Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes?

Aim Species distribution models and geographical information system (GIS) technologies are becoming increasingly important tools in conservation planning and decision‐making. Often the rich data bases of museums and herbaria serve as the primary data for predicting species distributions. Yet key assumptions about the primary data often are untested, and violation of such assumptions may have consequences for model predictions. For example, users of primary data assume that sampling has been random with respect to geography and environmental gradients. Here we evaluate the assumption that plant voucher specimens adequately sample the climatic gradient and test whether violation of this assumption influences model predictions. Location Bolivia and Ecuador. Methods Using 323,711 georeferenced herbarium collections and nine climatic variables, we predicted the distribution of 76 plant species using maximum entropy models (MAXENT) with training points that sampled the climate environments randomly and training points that reflected the climate bias in the herbarium collections. To estimate the distribution of species, MAXENT finds the distribution of maximum entropy (i.e. closest to uniform) subject to the constraint that the expected value for each environmental variable under the estimated distribution matches its empirical average. The experimental design included species that differed in geographical range and elevation; all species were modelled with 20 and 100 training points. We examined the influence of the number of training points and climate bias in training points, elevation and range size on model performance using analysis of variance models. Results We found that significant parts of the climatic gradient were poorly represented in herbarium collections for both countries. For the most part, existing climatic bias in collections did not greatly affect distribution predictions when compared with an unbiased data set. Although the effects of climate bias on prediction accuracy were found to be greater where geographical ranges were characterized by high spatial variation in the degree of climate bias (i.e. ranges where the bias of the various climates sampled by collections deviated considerably from the mean bias), the greatest influence on model performance was the number of presence points used to train the model. Main conclusions These results demonstrate that predictions of species distributions can be quite good despite existing climatic biases in primary data found in natural history collections, if a sufficiently large number of training points is available. Because of consistent overprediction of models, these results also confirm the importance of validating models with independent data or expert opinion. Failure to include independent model validation, especially in cases where training points are limited, may potentially lead to grave errors in conservation decision‐making and planning.     

Land use and climate influences on waterbirds in the Prairie Potholes

Aim We examined the influences of regional climate and land‐use variables on mallard (Anas platyrhynchos), blue‐winged teal (Anas discors), ruddy duck (Oxyura jamaicensis) and pied‐billed grebe (Podilymbus podiceps) abundances to inform conservation planning in the Prairie Pothole Region of the United States. Location The US portion of Bird Conservation Region 11 (US‐BCR11, the Prairie Potholes), which encompasses six states within the United States: Montana, North Dakota, South Dakota, Nebraska, Minnesota and Iowa. Methods We used data from the North American Breeding Bird Survey (NABBS), the National Land Cover Data Set, and the National Climatic Data Center to model the effects of environmental variables on waterbird abundance. We evaluated land‐use covariates at three logarithmically related spatial scales (1000, 10,000 and 100,000 ha), and constructed hierarchical spatial count models a priori using information from published habitat associations. Model fitting was performed using a hierarchical modelling approach within a Bayesian framework. Results Models with the same variables expressed at different scales were often in the best model subset, indicating that the influence of spatial scale was small. Both land‐use and climate variables contributed strongly to predicting waterbird abundance in US‐BCR11. The strongest positive influences on waterbird abundance were the percentage of wetland area across all three spatial scales, herbaceous vegetation and precipitation variables. Other variables that we included in our models did not appear to influence waterbirds in this study. Main conclusions Understanding the relationships of waterbird abundance to climate and land use may allow us to make predictions of future distribution and abundance as environmental factors change. Additionally, results from this study can suggest locations where conservation and management efforts should be focused.     

A dynamic, architectural plant model simulating resource-dependent growth

BACKGROUND AND AIMS: Physiological and architectural plant models have originally been developed for different purposes and therefore have little in common, thus making combined applications difficult. There is, however, an increasing demand for crop models that simulate the genetic and resource-dependent variability of plant geometry and architecture, because man is increasingly able to transform plant production systems through combined genetic and environmental engineering. MODEL: GREENLAB is presented, a mathematical plant model that simulates interactions between plant structure and function. Dual-scale automaton is used to simulate plant organogenesis from germination to maturity on the basis of organogenetic growth cycles that have constant thermal time. Plant fresh biomass production is computed from transpiration, assuming transpiration efficiency to be constant and atmospheric demand to be the driving force, under non-limiting water supply. The fresh biomass is then distributed among expanding organs according to their relative demand. Demand for organ growth is estimated from allometric relationships (e.g. leaf surface to weight ratios) and kinetics of potential growth rate for each organ type. These are obtained through parameter optimization against empirical, morphological data sets by running the model in inverted mode. Potential growth rates are then used as estimates of relative sink strength in the model. These and other 'hidden' plant parameters are calibrated using the non-linear, least-square method. KEY RESULTS AND CONCLUSIONS: The model reproduced accurately the dynamics of plant growth, architecture and geometry of various annual and woody plants, enabling 3D visualization. It was also able to simulate the variability of leaf size on the plant and compensatory growth following pruning, as a result of internal competition for resources. The potential of the model's underlying concepts to predict the plant's phenotypic plasticity is discussed.     

参数仪器变量估计近似分布方差的一种算法

本文对更一般的结构模型给出了参数的一种常用的仪器变量估计近似分布方差的一种算法．并且给出了未知真值x服从指数分布的例子．此算法对生物科学中统计规律的探讨有一定的应用价值．     

Does freshwater macroinvertebrate diversity along a pH‐gradient reflect adaptation to low pH?

1. The impacts of anthropogenic surface water acidification are much better known than those of natural acidity. Recent studies have indicated biodiversity is not degraded and species composition unaltered in naturally acidic compared to circumneutral watercourses.
2. Here, we use a geographically extensive dataset comprising sites in more than 200 Swedish streams to test whether the lack of effects on macroinvertebrate species diversity is due to exaptation and adaptation to natural acidity.
3. To this end, we modelled pH associated with spring flood episodes, which inflict the most challenging hydrochemical conditions to the biota. We compared taxonomic richness and species composition along the modelled pH gradient in northern Sweden, where acidity is largely natural, with southern Sweden, a region influenced by significant anthropogenic acidification.
4. We found Plecoptera richness did not respond to varying pH either in northern or southern Sweden. Ephemeroptera richness was sensitive to pH in both regions, while that of Trichoptera increased with increasing pH in southern Sweden, but decreased in the north. The taxonomic composition of Plecoptera changed along the pH gradient in both regions, whereas that of Ephemeroptera and Trichoptera changed more strongly with pH in southern Sweden.
5. Our results support the hypothesis that stream invertebrates are able to tolerate low pH through exaptation or adaptation, but that this capability varies among taxonomic groups.     

Detecting the coevolution of biosequences--an example of RNA interaction prediction

A probabilistic graphical model is proposed in order to detect the coevolution between different sites in biological sequences. The model extends the continuous-time Markov process of sequence substitution for single nucleic or amino acids and imposes general constraints regarding simultaneous changes on the substitution rate matrix. Given a multiple sequence alignment for each molecule of interest and a phylogenetic tree, the model can predict potential interactions within or between nucleic acids and proteins. Initial validation of the model is carried out using tRNA and 16S rRNA sequence data. The model accurately identifies the secondary interactions of tRNA as well as several known tertiary interactions. In addition, results on 16S rRNA data indicate this general and simple coevolutionary model outperforms several other parametric and nonparametric methods in predicting secondary interactions. Furthermore, the majority of the putative predictions exhibit either direct contact or proximity of the nucleotide pairs in the 3-dimensional structure of the Thermus thermophilus ribosomal small subunit. The results on RNA data suggest a general model of coevolution might be applied to other types of interactions between protein, DNA, and RNA molecules.     

LAKE SEDIMENT CHRYSOPHYTE SCALES FROM THE NORTHEASTERN U.S.A. AND THEIR RELATIONSHIP TO ENVIRONMENTAL VARIABLES

Chrysophyte scale assemblages were analyzed in the surface sediments (0–1 cm) of 146 lakes sampled in the U.S. Environmental Protection Agency's (EPA) Environmental Monitoring and Assessment Program–Surface Waters (EMAP-SW) in the northeastern U.S.A. Chrysophyte data from the EMAP lakes were combined with a previous study of 71 Adirondack PIRLA (Paleoecological Investigation of Recent Lake Acidification) lakes and collectively analyzed to examine the indicator potential of scaled chrysophytes in the northeastern U.S.A. with respect to several environmental variables. Canonical correspondence analysis (CCA) was used to determine which environmental variables influenced the distributions of species. Forward selection and Monte Carlo permutation tests showed that 51% of the variance in the chrysophyte assemblages was related to pH. The other six significant variables (conductivity, chloride, total phosphorus [TP], elevation, lake depth, and watershed area) contributed an additional 31% of the total (82%) variance explained by the seven forward-selected variables. Similar to previous studies, many taxa showed distinct distribution patterns with respect to pH. Partial and constrained CCAs indicated that, although all seven variables explained significant proportions of variation in the species data, a reliable inference model could be developed only for lake-water pH. The strength of this model ( R ²= 0.78, RMSE_boot= 0.47 of a pH unit) is comparable to a recently constructed diatom-based model for the EMAP lakes. The use of both models in paleolimnological and biomonitoring studies would be advantageous because they would provide two independent lines of evidence of environmental change.     

Adipokine and insulin profiles distinguish diabetogenic and non-diabetogenic obesities in mice

Objective: To use longitudinal profiling of plasma adipokines to distinguish diabetogenic vs. non‐diabetogenic obesity syndrome in two new mouse models of polygenic obesity. Research Methods and Procedures: Male mice of the NONcNZO5 strain develop a polygenic obesity syndrome uncomplicated by diabetes, whereas NONcNZO10 males develop a comparable polygenic obesity that precipitates type 2 diabetes. A multiplex immunoassay for simultaneous measurement of insulin and a panel of mouse adipokines (leptin, resistin, adiponectin, interleukin‐6, tumor necrosis factor α, macrophage chemoattractant protein‐1, plasminogen activator inhibitor‐1) were used to profile longitudinal changes in these strains between 4 and 16 weeks of age that might distinguish the non‐diabetogenic vs. diabetogenic obesity (diabesity). Results: Both strains became adipose, with NONcNZO5 males attaining a higher mean body weight with a higher percentage fat content. Weight gain in NONcNZO5 was accompanied by a transient peak in plasma insulin (PI) at 8 weeks followed by a decline into normal range, with normoglycemia maintained throughout. In contrast, NONcNZO10 showed no early PI secretory response because both body weight and plasma glucose increased between 4 and 8 weeks. Only after 12 weeks, with hyperglycemia established, was a delayed PI secretory response observed. Neither plasma leptin nor adiponectin concentrations significantly differentiated the two syndromes over time. However, repeated measures ANOVA showed that NONcNZO10 males maintained significantly higher plasma concentrations of two adipokines, resistin and plasminogen activator inhibitor‐1, and the pro‐inflammatory cytokine/adipokine macrophage chemoattractant protein‐1. Discussion: Longitudinal profiling of PI and adipokines in two new mouse models developing moderate obesity demonstrated that specific marker signatures differentiated a non‐diabetogenic obesity from a diabetogenic obesity.     

Environmental dependence of population dynamics and height growth of a subalpine conifer across its vertical distribution: an approach using high‐resolution aerial photographs

Many studies have reported shifts in the altitudinal ranges of plant species in response to recent global warming. However, most studies of tree species have been conducted on a small scale and have focused on tree line ecotones by examining tree rings and age structure on account of the long life spans of the trees. To examine the impact of climate change on forest dynamics at a regional scale, we investigated differences in the population density and canopy height of a Japanese subalpine coniferous species, Abies mariesii, between 1967 and 2003 by analysis of high‐resolution aerial photographs of the Hakkoda Mountains, Honshu, Japan. In 712 plots within the photographs we analyzed which environmental variables (including elevation, aspect, wetness, and distance from moorlands) account for these changes. The population density of A. mariesii decreased below 1000 m a.s.l. and increased above 1300 m a.s.l. It also increased around moorlands, which may provide refugia at low elevations. The rate of increase in canopy height was lowest on the southeastern slopes and on the periphery of the moorlands. The distinct changes in the population density of A. mariesii at its distribution limits probably reflect the responses of the population to climatic changes during three decades. Areas surrounding the moorlands may offer refugia in spite of the poor growing conditions there.     

Estimating abundance of mountain lions from unstructured spatial sampling

Mountain lions (Puma concolor) are often difficult to monitor because of their low capture probabilities, extensive movements, and large territories. Methods for estimating the abundance of this species are needed to assess population status, determine harvest levels, evaluate the impacts of management actions on populations, and derive conservation and management strategies. Traditional mark–recapture methods do not explicitly account for differences in individual capture probabilities due to the spatial distribution of individuals in relation to survey effort (or trap locations). However, recent advances in the analysis of capture–recapture data have produced methods estimating abundance and density of animals from spatially explicit capture–recapture data that account for heterogeneity in capture probabilities due to the spatial organization of individuals and traps. We adapt recently developed spatial capture–recapture models to estimate density and abundance of mountain lions in western Montana. Volunteers and state agency personnel collected mountain lion DNA samples in portions of the Blackfoot drainage (7,908 km²) in west-central Montana using 2 methods: snow back-tracking mountain lion tracks to collect hair samples and biopsy darting treed mountain lions to obtain tissue samples. Overall, we recorded 72 individual capture events, including captures both with and without tissue sample collection and hair samples resulting in the identification of 50 individual mountain lions (30 females, 19 males, and 1 unknown sex individual). We estimated lion densities from 8 models containing effects of distance, sex, and survey effort on detection probability. Our population density estimates ranged from a minimum of 3.7 mountain lions/100 km² (95% CI 2.3–5.7) under the distance only model (including only an effect of distance on detection probability) to 6.7 (95% CI 3.1–11.0) under the full model (including effects of distance, sex, survey effort, and distance × sex on detection probability). These numbers translate to a total estimate of 293 mountain lions (95% CI 182–451) to 529 (95% CI 245–870) within the Blackfoot drainage. Results from the distance model are similar to previous estimates of 3.6 mountain lions/100 km² for the study area; however, results from all other models indicated greater numbers of mountain lions. Our results indicate that unstructured spatial sampling combined with spatial capture–recapture analysis can be an effective method for estimating large carnivore densities. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.