Single Species as Indicators of Species Richness and Composition in California Coastal Sage Scrub Birds and Small Mammals

Abstract: Individual species may be useful as indicators of biodiversity if an association exists between the presence of a species and another component of biodiversity. We evaluated 40 species of birds and small mammals, including 11 species of conservation concern, as potential indicators of species richness and species composition in southern California coastal sage scrub habitats. This habitat, which is the target of large-scale conservation planning, has been greatly reduced by human development and supports many plants and animals of conservation concern. We asked whether there is an association between the presence of a potential indicator species and the species richness and composition of the bird or small-mammal community in which it is found. We used point counts and live-trapping to quantify the distribution of birds and small mammals, respectively, at 155 points in 16 sites located in three counties. Of the few species we found associated with species richness, some were associated with higher species richness and others with lower richness, and species of conservation concern were not more frequently associated with species richness than were common species. Ordination analysis revealed a geographic gradient in coastal sage scrub bird and small-mammal species composition across southern California, and 18 of the species we evaluated were associated with the composition of the bird and small-mammal community in which they were found. Our results suggest that efforts to conserve bird and small-mammal biodiversity in coastal sage scrub should not focus exclusively on rare species or on locations with the highest species richness, but instead should focus on a diverse suite of species that are representative of the range of variation in communities found in coastal sage scrub habitats.     

Cost-Efficiency of Decaying Wood as a Surrogate for Overall Species Richness in Boreal Forests

Abstract:  Decaying wood is one of the most important elements for species richness in boreal forests. We tested how well reserve selection based on the amount and quality of decaying wood results in a representation of four ecologically different taxa (beetles, birds, wood-inhabiting fungi, and vascular plants). We also compared the cost-efficiency of the use of dead-wood indicators with comprehensive species inventory. Our database included 32 seminatural old-forest stands located in northern Finland. Decaying wood was a relatively good indicator of saproxylic species but not overall species richness. Even though dead wood did not reflect accurately overall species richness, our results indicated that the use of decaying wood as an indicator in site selection was more cost-efficient than using information from large-scale species inventories. Thus, decaying wood is a valuable surrogate for species richness, but other cost-efficient indicators that reflect the requirements of those species which are not dependent on decaying wood should be identified.     

A Mechanistic Approach to Evaluation of Umbrella Species as Conservation Surrogates

Abstract:  Although species with large area requirements are sometimes used as umbrella species, their general utility as conservation tools is uncertain. We surveyed the species diversity of birds, butterflies, carabids, and forest-floor plants in forest sites across an area (1600 km²) in which we delineated large breeding home ranges of Northern Goshawk ( Accipiter gentilis ). We tested whether protection of the home ranges could serve as an effective umbrella to protect sympatric species of the four taxa. We also used an empirical habitat model of occupancy of home range to examine mechanisms by which the Northern Goshawk acts as an umbrella species. Among species richness, abundance, and species composition of the four taxa, only abundance and species composition of birds differed between sites located inside and outside home ranges, which was due to greater abundance of bird species that were prey of Northern Goshawks inside the home ranges. Thus, although home range indicated areas with high abundance of certain bird prey species, it was not effective as an indicator of the species diversity of all four taxa. We also did not find any difference in species richness, abundance, and species composition between sites predicted as occupied and unoccupied using the habitat model. In contrast, when we selected sites on the basis of each habitat variable in the model, habitat variables that selected sites either in agricultural or forested landscapes encompassed sites with high species richness or particular species composition. This result suggests that the low performance of the Northern Goshawk as an umbrella species is due to this species' preference for habitat in both agricultural and forested landscapes. Species that can adjust to changes in habitat conditions may not act as effective umbrella species despite having large home ranges.     

Ecological Uses of Vertebrate Indicator Species: A Critique

Abstract: Plant and animal species have been used for decades as indicators of air and water quality and agricultural and range conditions. Increasingly, vertebrates are used to assess population trends and habitat quality for other species. In this paper we review the conceptual bases, assumptions, and published guidelines for selection and use of vertebrates as ecological indicators. We conclude that an absence of precise definitions and procedures, confounded criteria used to select species, and discordance with ecological literature severely weaken the effectiveness and credibility of using vertebrates as ecological indicators. In many cases the use of ecological indicator species is inappropriate, but when necessary, the following recommendations will make their use more rigorous: (1) clearly state assessment goals, (2) use indicators only when other assessment options are unavailable, (3) choose indicator species by explicitly defined criteria that are in accord with assessment goals, (4) include all species that fulfill stated selection criteria (5) know the biology of the indicator in detail, and treat the indicator as a formal estimator in conceptual and statistical models, (6) identify and define sources of subjectivity when selecting monitoring and intetpreting indicator species, (7) submit assessment design, methods of data collection and statistical analysis, interpretations, and recommendations to peer review and (8) direct research at developing an overall strategy for monitoring wildlife that accounts for natural variability in population attributes and incorporates concepts from landscape ecology.     

New approaches to modelling fish-habitat relationships

Ecologists often develop models that describe the relationship between faunal communities and their habitat. Coral reef fishes have been the focus of numerous such studies, which have used a wide range of statistical tools to answer an equally wide range of questions. Here, we apply a series of both conventional statistical techniques (linear and generalized additive regression models) and novel machine-learning techniques (the support vector machine and three ensemble techniques used with regression trees) to predict fish species richness, biomass, and diversity from a range of habitat variables. We compare the techniques in terms of their predictive performance, and we compare a subset of the models in terms of the influence each habitat variable has for the predictions. Prediction errors are estimated by cross-validation, and variable importance is assessed using permutations of individual variable values. For predictions of species richness and diversity the tree-based models generally and the random forest model specifically are superior (produce the lowest errors). These model types are all able to model both nonlinear and interaction effects. The linear model, unable to model either effect type, performs the worst (produces the highest errors). For predictions of biomass, the generalized additive model is superior, and the support vector machine performs the worst. Depth range, the difference between maximum and minimum water depth at a given site, is identified as the most important variable in the majority of models predicting the three fish community variables. However, variable importance is highly dependent upon model type, which leads to questions regarding the interpretation of variable importance and its proper use as an indicator of causality. The representation of ecological relationships by tree-based ensemble learners will improve predictive performance, and provide a new avenue for exploring ecological relationships, both statistical and causal.     

Using Niche-Based Models to Improve the Sampling of Rare Species

Abstract:  Because data on rare species usually are sparse, it is important to have efficient ways to sample additional data. Traditional sampling approaches are of limited value for rare species because a very large proportion of randomly chosen sampling sites are unlikely to shelter the species. For these species, spatial predictions from niche-based distribution models can be used to stratify the sampling and increase sampling efficiency. New data sampled are then used to improve the initial model. Applying this approach repeatedly is an adaptive process that may allow increasing the number of new occurrences found. We illustrate the approach with a case study of a rare and endangered plant species in Switzerland and a simulation experiment. Our field survey confirmed that the method helps in the discovery of new populations of the target species in remote areas where the predicted habitat suitability is high. In our simulations the model-based approach provided a significant improvement (by a factor of 1.8 to 4 times, depending on the measure) over simple random sampling. In terms of cost this approach may save up to 70% of the time spent in the field.     

Woody plant richness and NDVI response to drought events in Catalonian (northeastern Spain) forests

The role of species diversity on ecosystem resistance in the face of strong environmental fluctuations has been addressed from both theoretical and experimental viewpoints to reveal a variety of positive and negative relationships. Here we explore empirically the relationship between the richness of forest woody species and canopy resistance to extreme drought episodes. We compare richness data from an extensive forest inventory to a temporal series of satellite imagery that estimated drought impact on forest canopy as NDVI (normalized difference vegetation index) anomalies of the dry summer in 2003 in relation to records of previous years. We considered five different types of forests that are representative of the main climatic and altitudinal gradients of the region, ranging from lowland Mediterranean to mountain boreal-temperate climates. The observed relationship differed among forest types and interacted with the climate, summarised by the Thorntwaite index. In Mediterranean Pinus halepensis forests, NDVI decreased during the drought. This decrease was stronger in forests with lower richness. In Mediterranean evergreen forests of Quercus ilex, drought did not result in an overall NDVI loss, but lower NDVI values were observed in drier localities with lower richness, and in more moist localities with higher number of species. In mountain Pinus sylvestris forests NDVI decreased, mostly due to the drought impact on drier localities, while no relation to species richness was observed. In moist Fagus sylvatica forests, NDVI only decreased in plots with high richness. No effect of drought was observed in the high mountain Pinus uncinata forests. Our results show that a shift on the diversity-stability relationship appears across the regional, climatic gradient. A positive relationship appears in drier localities, supporting a null model where the probability of finding a species able to cope with drier conditions increases with the number of species. However, in more moist localities we hypothesize that the proportion of drought-sensitive species would increase in richer localities, due to a higher likelihood of co-occurrence of species that share moist climatic requirements. The study points to the convenience of considering the causes of disturbance in relation to current environmental gradients and historical environmental constraints on the community.     

Modeling and Predicting Species Occurrence Using Broad-Scale Environmental Variables: an Example with Butterflies of the Great Basin

Abstract: If occurrence of individual species can be modeled as a function of easily quantified environmental variables (e.g., derived from a geographic information system [GIS]) and the predictions of these models are demonstrably successful, then the scientific foundation for management planning will be strengthened. We used Bayesian logistic regression to develop predictive models for resident butterflies in the central Great Basin of western North America. Species inventory data and values for 14 environmental variables from 49 locations (segments of canyons) in the Toquima Range ( Nevada, U.S.A.) were used to build the models. Squares of the environmental variables were also used to accommodate possibly nonmonotonic responses. We obtained statistically significant models for 36 of 56 (64%) resident species of butterflies. The models explained 8–72% of the deviance in occurrence of those species. Each of the independent variables was significant in at least one model, and squared versions of five variables contributed to models. Elevation was included in more than half of the models. Models included one to four variables; only one variable was significant in about half the models. We conducted preliminary tests of two of our models by using an existing set of data on the occurrence of butterflies in the neighboring Toiyabe Range. We compared conventional logistic classification with posterior probability distributions derived from Bayesian modeling. For the latter, we restricted our predictions to locations with a high ( 70%) probability of predicted presence or absence. We will perform further tests after conducting inventories at new locations in the Toquima Range and nearby Shoshone Mountains, for which we have computed environmental variables by using remotely acquired topographic data, digital-terrain and microclimatic models, and GIS computation.     

Spatial predictions of phylogenetic diversity in conservation decision making

Considering genetic relatedness among species has long been argued as an important step toward measuring biological diversity more accurately, rather than relying solely on species richness. Some researchers have correlated measures of phylogenetic diversity and species richness across a series of sites and suggest that values of phylogenetic diversity do not differ enough from those of species richness to justify their inclusion in conservation planning. We compared predictions of species richness and 10 measures of phylogenetic diversity by creating distribution models for 168 individual species of a species-rich plant family, the Cape Proteaceae. When we used average amounts of land set aside for conservation to compare areas selected on the basis of species richness with areas selected on the basis of phylogenetic diversity, correlations between species richness and different measures of phylogenetic diversity varied considerably. Correlations between species richness and measures that were based on the length of phylogenetic tree branches and tree shape were weaker than those that were based on tree shape alone. Elevation explained up to 31% of the segregation of species rich versus phylogenetically rich areas. Given these results, the increased availability of molecular data, and the known ecological effect of phylogenetically rich communities, consideration of phylogenetic diversity in conservation decision making may be feasible and informative.     

Bat Diversity and Abundance as Indicators of Disturbance in Neotropical Rainforests

Abstract: Evaluating the degree of disturbance of any region to determine its relative importance for conservation purposes requires procedures that are relatively inexpensive and that yield accurate results fast. Because bats are abundant, diverse, and easy to sample, especially in the Neotropical rainforest, they fulfill several of the requirements of indicator species as identified in the literature. For 10 months we sampled bat communities in the Selva Lacandona in Chiapas, Mexico, at 15 sites representing five habitats. We also measured 10 variables representing vegetation structure and diversity at each site. With fuzzy-set techniques we produced a gradient classification of disturbance for the 15 sites based on the vegetation data. We explored the relationship between vegetation conditions, described as the membership degrees in the construct "fuzzy forest set" (the complementary fuzzy set of "disturbance"), and four bat community variables. Bat species richness, number of rare bat species, and the bat diversity index were positively correlated with the vegetation scores, and relative abundance of the most abundant bat species was negatively correlated with vegetation scores. A high number of phyllostomine species in a community is a good indicator of low levels of disturbance. Although a single indicator group will probably not be sufficient for decision-making processes in conservation, evaluating bat populations may be a good first step in assessing an area's conservation value, especially in rainforest regions.     

Spatial heterogeneity influences native and nonnative plant species richness

Spatial heterogeneity may have differential effects on the distribution of native and nonnative plant species richness. We examined the effects of spatial heterogeneity on native and nonnative plant species richness distributions in the central part of Rocky Mountain National Park, Colorado, USA. Spatial heterogeneity around vegetation plots was characterized using landscape metrics, environmental/topographic variables (slope, aspect, elevation, and distance from stream or river), and soil variables (nitrogen, clay, and sand). The landscape metrics represented five components of landscape heterogeneity and were measured at four spatial extents (within varying radii of 120, 240, 480, and 960 m) using the FRAGSTATS landscape pattern analysis program. Akaike's Information Criterion adjusted for small sample size (AICc) was used to select the best models from a set of multiple linear regression models developed for native and nonnative plant species richness at four spatial extents and three levels of ecological hierarchy (i.e., landscape, land cover, and community). Both native and nonnative plant species richness were positively correlated with edge density, Simpson's diversity index and interspersion/juxtaposition index, and were negatively correlated with mean patch size. The amount of variation explained at four spatial extents and three hierarchical levels ranged from 30% to 70%. At the landscape level, the best models explained 43% of the variation in native plant species richness and 70% of the variation in nonnative plant species richness (240-m extent). In general, the amount of variation explained was always higher for nonnative plant species richness, and the inclusion of landscape metrics always significantly improved the models. The best models explained 66% of the variation in nonnative plant species richness for both the conifer land cover type and lodgepole pine community. The relative influence of the components of spatial heterogeneity differed for native and nonnative plant species richness and varied with the spatial extent of analysis and levels of ecological hierarchy. The study offers an approach to quantify spatial heterogeneity to improve models of plant biodiversity. The results demonstrate that ecologists must recognize the importance of spatial heterogeneity in managing native and nonnative plant species.     

The evaluation strip: A new and robust method for plotting predicted responses from species distribution models

Increasing use is being made in conservation management of statistical models that couple extensive collections of species and environmental data to make predictions of the geographic distributions of species. While the relationships fitted between a species and its environment are relatively transparent for many of these modeling techniques, others are more ‘black box’ in character, only producing geographic predictions and providing minimal or untraditional summaries of the fitted relationships on which these predictions are based. This in turn prevents robust evaluation of the ecological sensibility of such models, a necessary process if model predictions are to be treated with confidence. Here we propose a new but simple method for visualizing modeled responses that can be implemented with any modeling method, and demonstrate its application using five common methods applied to the prediction of an Australian tree species. This is achieved by insetting an “evaluation strip” into the spatial data layers, which, after predictions have been made, can be clipped out and used for creating plots of the modelled responses. We present findings of the application strip for algorithms GLMs, GAMs, CLIM, DOMAIN and MARS. Evaluation strips can be constructed to investigate either uni-variate responses, or the simultaneous variation in predicted values in relation to two variables. The latter option is particularly useful for evaluating responses in models that allow the fitting of complex interaction terms.     

Use of Abundance of One Species as a Surrogate for Abundance of Others

Abstract: Indicator species concepts have a long history in conservation biology. Arguments in favor of these approaches generally stress expediency and assume efficacy. We tested the premise that the abundance patterns of one species can be used to infer those of other species. Our data consisted of 72,495 bird observations on 55 species across 1046 plots distributed across 30 sub basins. We analyzed abundance patterns at two spatial scales (plot and sub basin) and for empirical and a priori grouping. There were few significant indicator relationships at either scale or under either grouping rule, and those few we found did not explain a substantial portion of the abundance of other species. Coupled with the lack of proven efficacy for species surrogacy in the literature, our results indicate the utility of indicators and similar types of surrogate approaches must be demonstrated rather than assumed.     

Predicting landscape-scale biodiversity recovery by natural tropical forest regrowth

Natural forest regrowth is a cost-effective, nature-based solution for biodiversity recovery, yet different socioenvironmental factors can lead to variable outcomes. A critical knowledge gap in forest restoration planning is how to predict where natural forest regrowth is likely to lead to high levels of biodiversity recovery, which is an indicator of conservation value and the potential provisioning of diverse ecosystem services. We sought to predict and map landscape-scale recovery of species richness and total abundance of vertebrates, invertebrates, and plants in tropical and subtropical second-growth forests to inform spatial restoration planning. First, we conducted a global meta-analysis to quantify the extent to which recovery of species richness and total abundance in second-growth forests deviated from biodiversity values in reference old-growth forests in the same landscape. Second, we employed a machine-learning algorithm and a comprehensive set of socioenvironmental factors to spatially predict landscape-scale deviation and map it. Models explained on average 34% of observed variance in recovery (range 9–51%). Landscape-scale biodiversity recovery in second-growth forests was spatially predicted based on socioenvironmental landscape factors (human demography, land use and cover, anthropogenic and natural disturbance, ecosystem productivity, and topography and soil chemistry); was significantly higher for species richness than for total abundance for vertebrates (median range-adjusted predicted deviation 0.09 vs. 0.34) and invertebrates (0.2 vs. 0.35) but not for plants (which showed a similar recovery for both metrics [0.24 vs. 0.25]); and was positively correlated for total abundance of plant and vertebrate species (Pearson r = 0.45, p = 0.001). Our approach can help identify tropical and subtropical forest landscapes with high potential for biodiversity recovery through natural forest regrowth.     

Cost‐Effectiveness of Using Small Vertebrates as Indicators of Disturbance

In species‐rich tropical forests, effective biodiversity management demands measures of progress, yet budgetary limitations typically constrain capacity of decision makers to assess response of biological communities to habitat change. One approach is to identify ecological‐disturbance indicator species (EDIS) whose monitoring is also monetarily cost‐effective. These species can be identified by determining individual species’ responses to disturbance across a gradient; however, such responses may be confounded by factors other than disturbance. For example, in mountain environments the effects of anthropogenic habitat alteration are commonly confounded by elevation. EDIS have been identified with the indicator value (IndVal) metric, but there are weaknesses in the application of this approach in complex montane systems. We surveyed birds, small mammals, bats, and leaf‐litter lizards in differentially disturbed cloud forest of the Ecuadorian Andes. We then incorporated elevation in generalized linear (mixed) models (GL(M)M) to screen for EDIS in the data set. Finally, we used rarefaction of species accumulation data to compare relative monetary costs of identifying and monitoring EDIS at equal sampling effort, based on species richness. Our GL(M)M generated greater numbers of EDIS but fewer characteristic species relative to IndVal. In absolute terms birds were the most cost‐effective of the 4 taxa surveyed. We found one low‐cost bird EDIS. In terms of the number of indicators generated as a proportion of species richness, EDIS of small mammals were the most cost‐effective. Our approach has the potential to be a useful tool for facilitating more sustainable management of Andean forest systems. Rentabilidad del Uso de Pequeños Vertebrados como Indicadores de Perturbaciones     

Modelling plant species richness using functional groups

Conservation biologists increasingly rely on spatial predictive models of biodiversity to support decision-making. Therefore, highly accurate and ecologically meaningful models are required at relatively broad spatial scales. While statistical techniques have been optimized to improve model accuracy, less focus has been given to the question: How does the autecology of a single species affect model quality? We compare a direct modelling approach versus a cumulative modelling approach for predicting plant species richness, where the latter gives more weight to the ecology of functional species groups. In the direct modelling approach, species richness is predicted by a single model calibrated for all species. In the cumulative modelling approach, the species were partitioned into functional groups, with each group calibrated separately and species richness of each group was cumulated to predict total species richness. We hypothesized that model accuracy depends on the ecology of individual species and that the cumulative modelling approach would predict species richness more accurately. The predictors explained plant species richness by ca. 25%. However, depending on the functional group the deviance explained varied from 3 to 67%. While both modelling approaches performed equally well, the models of the different functional groups highly varied in their quality and their spatial richness pattern. This variability helps to improve our understanding on how plant functional groups respond to ecological gradients.     

Selection of Multiple Umbrella Species for Functional and Taxonomic Diversity to Represent Urban Biodiversity

Surrogates, such as umbrella species, are commonly used to reduce the complexity of quantifying biodiversity for conservation purposes. The presence of umbrella species is often indicative of high taxonomic diversity; however, functional diversity is now recognized as an important metric for biodiversity and thus should be considered when choosing umbrella species. We identified umbrella species associated with high taxonomic and functional biodiversity in urban areas in Switzerland. We analyzed 39,752 individuals of 574 animal species from 96 study plots and 1397 presences of 262 plant species from 58 plots. Thirty‐one biodiversity measures of 7 taxonomic groups (plants, spiders, bees, ground beetles, lady bugs, weevils and birds) were included in within‐ and across‐taxa analyses. Sixteen measures were taxonomical (species richness and species diversity), whereas 15 were functional (species traits including mobility, resource use, and reproduction). We used indicator value analysis to identify umbrella species associated with single or multiple biodiversity measures. Many umbrella species were indicators of high biodiversity within their own taxonomic group (from 33.3% in weevils to 93.8% in birds), to a lesser extent they were indicators across taxa. Principal component analysis revealed that umbrella species for multiple measures of biodiversity represented different aspects of biodiversity, especially with respect to measures of taxonomic and functional diversity. Thus, even umbrella species for multiple measures of biodiversity were complementary in the biodiversity aspects they represented. Thus, the choice of umbrella species based solely on taxonomic diversity is questionable and may not represent biodiversity comprehensively. Our results suggest that, depending on conservation priorities, managers should choose multiple and complementary umbrella species to assess the state of biodiversity. Selección de Múltiples Especies Paraguas para la Diversidad Funcional y Taxonómica para Representar la Biodiversidad Urbana     

The influence of productivity on the species richness of plants: a critical assessment

Despite much scrutiny the relationship between productivity and species richness remains controversial, and there is little agreement about causal processes. We present the results of a survey of 159 productivity-plant species richness (P-PSR) relationships from 131 published studies. We critically assessed each study with respect to experimental design and for the appropriateness of the surrogates used for productivity. We were able to accept only 60 of the reported relationships as robust tests of the P-PSR relationship and a further 18 as robust tests of the biomass species richness relationship. Previous analyses have found that unimodal P-PSR relationships predominate. In contrast, we found that, in studies that used data of continental to global extent, all P-PSR relationships were positive regardless of grain, that almost all were also positive in data sets of regional extent, and that unimodal relationships were not dominant even in studies of fine grain or small spatial extent. Our results differ substantially from previous meta-analyses because previous studies have included a large number of studies that do not meet basic experimental design criteria for objectively testing P-PSR relationships. These results have important implications for theory that attempts to explain species richness patterns. We critically review four dominant theories in light of our results and develop new falsifiable predictions of relationship from these theories at both small and large spatial scales.     

Relationships between Plant and Animal Species Richness at a Regional Scale in China

Abstract:  Important questions in conservation biology and ecology include whether species diversities of different groups of organisms are correlated and, in particular, whether plant diversity influences animal diversity. I used correlation and partial regression analyses to examine the relationships between species richness of vascular plants and four major groups of terrestrial vertebrates (mammals, amphibians, reptiles, and birds) in 28 provinces in China. Species richness data were obtained from the literature. Environmental variables included normalized difference vegetation index, mean January temperature, mean annual temperature, annual precipitation, May through August precipitation, actual evapotranspiration, potential evapotranspiration, and elevation range. Species richness was strongly and positively correlated among the five groups of organisms. Plant richness was correlated with animal richness more strongly than the richness of different animal groups correlated with each other except for reptile richness, which had a slightly higher correlation with amphibian richness than with plant richness. Plant richness uniquely explained 41 times more variance in the species richness of the four vertebrate groups combined than environmental variables uniquely did, suggesting that plant richness influences terrestrial vertebrate richness at the regional scale examined. Because of strong correlations between the diversity of vascular plants and vertebrates, the diversity of vascular plants may be used as a surrogate for the diversity of terrestrial animals in China. My results have implications for selection of areas to be protected at both regional and local scales.