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Phylogenetic conservatism in plant phenology

Phenological events – defined points in the life cycle of a plant or animal – have been regarded as highly plastic traits, reflecting flexible responses to various environmental cues. The ability of a species to track, via shifts in phenological events, the abiotic environment through time might dictate its vulnerability to future climate change. Understanding the predictors and drivers of phenological change is therefore critical. Here, we evaluated evidence for phylogenetic conservatism – the tendency for closely related spe-cies to share similar ecological and biological attributes – in phenological traits across flowering plants. We aggregated published and unpublished data on timing of first flower and first leaf, encompassing ~4000 species at 23 sites across the Northern Hemisphere. We reconstructed the phylogeny for the setof included species, first, using the software program Phylomatic, and second, from DNA data. We then quantified phylogenetic conservatism in plant phenology within and across sites. We show that more closely related species tend to flower and leaf at similar times. By contrastingmean flowering times within and across sites, however, we illustrate that it is not the time of yearthat is conserved, but rather the phenological responses to a common set of abiotic cues. Our findings suggest that species cannot be treated as statistically independent when modelling phenological responses. Synthesis. Closely related species tend to resemble each other in the timing of their life-history events, a likely product of evolutionarily conser ved responses to environmental cues. The search for the underlying drivers of phenology must therefore account for species’ shared evolutionary histories.

Journal of Ecology

NABat ML: Utilizing deep learning to enable crowdsourced development of automated, scalable solutions for documenting North American bat populations

Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to use its technology infrastructure to assess status and trends of bat populations, while developing innovative and community-driven conservation solutions. Here, we present NABat ML , an automated machine-learning algorithm that improves the scalability and scientific transparency of NABat acoustic monitoring. This model combines signal processing techniques and convolutional neural networks (CNNs) to detect and classify recorded bat echolocation calls. We developed our CNN model with internet-based computing resources (‘cloud environment’), and trained it on >600,000 spectrogram images. We also incorporated species range maps to improve the robustness and accuracy of the model for future ‘unseen’ data. We evaluated model performance using a comprehensive, independent, holdout dataset. NABat ML successfully distinguished 31 classes (30 species and a noise class) with overall weighted-average accuracy and precision rates of 92%, and ≥90% classification accuracy for 19 of the bat species. Using a single cloud-environment computing instance, the entire model training process took <16 h. Synthesis and applications . Our convolutional neural network (CNN)-based model, NABat ML , classifies 30 North American bat species using their recorded echolocation calls with an overall accuracy of 92%. In addition to providing highly accurate species-level classification, NABat ML and its outputs are compatible with Bayesian and other statistical techniques for measuring uncertainty in classification. Our model is open-source and reproducible, enabling future implementations as software on end-user devices and cloud-based web applications. These qualities make NABat ML highly suitable for applications ranging from grassroots community science initiatives to big-data methods developed and implemented by researchers and professional practitioners. We believe the transparency and accessibility of NABat ML will encourage broad-scale participation in bat monitoring, and enable development of innovative solutions needed to conserve North American bat species.

Journal of Applied Ecology

Multiscale analysis of river networks using the R package linbin

Analytical tools are needed in riverine science and management to bridge the gap between GIS and statistical packages that were not designed for the directional and dendritic structure of streams. We introduce linbin, an R package developed for the analysis of riverscapes at multiple scales. With this software, riverine data on aquatic habitat and species distribution can be scaled and plotted automatically with respect to their position in the stream network or&mdash;in the case of temporal data&mdash;their position in time. The linbin package aggregates data into bins of different sizes as specified by the user. We provide case studies illustrating the use of the software for (1) exploring patterns at different scales by aggregating variables at a range of bin sizes, (2) comparing repeat observations by aggregating surveys into bins of common coverage, and (3) tailoring analysis to data with custom bin designs. Furthermore, we demonstrate the utility of linbin for summarizing patterns throughout an entire stream network, and we analyze the diel and seasonal movements of tagged fish past a stationary receiver to illustrate how linbin can be used with temporal data. In short, linbin enables more rapid analysis of complex data sets by fisheries managers and stream ecologists and can reveal underlying spatial and temporal patterns of fish distribution and habitat throughout a riverscape.

North American Journal of Fisheries Management

Comparison of HSPF and PRMS model simulated flows using different temporal and spatial scales in the Black Hills, South Dakota

The hydrological simulation program Fortran ( HSPF ) [ Hydrological Simulation Program Fortran version 12.2 (Computer software). USEPA, Washington, DC] and the precipitation runoff modeling system ( PRMS ) [ Precipitation Runoff Modeling System version 4.0 (Computer software). USGS, Reston, VA] models are semidistributed, deterministic hydrological tools for simulating the impacts of precipitation, land use, and climate on basin hydrology and streamflow. Both models have been applied independently to many watersheds across the United States. This paper reports the statistical results assessing various temporal (daily, monthly, and annual) and spatial (small versus large watershed) scale biases in HSPF and PRMS simulations using two watersheds in the Black Hills, South Dakota. The Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient ( r "> r r ), and coefficient of determination ( R 2 "> R 2 R2 ) statistics for the daily, monthly, and annual flows were used to evaluate the models’ performance. Results from the HSPF models showed that the HSPF consistently simulated the annual flows for both large and small basins better than the monthly and daily flows, and the simulated flows for the small watershed better than flows for the large watershed. In comparison, the PRMS model results show that the PRMS simulated the monthly flows for both the large and small watersheds better than the daily and annual flows, and the range of statistical error in the PRMS models was greater than that in the HSPF models. Moreover, it can be concluded that the statistical error in the HSPF and the PRMS daily, monthly, and annual flow estimates for watersheds in the Black Hills was influenced by both temporal and spatial scale variability.

South Dakota

Greater than the sum of its parts: Computationally flexible Bayesian hierarchical modeling

We propose a multistage method for making inference at all levels of a Bayesian hierarchical model (BHM) using natural data partitions to increase efficiency by allowing computations to take place in parallel form using software that is most appropriate for each data partition. The full hierarchical model is then approximated by the product of independent normal distributions for the data component of the model. In the second stage, the Bayesian maximum a posteriori (MAP) estimator is found by maximizing the approximated posterior density with respect to the parameters. If the parameters of the model can be represented as normally distributed random effects, then the second-stage optimization is equivalent to fitting a multivariate normal linear mixed model. We consider a third stage that updates the estimates of distinct parameters for each data partition based on the results of the second stage. The method is demonstrated with two ecological data sets and models, a generalized linear mixed effects model (GLMM) and an integrated population model (IPM). The multistage results were compared to estimates from models fit in single stages to the entire data set. In both cases, multistage results were very similar to a full MCMC analysis. Supplementary materials accompanying this paper appear online.

Journal of Agricultural, Biological and Environmen

A comparison of measures of riverbed form for evaluating distributions of benthic fishes

A method to quantitatively characterize the bed forms of a large river and a preliminary test of the relationship between bed-form characteristics and catch per unit area of benthic fishes is presented. We used analog paper recordings of bathymetric data from the Missouri River and fish data collected from 1996 to 1998 at both the segment (???101-102-km) and macrohabitat (???10-1-100-km) spatial scales. Bed-form traces were transformed to digital data with image analysis software. The slope, mean residual, and SD of the residuals of the regression of depth versus distance along the bottom, as well as mean depth, were estimated for each trace. These four metrics were compared with sinuosity, fractal dimension, critical scale, and maximum mean angle for the same traces. Mean depth and sinuosity differed among segments and macrohabitats. Fractal-based measures of the relative depth of bottom troughs (critical scale) and smoothness (maximum mean angle) differed among segments. Statistics-based measures of the relative depth of bottom troughs (mean residual) and smoothness (SD of the residuals) differed among macrohabitats. Sites with shovelnose sturgeon Scaphirhynchus platorynchus were shallower and smoother than sites without shovelnose sturgeon. When compared with sites without sicklefin chub Macrhybopsis meeki, sites with sicklefin chub were shallower, had shallower troughs, and sloped more out of the flow of the river. Sites with sturgeon chub M. gelida were shallower, had shallower troughs, and were smoother than sites without sturgeon chub. Sites with and without channel catfish Ictalurus punctatus did not differ for any bed-form variables measured. Nonzero shovelnose sturgeon density increased with depth, whereas nonzero sturgeon chub density decreased with depth. Indices of bed-form structure demonstrated potential for describing the distribution and abundance of Missouri River benthic fishes. The observed fish patterns, though limited, provide valuable direction for future research into the habitat preferences of these fishes.

North American Journal of Fisheries Management