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.     

Combining geodiversity with climate and topography to account for threatened species richness

Understanding threatened species diversity is important for long‐term conservation planning. Geodiversity—the diversity of Earth surface materials, forms, and processes—may be a useful biodiversity surrogate for conservation and have conservation value itself. Geodiversity and species richness relationships have been demonstrated; establishing whether geodiversity relates to threatened species’ diversity and distribution pattern is a logical next step for conservation. We used 4 geodiversity variables (rock‐type and soil‐type richness, geomorphological diversity, and hydrological feature diversity) and 4 climatic and topographic variables to model threatened species diversity across 31 of Finland's national parks. We also analyzed rarity‐weighted richness (a measure of site complementarity) of threatened vascular plants, fungi, bryophytes, and all species combined. Our 1‐km² resolution data set included 271 threatened species from 16 major taxa. We modeled threatened species richness (raw and rarity weighted) with boosted regression trees. Climatic variables, especially the annual temperature sum above 5 °C, dominated our models, which is consistent with the critical role of temperature in this boreal environment. Geodiversity added significant explanatory power. High geodiversity values were consistently associated with high threatened species richness across taxa. The combined effect of geodiversity variables was even more pronounced in the rarity‐weighted richness analyses (except for fungi) than in those for species richness. Geodiversity measures correlated most strongly with species richness (raw and rarity weighted) of threatened vascular plants and bryophytes and were weakest for molluscs, lichens, and mammals. Although simple measures of topography improve biodiversity modeling, our results suggest that geodiversity data relating to geology, landforms, and hydrology are also worth including. This reinforces recent arguments that conserving nature's stage is an important principle in conservation.     

A multivariate analysis of beta diversity across organisms and environments

We examined variability in hierarchical beta diversity across ecosystems, geographical gradients, and organism groups using multivariate spatial mixed modeling analysis of two independent data sets. The larger data set comprised reported ratios of regional species richness (RSR) to local species richness (LSR) and the second data set consisted of RSR:LSR ratios derived from nested species-area relationships. There was a negative, albeit relatively weak, relationship between beta diversity and latitude. We found only relatively subtle differences in beta diversity among the realms, yet beta diversity was lower in marine systems than in terrestrial or freshwater realms. Beta diversity varied significantly among organisms' major characteristics such as body mass, trophic position, and dispersal type in the larger data set. Organisms that disperse via seeds had highest beta diversity, and passively dispersed organisms showed the lowest beta diversity. Furthermore, autotrophs had lower beta diversity than organisms higher up the food web; omnivores and carnivores had consistently higher beta diversity. This is evidence that beta diversity is simultaneously controlled by extrinsic factors related to geography and environment, and by intrinsic factors related to organism characteristics.     

Robust ridge regression for estimating the effects of correlated gene expressions on phenotypic traits

Statistical packages such as edgeR and DESeq are intended to detect genes that are relevant to phenotypic traits and diseases. A few studies have also modeled the relationships between gene expressions and traits. In the presence of multicollinearity and outliers, which are unavoidable in genetic data, the robust ridge regression estimator can be applied with the trait value as the response variable and the gene expressions as explanatory variables. In some simulation scenarios, the robust ridge estimator is resistant to outliers and less susceptible to multicollinearity than the ordinary least-squares (OLS) estimator. This study investigated the reliability of the robust ridge estimator, in a scenario where the explanatory variables have tail-dependence and negative binomial distributions, by comparing its performance to that of OLS using vine copula to model the tail-dependence among gene expressions. The robust ridge estimator and OLS were both applied to an ecological dataset. First, statistical analysis was used to compare RNA sequencing data between two treatments; then, 15 differentially expressed genes were selected. Next, the regression parameter estimates of robust ridge and OLS for the effects of the 15 contigs (explanatory variables) on trait values (response variables) were compared. Robust ridge regression was found to detect fewer positive and negative slopes than OLS regression. These results indicate that robust ridge regression can be successfully applied for RNA sequencing analysis to estimate the effect of trait-associated genes using real data, and holds great promise as a tool for modeling the association between RNA expression and phenotypic traits.     

Metabolic rates of epipelagic marine copepods as a function of body mass and temperature

Metabolic rates (oxygen consumption, ammonia excretion, phosphate excretion) have been calculated as a function of body mass (dry, carbon, nitrogen and phosphorus weights) and habitat temperature, using multiple regression. The metabolic data used for this analysis were species structured, collected from Arctic to Antarctic seas (temperature range: -1.7°C to 29.0°C). The data were further divided into geographical and/or seasonal groups (35 species and 43 data sets for oxygen consumption; 38 species and 58 data sets for ammonia excretion; 22 species and 31 data sets for phosphate excretion). The results revealed that the variance attributed to body mass and temperature was highest (93-96%) for oxygen consumption rates, followed by ammonia excretion rates (74-80%) and phosphate excretion rates (46-56%). Among the various body mass units, the best correlation was provided by the nitrogen unit, followed by the dry weight unit. The calculated Q₁₀ values varied slightly according to the choice of body mass units; overall ranges were 1.8-2.1 for oxygen consumption rates, 1.8-2.0 for ammonia excretion rates and 1.6-1.9 for phosphate excretion rates. The effects of body mass and temperature on the metabolic quotients (O:N, N:P, O:P) were insignificant in most cases. Although the copepod metabolic data used in the present analysis were for adult and pre-adult stages, possible applications of the resultant regression equations to predict the metabolic rates of naupliar and early copepodite stages are discussed. Finally, global patterns of net growth efficiency [growth (growth+metabolism)^-1] of copepods were deduced by combining the present metabolic equation with Hirst and Lampitt's global growth equation for epipelagic marine copepods.     

Effectiveness of terrestrial protected areas for conservation of lake fish communities

Freshwater protected areas are rare even though freshwater ecosystems are among the most imperiled in the world. Conservation actions within terrestrial protected areas (TPAs) such as development or resource extraction regulations may spill over to benefit freshwater ecosystems within their boundaries. Using data from 175 lakes across Ontario, Canada, we compared common indicators of fish‐assemblage status (i.e., species richness, Shannon diversity index, catch per unit effort, and normalized‐length size spectrum slopes) to evaluate whether TPAs benefit lake fish assemblages. Nearest neighbor cluster analysis was used to generate pairs of lakes: inside versus outside, inside versus bordering, and bordering versus outside TPAs based on lake characteristics. The diversity and abundance indicators did not differ significantly across comparisons, but normalized‐length size spectrum slopes (NLSS) were significantly steeper in lakes outside parks. The latter indicated assemblage differences (greater abundances of small‐bodied species) and less‐efficient energy transfer through the trophic levels of assemblages outside parks. Although not significantly different, pollution‐ and turbidity‐tolerant species were more abundant outside parks, whereas 3 of the 4 pollution‐intolerant species were more abundant within parks. Twenty‐one percent of the difference in slopes was related to higher total dissolved solids concentrations and angling pressure. Our results support the hypothesis that TPAs benefit lake fish assemblages and suggest that NLSS slopes are informative indicators for aquatic protected area evaluations because they represent compositional and functional aspects of communities.     

Land‐Cover Change and Avian Diversity in the Conterminous United States

Abstract: Changes in land use and land cover have affected and will continue to affect biological diversity worldwide. Yet, understanding the spatially extensive effects of land‐cover change has been challenging because data that are consistent over space and time are lacking. We used the U.S. National Land Cover Dataset Land Cover Change Retrofit Product and North American Breeding Bird Survey data to examine land‐cover change and its associations with diversity of birds with principally terrestrial life cycles (landbirds) in the conterminous United States. We used mixed‐effects models and model selection to rank associations by ecoregion. Land cover in 3.22% of the area considered in our analyses changed from 1992 to 2001, and changes in species richness and abundance of birds were strongly associated with land‐cover changes. Changes in species richness and abundance were primarily associated with changes in nondominant types of land cover, yet in many ecoregions different types of land cover were associated with species richness than were associated with abundance. Conversion of natural land cover to anthropogenic land cover was more strongly associated with changes in bird species richness and abundance than persistence of natural land cover in nearly all ecoregions and different covariates were most strongly associated with species richness than with abundance in 11 of 17 ecoregions. Loss of grassland and shrubland affected bird species richness and abundance in forested ecoregions. Loss of wetland was associated with bird abundance in forested ecoregions. Our findings highlight the value of understanding changes in nondominant land cover types and their association with bird diversity in the United States.     

Effects of productivity on biodiversity in forest ecosystems across the United States and China

In the global campaign against biodiversity loss in forest ecosystems, land managers need to know the status of forest biodiversity, but practical guidelines for conserving biodiversity in forest management are lacking. A major obstacle is the incomplete understanding of the relationship between site primary productivity and plant diversity, due to insufficient ecosystem‐wide data, especially for taxonomically and structurally diverse forest ecosystems. We investigated the effects of site productivity (the site's inherent capacity to grow timber) on tree species richness across 19 types of forest ecosystems in North America and China through 3 ground‐sourced forest inventory data sets (U.S. Forest Inventory and Analysis, Cooperative Alaska Forest Inventory, and Chinese Forest Management Planning Inventory). All forest types conformed to a consistent and highly significant (P < 0.001) hump‐shaped unimodal relationship, of which the generalized coefficients of determination averaged 20.5% over all the forest types. That is, tree species richness first increased as productivity increased at a progressively slower rate, and, after reaching a maximum, richness started to decline. Our consistent findings suggest that forests of high productivity would sustain few species because they consist mostly of flat homogeneous areas lacking an environmental gradient along which a diversity of species with different habitats can coexist. The consistency of the productivity–biodiversity relationship among the 3 data sets we examined makes it possible to quantify the expected tree species richness that a forest stand is capable of sustaining, and a comparison between the actual species richness and the sustainable values can be useful in prioritizing conservation efforts.     

Landscape connectivity strengthens local-regional richness relationships in successional plant communities

Local species diversity is maintained over ecological time by a balance between dispersal and species interactions. Local-regional species richness relationships are often used to investigate the relative importance of these two processes and the scales at which they operate. For communities undergoing succession, theory predicts a temporal progression in local-regional species richness relationships: from no relationship to positive linear to saturating. However, observational tests have been mixed, and experiments have been rare. Using a replicated large-scale experiment, we evaluate the impact of two dispersal-governing processes at the regional scale, connectivity and shape of the region (i.e., patches), on the progression of local-regional species richness relationships for plant communities undergoing succession. Regional connectivity accelerates the transition from no relationship to a positive linear relationship, while the shape of the region has no consistent effect nine years post-disturbance. Our results experimentally demonstrate the importance of dispersal in structuring local-regional species richness relationships over time and suggest that conservation corridors among regions can increase local diversity through regional enrichment of plant communities undergoing reassembly.     

Extrinsic and intrinsic controls of zooplankton diversity in lakes

Pelagic crustacean zooplankton were collected from 336 Norwegian lakes covering a wide range of latitude, altitude, lake area, mean depth, production (as chlorophyll a), and fish community structure. Mean zooplankton species richness during the ice-free season was generally low at high latitudes and altitudes. Further, lower species richness was recorded in western lakes, possibly reflecting constraints on migration and dispersal. However, despite obvious spatial limitations, geographic boundaries were only weak predictors of mean zooplankton richness. Similarly, lake surface area did not contribute positively to mean richness such as seen in other ecosystem surveys. Rather, intrinsic factors such as primary production and fish community (planktivore) structure were identified by regression analysis as the major predictors of zooplankton diversity, while a positive correlation was observed between species richness and total zooplankton biomass. However, in spite of a large number of variables included in this study, the predictive power of multiple regression models was modest (<50% variance explained), pointing to a major role for within-lake properties, as yet unidentified intrinsic forces, stochasticity, or dispersal as constraints on zooplankton diversity in these lakes.     

Spatial scale and species identity influence the indigenous-alien diversity relationship in springtails

Although theory underlying the invasion paradox, or the change in the relationship between the richness of alien and indigenous species from negative to positive with increasing spatial scale, is well developed and much empirical work on the subject has been undertaken, most of the latter has concerned plants and to a lesser extent marine invertebrates. Here we therefore examine the extent to which the relationships between indigenous and alien species richness change from the local metacommunity to the interaction neighborhood scales, and the influences of abundance, species identity, and environmental favorability thereon, in springtails, a significant component of the soil fauna. Using a suite of modeling techniques, including generalized least squares and geographically weighted regressions to account for spatial autocorrelation or nonstationarity of the data, we show that the abundance and species richness of both indigenous and alien species at the metacommunity scale respond strongly to declining environmental favorability, represented here by altitude. Consequently, alien and indigenous diversity covary positively at this scale. By contrast, relationships are more complex at the interaction neighborhood scale, with the relationship among alien species richness and/or density and the density of indigenous species varying between habitats, being negative in some, but positive in others. Additional analyses demonstrated a strong influence of species identity, with negative relationships identified at the interaction neighborhood scale involving alien hypogastrurid springtails, a group known from elsewhere to have negative effects on indigenous species in areas where they have been introduced. By contrast, diversity relationships were positive with the other alien species. These results are consistent with both theory and previous empirical findings for other taxa, that interactions among indigenous and alien species change substantially with spatial scale and that environmental favorability may play a key role in explaining the larger scale patterns. However, they also suggest that the interactions may be affected by the identity of the species concerned, especially at the interaction neighborhood scale.     

Using community‐level metrics to monitor the effects of marine protected areas on biodiversity

Marine protected areas (MPAs) are used to protect species, communities, and their associated habitats, among other goals. Measuring MPA efficacy can be challenging, however, particularly when considering responses at the community level. We gathered 36 abundance and 14 biomass data sets on fish assemblages and used meta‐analysis to evaluate the ability of 22 distinct community diversity metrics to detect differences in community structure between MPAs and nearby control sites. We also considered the effects of 6 covariates—MPA size and age, MPA size and age interaction, latitude, total species richness, and level of protection—on each metric. Some common metrics, such as species richness and Shannon diversity, did not differ consistently between MPA and control sites, whereas other metrics, such as total abundance and biomass, were consistently different across studies. Metric responses derived from the biomass data sets were more consistent than those based on the abundance data sets, suggesting that community‐level biomass differs more predictably than abundance between MPA and control sites. Covariate analyses indicated that level of protection, latitude, MPA size, and the interaction between MPA size and age affect metric performance. These results highlight a handful of metrics, several of which are little known, that could be used to meet the increasing demand for community‐level indicators of MPA effectiveness.     

Invertebrate Morphospecies as Surrogates for Species: A Case Study

Environmental monitoring and conservation evaluation in terrestrial habitats may be enhanced by the use of invertebrate inventories, but taxonomic and logistic constraints frequently encountered during conventional taxonomic treatment have greatly restricted their use. To overcome this problem we suggest that nonspecialists may be used to classify invertebrates to morphospecies without compromising scientific accuracy. To test this proposition, large pitfall and litter samples of ants, beetles, and spiders from four forest types were sorted to morphospecies by a nonspecialist and to species by specialists. These data were used to generate morphospecies and species inventories and to estimate richness (α diversity) and turnover (β diversity), information frequently used in the above activities. Our results show that the estimates of richness of ants and spiders varied little between morphospecies and species inventories. Differences between estimates of beetle richness were largely influenced by errors of identification in two families, Curculionidae and Staphylinidae. But morphospecies and species inventories yielded identical ranking of forest type using richness. Turnover was assessed by sample ordination, which revealed similar clusters regardless of the type of inventory. Analysis of similarities of assemblages of ants and beetles showed significant differences between all forest types. Spider assemblages showed a lower level of discrimination. The assessment of turnover was consistent among inventories but different between the major taxa. Our findings suggest that morphospecies may be used as surrogates for species in some environmental monitoring and conservation, in particular when decisions are guided by estimates of richness and the assessment of turnover.     

Changes in distribution patterns of weed species richness in agricultural lands on Qinghai-Tibet Plateau under climate change北大核心CSCD

农田杂草是阻碍农业生产的主要因素之一.明确农田杂草丰富度分布格局对农业生产管理具有重要意义.以青藏高原农田杂草为研究对象,利用物种分布模型探讨基于县域尺度的农田杂草物种丰富度分布格局及其未来(2050s)的变化,利用逐步回归筛选影响物种丰富度的环境因子,基于传统最小二乘法(OLS)和地理加权回归模型(GWR)分析环境因子对农田杂草物种丰富度的影响,并对两种分析方法进行比较.结果显示:(1)分布在青藏高原的农田主要杂草有51科284种,其中59种单子叶杂草、222种双子叶杂草、135种一年生杂草和149种多年生杂草.青藏高原农田杂草物种丰富度呈由西向东递增的变化规律,物种丰富度中心(丰富度值为167-194)主要集中在一江两河、河湟谷地和川西北等地区;(2)全球气候变化背景下,未来(2050s)青藏高原农田杂草物种丰富度整体呈由东南向西北方向增加的趋势,其中SSP1-2.6情境下最多增加43种,SSP5-8.5情境下最多增加49种;(3)GWR模型优于OLS,其结果表明青藏高原农田杂草物种丰富度的主要驱动因子是最冷季平均温、太阳辐射和最干月降水量,上述变量对杂草丰富度的影响存在明显的空间差异性,其中最冷季平均温由南向北逐渐从负向影响转变为正向影响.太阳辐射整体在青藏高原东部边缘等地区对农田杂草丰富度起正向的影响,在藏东南、青藏高原北部边缘等地区起负向的影响.最干月降水量对整个研究区域起负向影响,并表现出影响力由南向北逐步递增的趋势.上述结果表明青藏高原农田杂草物种丰富度调查不足,实际观测到的丰富度值明显低于当前气候下潜在的丰富度值,存在低估现象.当前气候背景下的农田杂草物种丰富度中心分布地区在未来仍是重点监管对象,且未来青藏高原部分地区作物可能面临新的杂草入侵风险.建议未来研究应注重于青藏高原粮食主产区农田杂草群落结构和功能调查、杂草和作物种间关系、耕地尺度上丰富度驱动因子分析等方面,为区域杂草管理和防治提供充分科学依据.(图6表2参53)     

Scale-dependent variation in coral community similarity across sites, islands, and island groups

Community similarity is the proportion of species richness in a region that is shared on average among communities within that region. The slope of local richness (alpha diversity) regressed on regional richness (gamma diversity) can serve as an index of community similarity across regions with different regional richness. We examined community similarity in corals at three spatial scales (among transects at a site, sites on an island, and islands within an island group) across a 10 000-km longitudinal diversity gradient in the west-central Pacific Ocean. When alpha diversity was regressed on gamma diversity, the slopes, and thus community similarity, increased with scale (0.085, 0.261, and 0.407, respectively) because a greater proportion of gamma diversity was subsumed within alpha diversity as scale increased. Using standard randomization methods, we also examined how community similarity differed between observed and randomized assemblages and how this difference was affected by spatial separation of species within habitat types and specialization of species to three habitat types (reef flats, crests, and slopes). If spatial separation within habitat types and/or habitat specialization (i.e., underdispersion) occurs, fewer species are shared among assemblages than the random expectation. When the locations of individual coral colonies were randomized within and among habitat types, community similarity was 46-47% higher than that for observed assemblages at all three scales. We predicted that spatial separation of coral species within habitat types should increase with scale due to dispersal/extinction dynamics in this insular system, but that specialization of species to different habitat types should not change because habitat differences do not change with scale. However, neither habitat specialization nor spatial separation within habitat types differed among scales. At the two larger scales, each accounted for 22-24% of the difference in community similarity between observed and randomized assemblages. At the smallest scale (transect-site), neither spatial separation within habitat types nor habitat specialization had significant effects on community similarity, probably due to the small size of transect samples. The results suggest that coral species can disperse among islands in an island group as easily as they can among sites on an island over time scales that are relevant to their establishment and persistence on reefs.     

Efficacy of extracting indices from large‐scale acoustic recordings to monitor biodiversity

Passive acoustic monitoring could be a powerful way to assess biodiversity across large spatial and temporal scales. However, extracting meaningful information from recordings can be prohibitively time consuming. Acoustic indices (i.e., a mathematical summary of acoustic energy) offer a relatively rapid method for processing acoustic data and are increasingly used to characterize biological communities. We examined the relationship between acoustic indices and the diversity and abundance of biological sounds in recordings. We reviewed the acoustic‐index literature and found that over 60 indices have been applied to a range of objectives with varying success. We used 36 of the most indicative indices to develop a predictive model of the diversity of animal sounds in recordings. Acoustic data were collected at 43 sites in temperate terrestrial and tropical marine habitats across the continental United States. For terrestrial recordings, random‐forest models with a suite of acoustic indices as covariates predicted Shannon diversity, richness, and total number of biological sounds with high accuracy (R² ≥ 0.94, mean squared error [MSE] ≤170.2). Among the indices assessed, roughness, acoustic activity, and acoustic richness contributed most to the predictive ability of models. Performance of index models was negatively affected by insect, weather, and anthropogenic sounds. For marine recordings, random‐forest models poorly predicted Shannon diversity, richness, and total number of biological sounds (R² ≤ 0.40, MSE ≥ 195). Our results suggest that using a combination of relevant acoustic indices in a flexible model can accurately predict the diversity of biological sounds in temperate terrestrial acoustic recordings. Thus, acoustic approaches could be an important contribution to biodiversity monitoring in some habitats.     

Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters

Two artificial neural networks (ANNs), unsupervised and supervised learning algorithms, were applied to suggest practical approaches for the analysis of ecological data. Four major aquatic insect orders (Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera, i.e. EPTC), and four environmental variables (elevation, stream order, distance from the source, and water temperature) were used to implement the models. The data were collected and measured at 155 sampling sites on streams of the Adour–Garonne drainage basin (South-western France). The modelling procedure was carried out following two steps. First, a self-organizing map (SOM), an unsupervised ANN, was applied to classify sampling sites using EPTC richness. Second, a backpropagation algorithm (BP), a supervised ANN, was applied to predict EPTC richness using a set of four environmental variables. The trained SOM classified sampling sites according to a gradient of EPTC richness, and the groups obtained corresponded to geographic regions of the drainage basin and characteristics of their environmental variables. The SOM showed its convenience to analyze relationships among sampling sites, biological attributes, and environmental variables. After accounting for the relationships in data sets, the BP used to predict the EPTC richness with a set of four environmental variables showed a high accuracy (r=0.91 and r=0.61 for training and test data sets respectively). The prediction of EPTC richness is thus a valuable tool to assess disturbances in given areas: by knowing what the EPTC richness should be, we can determine the degree to which disturbances have altered it. The results suggested that methodologies successively using two different neural networks are helpful to understand ecological data through ordination first, and then to predict target variables.     

Modifying native and exotic species richness correlations: the influence of fire and seed addition.

An important goal in restoration is to increase the richness of native species while reducing exotic species. However, native species richness is often positively correlated with exotic species richness. In a grassland-savanna system in Michigan (USA), we show that management that focuses on changing the nature of the exotic-native richness relationship can be used to restore native communities. Native and exotic species richnesses were positively correlated, likely due to a shared coupling with aboveground live biomass (a surrogate for productivity). The addition of native seed shifted the exotic-native richness relationship from a linear positive to a monotonic relationship: in areas of intermediate levels of exotic species richness, seed addition increased native diversity without an associated effect on exotic diversity, but in areas of high or low exotic richness, it did not affect native species richness. Prescribed burning broke the correlation between native and exotic richness with no consistent effect on the richness of either group. However, when burning was combined with native-seed addition, the relationship between native and exotic richness was maintained and was shifted upwards, enhancing native recruitment. Although aboveground productivity was strongly related to species richness across the landscape, changes in productivity did not drive these shifts.     

Predicting Plant Extinction Based on Species-Area Curves in Prairie Fragments with High Beta Richness

Abstract:  Species-area relationships and island biogeography theory are commonly used to predict how species richness will decline with fragmentation. There are a variety of largely untested assumptions in these approaches, including the assumptions that populations are distributed uniformly before fragmentation, and that local extinctions are due to effects of small population sizes. If populations are not distributed uniformly, then populations can be abundant locally but rare globally. This would cause extinction rates to be smaller than predicted. We tested extinction theory by developing estimates of the number of plant species that should be present in small tallgrass prairie fragments and then testing the uniformity assumption by partitioning species richness into α (within site) and β (among site) components in Iowa prairies. Many more native prairie plant species were present in surveys of prairie fragments (491) than was predicted based on theory (27–207). A large proportion (75%) of the total species richness was β richness. We suggest that the high proportion of β richness was responsible for the shallow species-area slopes and the lower than expected number of species losses and that a better understanding of what determines β diversity will improve predictions of fragmentation effects on richness of plants. We also suggest that plants in prairie remnants may be best conserved by protecting different prairie types rather than by protecting a few large areas containing a single prairie type.     

Dealing with collinearity in behavioural and ecological data: model averaging and the problems of measurement error

There has been a great deal of recent discussion of the practice of regression analysis (or more generally, linear modelling) in behaviour and ecology. In this paper, I wish to highlight two factors that have been under-considered, collinearity and measurement error in predictors, as well as to consider what happens when both exist at the same time. I examine what the consequences are for conventional regression analysis (ordinary least squares, OLS) as well as model averaging methods, typified by information theoretic approaches based around Akaike’s information criterion. Collinearity causes variance inflation of estimated slopes in OLS analysis, as is well known. In the presence of collinearity, model averaging reduces this variance for predictors with weak effects, but also can lead to parameter bias. When collinearity is strong or when all predictors have strong effects, model averaging relies heavily on the full model including all predictors and hence the results from this and OLS are essentially the same. I highlight that it is not safe to simply eliminate collinear variables without due consideration of their likely independent effects as this can lead to biases. Measurement error is also considered and I show that when collinearity exists, this can lead to extreme biases when predictors are collinear, have strong effects but differ in their degree of measurement error. I highlight techniques for dealing with and diagnosing these problems. These results reinforce that automated model selection techniques should not be relied on in the analysis of complex multivariable datasets.