problematic. The slopes for choros and Arrhenius models both assert that, for land snails, the Skyros Archipelago is a portion of a larger biogeographical province. The choros model, informed by ecologically relevant habitat measures, in conjunction with path analysis points to the importance of habitat diversity in island species richness.     

Predicting biodiversity loss in island and countryside ecosystems through the lens of taxonomic and functional biogeography

We investigate how variation in patch area and forest cover quantified for three different spatial scales (buffer size of 500, 1500 and 3000 m radius) affects species richness and functional diversity of bat assemblages in two ecosystems differing in fragment–matrix contrast: a landbridge island system in Panama and a countryside ecosystem in the Brazilian Amazon. Bats were sampled on 11 islands and the adjacent mainland in Panama, and in eight forest fragments and nearby continuous forest in Brazil. Species–area relationships (SAR) were assessed based on Chao1 species richness estimates, and functional diversity–area relationships (FAR) were quantified using Chao1 functional diversity estimates measured as the total branch length of a trait dendrogram. FARs were calculated using three trait sets: considering five species functional traits (FAR_ALL), and trait subsets reflecting ‘diet breadth’ (FAR_DIET) and ‘dispersal ability’ (FAR_DISPERSAL). We found that in both study systems, FAR_ALL was less sensitive to habitat loss than SAR, in the sense that an equal reduction in habitat loss led to a disproportionately smaller loss of functional diversity compared to species richness. However, the inhospitable and static aquatic matrix in the island ecosystem resulted in more pronounced species loss with increasing loss of habitat compared to the countryside ecosystem. Moreover, while we found a significant FAR_DISPERSAL for the island ecosystem in relation to forest cover within 500 m landscape buffers, FAR_DIET and FAR_DISPERSAL were not significant for the countryside ecosystem. Our findings highlight that species richness and functional diversity in island and countryside ecosystems scale fundamentally differently with habitat loss, and suggest that key bat ecological functions, such as pollination, seed dispersal and arthropod suppression, may be maintained in fragments despite a reduction in species richness. Our study reinforces the importance of increasing habitat availability for decreasing the chances of losing species richness in smaller fragments.     

Patterns of species richness on very small islands: the plants of the Aegean archipelago

Aim To investigate the species–area relationship (SAR) of plants on very small islands, to examine the effect of other factors on species richness, and to check for a possible Small Island Effect (SIE). Location The study used data on the floral composition of 86 very small islands (all < 0.050 km²) of the Aegean archipelago (Greece). Methods We used standard techniques for linear and nonlinear regression in order to check several models of the SAR, and stepwise multiple regression to check for the effects of factors other than area on species richness (‘habitat diversity’, elevation, and distance from nearest large island), as well as the performance of the Choros model. We also checked for the SAR of certain taxonomic and ecological plant groups that are of special importance in eastern Mediterranean islands, such as halophytes, therophytes, Leguminosae and Gramineae. We used one‐way anova to check for differences in richness between grazed and non‐grazed islands, and we explored possible effects of nesting seabirds on the islands’ flora. Results Area explained a small percentage of total species richness variance in all cases. The linearized power model of the SAR provided the best fit for the total species list and several subgroups of species, while the semi‐log model provided better fits for grazed islands, grasses and therophytes. None of the nonlinear models explained more variance. The slope of the SAR was very high, mainly due to the contribution of non‐grazed islands. No significant SIE could be detected. The Choros model explained more variance than all SARs, although a large amount of variance of species richness still remained unexplained. Elevation was found to be the only important factor, other than area, to influence species richness. Habitat diversity did not seem important, although there were serious methodological problems in properly defining it, especially for plants. Grazing was an important factor influencing the flora of small islands. Grazed islands were richer than non‐grazed, but the response of their species richness to area was particularly low, indicating decreased floral heterogeneity among islands. We did not detect any important effects of the presence of nesting seabird colonies. Main conclusions Species richness on small islands may behave idiosyncratically, but this does not always lead to a typical SIE. Plants of Aegean islets conform to the classical Arrhenius model of the SAR, a result mainly due to the contribution of non‐grazed islands. At the same time, the factors examined explain a small portion of total variance in species richness, indicating the possible contribution of other, non‐standard factors, or even of stochastic effects. The proper definition of habitat diversity as pertaining to the taxon examined in each case is a recurrent problem in such studies. Nevertheless, the combined effect of area and a proxy for environmental heterogeneity is once again superior to area alone in explaining species richness.     

Re‐approaching the small island effect

Aim To propose a new approach to the small island effect (SIE) and a simple mathematical procedure for the estimation of its upper limit. The main feature of the SIE is that below an upper size threshold an increase of species number with increase of area in small islands is not observed. Location Species richness patterns from different taxa and insular systems are analysed. Methods Sixteen different data sets from 12 studies are analysed. Path analysis was used for the estimation of the upper limit of the SIE. We studied each data set in order to detect whether there was a certain island size under which the direct effects of area were eliminated. This detection was carried out through the sequential exclusion of islands from the largest to the smallest. For the cases where an SIE was detected, a log‐log plot of species number against area is presented. The relationships between habitat diversity, species number and area are studied within the limits of the SIE. In previous studies only area was used for the detection of the SIE, whereas we also encompass habitat diversity, a parameter with well documented influence on species richness, especially at small scales. Results An SIE was detected in six out of the 16 studied cases. The upper limit of the SIE varies, depending on the characteristics of the taxon and the archipelago under study. In general, the values of the upper limit of the SIE calculated according to the approach undertaken in our study differ from the values calculated in previous studies. Main conclusions Although the classical species–area models have been used to estimate the upper limit of the SIE, we propose that the detection of this phenomenon should be undertaken independently from the species–area relationship, so that the net effects of area are calculated excluding the surrogate action of area on other variables, such as environmental heterogeneity. The SIE appears when and where area ceases to influence species richness directly. There are two distinct SIE patterns: (1) the classical SIE where both the direct and indirect effects of area are eliminated and (2) the cryptic SIE where area affects species richness indirectly. Our approach offers the opportunity of studying the different factors influencing biodiversity on small scales more accurately. The SIE cannot be considered a general pattern with fixed behaviour that can be described by the same model for different island groups and taxa. The SIE should be recognized as a genuine but idiosyncratic phenomenon.     

The role of habitat diversity in generating the small-island effect

The small-island effect (SIE) has become a widespread pattern in island biogeography and biodiversity research. However, in most previous studies only area is used for the detection of the SIE, while other causal factors such as habitat diversity is rarely considered. Therefore, the role of habitat diversity in generating SIEs is poorly known. Here, we compiled 86 global datasets that included the variables of habitat diversity, area and species richness to systematically investigate the prevalence and underlying factors determining the role of habitat diversity in generating SIEs. For each dataset, we used both path analysis and breakpoint regressions to identify the existence of an SIE. We collected a number of system characteristics and employed logistic regression models and an information–theoretic approach to determine which combination of variables was important in determining the role of habitat diversity in generating SIEs. Among the 61 datasets with adequate fits, habitat diversity was found to influence the detection of SIEs in 32 cases (52.5%) when using path analysis. By contrast, SIEs were detected in 26 of 61 cases (42.6%) using breakpoint regressions. Model selection and model-averaged parameter estimates showed that Number of sites, Habitat range and Species range were three key variables that determined the role of habitat diversity in generating SIEs. However, Area range, Taxon group and Site type received considerably less support. Our study demonstrates that the effect of habitat diversity on generating SIEs is quite prevalent. The inclusion of habitat diversity is important because it provides a causal factor for the detection of SIEs. We conclude that for a better understanding of the causes of SIEs, habitat diversity should be included in future studies.     

Small area and low connectivity constrain the diversity of plant life strategies in temporary ponds

Aim

(i) To determine whether area and connectivity of temporary ponds can predict plant species diversity, and the diversity and abundance of different plant life histories; (ii) To explore whether pond connectivity with the river prior to river regulation predicts better plant diversity patterns than current pond connectivity, suggestive of possible effects of connectivity loss.

Location

Eastern Carpathian Mountains, Romania, Europe.

Methods

We fitted linear and generalized linear models (LM and GLM) to examine whether pond area and current distance from the Olt River predict plant species richness, Shannon diversity and relative cover of different social behaviour types and overall plant species richness and Shannon diversity. Using historical maps, we measured pond distance from the river ca. 60 years before the Olt River was regulated, and we refitted the LM and GLM models using pond area and past distance from the river as independent variables.

Results

Total plant species richness increased with pond area, and it decreased with the distance from the river, but total plant Shannon diversity index was affected, positively, only by pond area. The strength of responses to pond area and connectivity of species richness, Shannon diversity and relative cover varied across the different social behaviour types. Past and current distances between ponds and riverbeds had similar effects on plant diversity, with some evidence for stronger effect of the present connectivity on specialist species Shannon diversity and a weaker effect on disturbance tolerants, generalists and competitors.

Main Conclusions

Pond area and connectivity with the landscape are important predictors of the diversity of plant life history strategies, and therefore, useful tools in pond conservation. Consistent species richness and Shannon diversity responses of wetland specialists to pond area and connectivity make this life history type well suited for monitoring pond condition.     

Towards a more general species–area relationship: diversity on all islands, great and small

Aim

To demonstrate a new and more general model of the species–area relationship that builds on traditional models, but includes the provision that richness may vary independently of island area on relatively small islands (the small island effect).

Location

We analysed species–area patterns for a broad diversity of insular biotas from aquatic and terrestrial archipelagoes.

Methods

We used breakpoint or piecewise regression methods by adding an additional term (the breakpoint transformation) to traditional species–area models. The resultant, more general, species–area model has three readily interpretable, biologically relevant parameters: (1) the upper limit of the small island effect (SIE), (2) an estimate of richness for relatively small islands and (3) the slope of the species–area relationship (in semi‐log or log–log space) for relatively large islands.

Results

The SIE, albeit of varying magnitude depending on the biotas in question, appeared to be a relatively common feature of the data sets we studied. The upper limit of the SIE tended to be highest for species groups with relatively high resource requirements and low dispersal abilities, and for biotas of more isolated archipelagoes.

Main conclusions

The breakpoint species–area model can be used to test for the significance, and to explore patterns of variation in small island effects, and to estimate slopes of the species–area (semi‐log or log–log) relationship after adjusting for SIE. Moreover, the breakpoint species–area model can be expanded to investigate three fundamentally different realms of the species–area relationship: (1) small islands where species richness varies independent of area, but with idiosyncratic differences among islands and with catastrophic events such as hurricanes, (2) islands beyond the upper limit of SIE where richness varies in a more deterministic and predictable manner with island area and associated, ecological factors and (3) islands large enough to provide the internal geographical isolation (large rivers, mountains and other barriers within islands) necessary for in situ speciation.

     

Island biogeography of bats in Baja California, Mexico: patterns of bat species richness in a near-shore archipelago

Aim We studied the relationship between the size and isolation of islands and bat species richness in a near‐shore archipelago to determine whether communities of vagile mammals conform to predictions of island biogeography theory. We compared patterns of species richness in two subarchipelagos to determine whether area per se or differences in habitat diversity explain variations in bat species richness. Location Islands in the Gulf of California and adjacent coastal habitats on the Baja California peninsula in northwest Mexico. Methods Presence–absence surveys for bats were conducted on 32 islands in the Gulf of California using acoustic and mist‐net surveys. We sampled for bats in coastal habitats of four regions of the Baja peninsula to characterize the source pool of potential colonizing species. We fitted a semi‐log model of species richness and multiple linear regression and used Akaike information criterion model selection to assess the possible influence of log₁₀ area, isolation, and island group (two subarchipelagos) on the species richness of bats. We compared the species richness of bats on islands with greater vegetation densities in the southern gulf (n = 20) with that on drier islands with less vegetation in the northern gulf (n = 12) to investigate the relationship between habitat diversity and the species richness of bats. Results Twelve species of bats were detected on islands in the Gulf of California, and 15 species were detected in coastal habitats on the Baja peninsula. Bat species richness was related to both area and isolation of islands, and was higher in the southern subarchipelago, which has denser vegetation. Log₁₀ area was positively related to bat species richness, which increased by one species for every 5.4‐fold increase in island area. On average, richness declined by one species per 6.25 km increase in isolation from the Baja peninsula. Main conclusions Our results demonstrate that patterns of bat species richness in a near‐shore archipelago are consistent with patterns predicted by the equilibrium theory of island biogeography. Despite their vagility, bats may be more sensitive to moderate levels of isolation than previously expected in near‐shore archipelagos. Differences in vegetation and habitat xericity appear to be associated with richness of bat communities in this desert ecosystem. Although observed patterns of species richness were consistent with those predicted by the equilibrium theory, similar relationships between species richness and size and isolation of islands may arise from patch‐use decision making by individuals (optimal foraging strategies).     

Global diversity of island floras from a macroecological perspective

Islands harbour a significant portion of all plant species worldwide. Their biota are often characterized by narrow distributions and are particularly susceptible to biological invasions and climate change. To date, the global richness pattern of islands is only poorly documented and factors causing differences in species numbers remain controversial. Here, we present the first global analysis of 488 island and 970 mainland floras. We test the relationship between island characteristics (area, isolation, topography, climate and geology) and species richness using traditional and spatial models. Area is the strongest determinant of island species numbers ( R ² = 0.66) but a weaker predictor for mainlands ( R ² = 0.25). Multivariate analyses reveal that all investigated variables significantly contribute to insular species richness with area being the strongest followed by isolation, temperature and precipitation with about equally strong effects. Elevation and island geology show relatively weak yet significant effects. Together these variables account for 85% of the global variation in species richness.     

How,and how much,natural cover loss increases species richness

Aim The species–area relationship has been applied in the conservation context to predict monotonic species richness declines as natural area is converted to human‐dominated land covers. However, some conversion of natural cover could introduce new habitat types and allow new open habitat species to occur. Moreover, decelerating richness–area relationships suggest that, as natural area is converted to human‐dominated covers, more species will be added to the rare habitat than are lost from the common one. Area effects and increased habitat diversity could each lead to a peaked relationship between species richness and the relative amount of natural area. The purpose of this study is to quantify the effect on avian species richness of conversion of natural area to human‐dominated land cover. Location Ontario, Canada. Methods We evaluated the responses of total avian richness, forest bird richness and open habitat bird richness to remaining natural area within 993 quadrats, each of 100 km². We quantified the amount of natural land cover and land‐cover heterogeneity using remote sensing data. We used structural equation modelling (SEM) to disentangle the relationships among avian richness, natural area and land‐cover heterogeneity. Results Spatial variation in avian richness was a peaked function of remaining natural area, such that losses of up to 44% of the natural area increased avian richness. This partly reflects increased variety of land cover; however, SEM suggests that much of the increase in richness is due to pure area effects. Richness of forest species declined by two species over this range of natural cover loss while open habitat bird richness increased by approximately 20 species. The effect of natural area on species richness is consistent with the sum of species–area curves for natural habitat species and human‐dominated habitat species. Main conclusions At least in northern temperate forests, almost half of the natural land cover can be converted to human‐dominated forms before avian richness declines. Conversion of < 50% of regional natural area to human‐dominated land cover can benefit open‐area species richness with relatively few losses of forest obligate species. However, with > 50% natural area conversion, species begin to drop out of regional assemblages.