TRAMPR: AN R package for analysis and matching of terminal‐restriction fragment length polymorphism (TRFLP) profiles

trampr (TRFLP analysis and matching package for r ) is a package for matching multiple terminal restriction fragment length polymorphism (TRFLP) profiles between unknown samples and a database of known TRFLP profiles in order to infer the presence of species in environmental samples. It permits simultaneous analysis of multiple samples and facilitates direct workflow from electrophoresis output through to community analyses. trampr also resolves the issues of multiple TRFLP profiles within a species and (conversely) shared TRFLP profiles across species.     

demoniche – an R‐package for simulating spatially‐explicit population dynamics

demoniche is a freely available R‐package which simulates stochastic population dynamics in multiple populations of a species. A demographic model projects population sizes utilizing several transition matrices that can represent impacts on species growth. The demoniche model offers options for setting demographic stochasticity, carrying capacity, and dispersal. The demographic projection in each population is linked to spatially‐explicit niche values, which affect the species growth. With the demoniche package it is possible to compare the influence of scenarios of environmental changes on future population sizes, extinction probabilities, and range shifts of species.     

Ecological partitioning among parapatric cryptic species

Geographic range differences among species may result from differences in their physiological tolerances. In the intertidal zone, marine and terrestrial environments intersect to create a unique habitat, across which physiological tolerance strongly influences range. Traits to cope with environmental extremes are particularly important here because many species live near their physiological limits and environmental gradients can be steep. The snail Melampus bidentatus occurs in coastal salt marshes in the western Atlantic and the Gulf of Mexico. We used sequence data from one mitochondrial (COI) and two nuclear markers (histone H3 and a mitochondrial carrier protein, MCP) to identify three cryptic species within this broad‐ranging nominal species, two of which have partially overlapping geographic ranges. High genetic diversity, low population structure, and high levels of migration within these two overlapping species suggest that historical range limitations do not entirely explain their different ranges. To identify microhabitat differences between these two species, we modelled their distributions using data from both marine and terrestrial environments. Although temperature was the largest factor setting range limits, other environmental components explained features of the ranges that temperature alone could not. In particular, the interaction of precipitation and salinity likely sets physiological limits that lead to range differences between these two cryptic species. This suggests that the response to climatic change in these snails will be mediated by changes to multiple environmental factors, and not just to temperature alone.     

THE EVOLUTION OF ENVIRONMENTAL TOLERANCE AND RANGE SIZE: A COMPARISON OF GEOGRAPHICALLY RESTRICTED AND WIDESPREAD MIMULUS

The geographic ranges of closely related species can vary dramatically, yet we do not fully grasp the mechanisms underlying such variation. The niche breadth hypothesis posits that species that have evolved broad environmental tolerances can achieve larger geographic ranges than species with narrow environmental tolerances. In turn, plasticity and genetic variation in ecologically important traits and adaptation to environmentally variable areas can facilitate the evolution of broad environmental tolerance. We used five pairs of western North American monkeyflowers to experimentally test these ideas by quantifying performance across eight temperature regimes. In four species pairs, species with broader thermal tolerances had larger geographic ranges, supporting the niche breadth hypothesis. As predicted, species with broader thermal tolerances also had more within‐population genetic variation in thermal reaction norms and experienced greater thermal variation across their geographic ranges than species with narrow thermal tolerances. Species with narrow thermal tolerance may be particularly vulnerable to changing climatic conditions due to lack of plasticity and insufficient genetic variation to respond to novel selection pressures. Conversely, species experiencing high variation in temperature across their ranges may be buffered against extinction due to climatic changes because they have evolved tolerance to a broad range of temperatures.     

multimark: an R package for analysis of capture–recapture data consisting of multiple “noninvasive” marks

I describe an open‐source R package, multimark , for estimation of survival and abundance from capture–mark–recapture data consisting of multiple “noninvasive” marks. Noninvasive marks include natural pelt or skin patterns, scars, and genetic markers that enable individual identification in lieu of physical capture. multimark provides a means for combining and jointly analyzing encounter histories from multiple noninvasive sources that otherwise cannot be reliably matched (e.g., left‐ and right‐sided photographs of bilaterally asymmetrical individuals). The package is currently capable of fitting open population Cormack–Jolly–Seber (CJS) and closed population abundance models with up to two mark types using Bayesian Markov chain Monte Carlo (MCMC) methods. multimark can also be used for Bayesian analyses of conventional capture–recapture data consisting of a single‐mark type. Some package features include (1) general model specification using formulas already familiar to most R users, (2) ability to include temporal, behavioral, age, cohort, and individual heterogeneity effects in detection and survival probabilities, (3) improved MCMC algorithm that is computationally faster and more efficient than previously proposed methods, (4) Bayesian multimodel inference using reversible jump MCMC, and (5) data simulation capabilities for power analyses and assessing model performance. I demonstrate use of multimark using left‐ and right‐sided encounter histories for bobcats (Lynx rufus) collected from remote single‐camera stations in southern California. In this example, there is evidence of a behavioral effect (i.e., trap “happy” response) that is otherwise indiscernible using conventional single‐sided analyses. The package will be most useful to ecologists seeking stronger inferences by combining different sources of mark–recapture data that are difficult (or impossible) to reliably reconcile, particularly with the sparse datasets typical of rare or elusive species for which noninvasive sampling techniques are most commonly employed. Addressing deficiencies in currently available software, multimark also provides a user‐friendly interface for performing Bayesian multimodel inference using capture–recapture data consisting of a single conventional mark or multiple noninvasive marks.     

The MIGCLIM R package – seamless integration of dispersal constraints into projections of species distribution models

The MIGCLIM R package is a function library for the open source R software that enables the implementation of species‐specific dispersal constraints into projections of species distribution models under environmental change and/or landscape fragmentation scenarios. The model is based on a cellular automaton and the basic modeling unit is a cell that is inhabited or not. Model parameters include dispersal distance and kernel, long distance dispersal, barriers to dispersal, propagule production potential and habitat invasibility. The MIGCLIM R package has been designed to be highly flexible in the parameter values it accepts, and to offer good compatibility with existing species distribution modeling software. Possible applications include the projection of future species distributions under environmental change conditions and modeling the spread of invasive species.     

Local adaptation across a complex bioclimatic landscape in two montane bumble bee species

Understanding evolutionary responses to variation in temperature and precipitation across species ranges is of fundamental interest given ongoing climate change. The importance of temperature and precipitation for multiple aspects of bumble bee (Bombus) biology, combined with large geographic ranges that expose populations to diverse environmental pressures, make these insects well‐suited for studying local adaptation. Here, we analyzed genome‐wide sequence data from two widespread bumble bees, Bombus vosnesenskii and Bombus vancouverensis, using multiple environmental association analysis methods to investigate climate adaptation across latitude and altitude. The strongest signatures of selection were observed in B. vancouverensis, but despite unique responses between species for most loci, we detected several shared responses. Genes relating to neural and neuromuscular function and ion transport were especially evident with respect to temperature variables, while genes relating to cuticle formation, tracheal and respiratory system development, and homeostasis were associated with precipitation variables. Our data thus suggest that adaptive responses for tolerating abiotic variation are likely to be complex, but that several parallels among species can emerge even for these complex traits and landscapes. Results provide the framework for future work into mechanisms of thermal and desiccation tolerance in bumble bees and a set of genomic targets that might be monitored for future conservation efforts.     

HomeRange: A global database of mammalian home ranges

Motivation

Home range is a common measure of use of space by animals because it provides ecological information that is useful for conservation applications. In macroecological studies, values are typically aggregated to species means to examine general patterns of use of space by animals. However, this ignores the environmental context in which the home range was estimated and does not account for intraspecific variation in home range size. In addition, the focus of macroecological studies on home ranges has historically been biased towards terrestrial mammals. The use of aggregated numbers and the terrestrial focus limit our ability to examine home-range patterns across different environments, their variation in time and variation between different levels of organization. Here, we introduce HomeRange, a global database with 75,611 home-range values across 960 different species of mammals, including terrestrial, aquatic and aerial species.

Main types of variables contained

The dataset contains estimates of home ranges of mammals, species names, methodological information on data collection, method of home-range estimation, period of data collection, study coordinates and name of location, in addition to species traits derived from the studies, such as body mass, life stage, reproductive status and locomotor habit.

Spatial location and grain

The collected data are distributed globally. Across studies, the spatial accuracy varies, with the coarsest resolution being 1°.

Time period and grain

The data represent information published between 1939 and 2022. Across studies, the temporal accuracy varies; some studies report start and end dates specific to the day, whereas for other studies only the month or year is reported.

Major taxa and level of measurement

Mammalian species from 24 of the 27 different taxonomic orders. Home-range estimates range from individual-level values to population-level averages.

Software format

Data are supplied as a comma-delimited text file (.csv) and can be loaded directly into R using the “HomeRange” R package ( https://github.com/SHoeks/HomeRange ).     

sars: an R package for fitting,evaluating and comparing species–area relationship models

The species–area relationship (SAR) constitutes one of the most general ecological patterns globally. A number of different SAR models have been proposed. Recent work has shown that no single model universally provides the best fit to empirical SAR datasets: multiple models may be of practical and theoretical interest. However, there are no software packages available that a) allow users to fit the full range of published SAR models, or b) provide functions to undertake a range of additional SAR‐related analyses. To address these needs, we have developed the R package ‘sars’ that provides a wide variety of SAR‐related functionality. The package provides functions to: a) fit 20 SAR models using non‐linear and linear regression, b) calculate multi‐model averaged curves using various information criteria, and c) generate confidence intervals using bootstrapping. Plotting functions allow users to depict and scrutinize the fits of individual models and multi‐model averaged curves. The package also provides additional SAR functionality, including functions to fit, plot and evaluate the random placement model using a species–sites abundance matrix, and to fit the general dynamic model of oceanic island biogeography. The ‘sars’ R package will aid future SAR research by providing a comprehensive set of simple to use tools that enable in‐depth exploration of SARs and SAR‐related patterns. The package has been designed to allow other researchers to add new functions and models in the future and thus the package represents a resource for future SAR work that can be built on and expanded by workers in the field.     

helminthR: an R interface to the London Natural History Museum's Host–Parasite Database

The understanding of the diversity and distribution of helminth parasites is currently constrained by the limited number of host–parasite interaction databases, and the difficulty in accessing existing data. The London Natural History Museum's Host–Parasite Database represents one such underutilized database, containing over a quarter million helminth parasite occurrence records, accessible through a web interface. To enable users to programmatically search and manipulate data from this database, I developed an R package called helminthR. Here, I introduce the core functions of the package, and detail how helminthR can be used to obtain host–parasite interaction records, citations for interactions, and host taxonomic data.     

TreeGOER: A database with globally observed environmental ranges for 48,129 tree species

The TreeGOER (Tree Globally Observed Environmental Ranges) database provides information for most known tree species of their environmental ranges for 38 bioclimatic, eight soil and three topographic variables. It is based on species distribution modelling analyses of more than 44 million occurrences. The database can be accessed from https://doi.org/10.5281/zenodo.7922927 . Statistics that include 5% and 95% quantiles were estimated for a cleaned and taxonomically standardized occurrence data set with different methods of outlier detection, with estimates for roughly 45% of species being based on 20 or more observation records. Where sufficient representative observations are available, the ranges provide useful preliminary estimates of suitable conditions particularly for lesser-known species under climate change. Inferred core bioclimatic ranges of species along global temperature and moisture index gradients and across continents follow the known global distribution of tree diversity such as its highest levels in moist tropical forests and the ‘odd man out’ pattern of lower levels in Africa. To demonstrate how global analyses for large numbers of tree species can easily be done in R with TreeGOER , here I present two case studies. The first case study investigated latitudinal trends of tree vulnerability and compared these with previous results obtained for urban trees. The second case study focused on tropical areas, compared trends in different longitudinal zones and investigated patterns for the moisture index. TreeGOER is expected to benefit researchers conducting biogeographical and climate change research for a wide range of tree species at a variety of spatial and temporal scales.     

An evolutionary tolerance model explaining spatial patterns in species richness under environmental gradients and geometric constraints

In this study, we developed a simulation model based on the ecological and evolutionary dynamics of geographical ranges, to understand the role of species' environmental tolerances and the strength of the environmental gradient in determining spatial patterns in species richness. Using an one-dimensional space, we present the model and dissect its parameters. Also, we test the ability of the model to simulate richness in complex two-dimensional domains and to fit real patterns in species richness, using South American Tyrannidae as an example. We found that a mid-spatial peak in species richness arises spontaneously under conditions of high environmental tolerances and/or a weak environmental gradient, since this condition causes wide species' geographic ranges, which are constrained by domain's boundary and tend to overlap in the middle. Our model was also a good predictor of real patterns in species richness, especially under conditions of high environmental strength and small species' tolerance. We conclude that this kind of spatial simulation models based on species' physiological tolerance may be an important tool to understand the evolutionary dynamics of species' geographic ranges and in spatial patterns of species richness.     

The Relationship between Local Abundance and Distribution of Rain Forest Trees across Environmental Gradients in India

We tested whether local abundance of rain forest trees in the medium elevation wet forests of the southern Western Ghats (WG) was related to environmental tolerance, life form, and geographical range. We selected trees in medium elevation wet forests (750–1700 m asl) of the southern WG, using two data bases: a small plot (30 × 30 m) data base of 288 species of trees (≥ 3 cm dbh) in 33 plots totaling 2.97 ha, and a data base of 135 species of tree (≥ 10 cm dbh) in larger plots of 1 ha each, totaling 4.84 ha. The species density per hectare and number of records in the plot network was used in a factor analysis to give a measure of the local abundance of each species. The altitude and seasonality ranges of these species in the WG was assessed from independent data bases and used to generate an environmental tolerance score. Results indicated that as a species became locally more abundant, it occurred across a wider range of environmental gradients, but regional distribution was not related to geographical distribution. Understory species tended to be rarer with smaller range sizes and lower environmental tolerances than overstory species. Climate change is predicted to have drastic effects on restricted range species with limited environmental tolerances.     

Opening the black box: an open‐source release of Maxent

This software note announces a new open‐source release of the Maxent software for modeling species distributions from occurrence records and environmental data, and describes a new R package for fitting such models. The new release (ver. 3.4.0) will be hosted online by the American Museum of Natural History, along with future versions. It contains small functional changes, most notably use of a complementary log‐log (cloglog) transform to produce an estimate of occurrence probability. The cloglog transform derives from the recently‐published interpretation of Maxent as an inhomogeneous Poisson process (IPP), giving it a stronger theoretical justification than the logistic transform which it replaces by default. In addition, the new R package, maxnet, fits Maxent models using the glmnet package for regularized generalized linear models. We discuss the implications of the IPP formulation in terms of model inputs and outputs, treating occurrence records as points rather than grid cells and interpreting the exponential Maxent model (raw output) as as an estimate of relative abundance. With these two open‐source developments, we invite others to freely use and contribute to the software.     

ecospat: an R package to support spatial analyses and modeling of species niches and distributions

The aim of the ecospat package is to make available novel tools and methods to support spatial analyses and modeling of species niches and distributions in a coherent workflow. The package is written in the R language (R Development Core Team) and contains several features, unique in their implementation, that are complementary to other existing R packages. Pre‐modeling analyses include species niche quantifications and comparisons between distinct ranges or time periods, measures of phylogenetic diversity, and other data exploration functionalities (e.g. extrapolation detection, ExDet). Core modeling brings together the new approach of ensemble of small models (ESM) and various implementations of the spatially‐explicit modeling of species assemblages (SESAM) framework. Post‐modeling analyses include evaluation of species predictions based on presence‐only data (Boyce index) and of community predictions, phylogenetic diversity and environmentally‐constrained species co‐occurrences analyses. The ecospat package also provides some functions to supplement the ‘biomod2’ package (e.g. data preparation, permutation tests and cross‐validation of model predictive power). With this novel package, we intend to stimulate the use of comprehensive approaches in spatial modelling of species and community distributions.     

Identifying climatic niche shifts using coarse‐grained occurrence data: a test with non‐native freshwater fish

Aim We tested whether coarse‐grained occurrence data can be used to detect climatic niche shifts between native and non‐native ranges for a set of widely introduced freshwater fishes. Location World‐wide. Methods We used a global database of freshwater fish occurrences at the river basin scale to identify native and non‐native ranges for 18 of the most widely introduced fish species. We also examined climatic conditions within each river basin using fine‐grained climate data. We combined this information to test whether climatic niche shifts have occurred between native and non‐native ranges. We defined climatic niche shifts as instances where the ranges of a climatic variable within native and non‐native basins exhibit zero overlap. Results We detected at least one climatic niche shift for each of the 18 studied species. However, we did not detect common patterns in the thermal preference or biogeographic origin of the non‐native fish, hence suggesting a species‐specific response. Main conclusions Coarse‐grained occurrence data can be used to detect climatic niche shifts. They also enable the identification of the species experiencing niche shifts, although the mechanisms responsible for these shifts (e.g. local adaptation, dispersal limitation or physiological constraints) have yet to be determined. Furthermore, the coarse‐grained approach, which highlights regions where climatic niche shifts have occurred, can be used to select specific river basins for more detailed, fine‐grained studies.     

Identifying models of trait‐mediated community assembly using random forests and approximate Bayesian computation

Ecologists often use dispersion metrics and statistical hypothesis testing to infer processes of community formation such as environmental filtering, competitive exclusion, and neutral species assembly. These metrics have limited power in inferring assembly models because they rely on often‐violated assumptions. Here, we adapt a model of phenotypic similarity and repulsion to simulate the process of community assembly via environmental filtering and competitive exclusion, all while parameterizing the strength of the respective ecological processes. We then use random forests and approximate Bayesian computation to distinguish between these models given the simulated data. We find that our approach is more accurate than using dispersion metrics and accounts for uncertainty in model selection. We also demonstrate that the parameter determining the strength of the assembly processes can be accurately estimated. This approach is available in the R package CAMI; Community Assembly Model Inference. We demonstrate the effectiveness of CAMI using an example of plant communities living on lava flow islands.     

multi‐dice: r package for comparative population genomic inference under hierarchical co‐demographic models of independent single‐population size changes

Population genetic data from multiple taxa can address comparative phylogeographic questions about community‐scale response to environmental shifts, and a useful strategy to this end is to employ hierarchical co‐demographic models that directly test multi‐taxa hypotheses within a single, unified analysis. This approach has been applied to classical phylogeographic data sets such as mitochondrial barcodes as well as reduced‐genome polymorphism data sets that can yield 10,000s of SNPs, produced by emergent technologies such as RAD‐seq and GBS. A strategy for the latter had been accomplished by adapting the site frequency spectrum to a novel summarization of population genomic data across multiple taxa called the aggregate site frequency spectrum (aSFS), which potentially can be deployed under various inferential frameworks including approximate Bayesian computation, random forest and composite likelihood optimization. Here, we introduce the r package multi‐dice , a wrapper program that exploits existing simulation software for flexible execution of hierarchical model‐based inference using the aSFS, which is derived from reduced genome data, as well as mitochondrial data. We validate several novel software features such as applying alternative inferential frameworks, enforcing a minimal threshold of time surrounding co‐demographic pulses and specifying flexible hyperprior distributions. In sum, multi‐dice provides comparative analysis within the familiar R environment while allowing a high degree of user customization, and will thus serve as a tool for comparative phylogeography and population genomics.     

Environmental variability and biogeography: the relationship between bathymetric distribution and geographical range size in marine algae and gastropods

Aims Gradients of environmental variability have been proposed to explain spatial variation in patterns of geographical range size. We explore this relationship in NE Pacific algae and NW Atlantic gastropods by using the characteristics of species’ bathymetric distributions as a proxy for tolerance of environmental variability. Location NE Pacific and NW Atlantic. Methods Data on species bathymetric and geographical distributions were compiled from the literature. Results For both algae and gastropods, species that inhabit highly seasonal, shallow depth zones have broader latitudinal ranges, and occupy more biogeographical provinces, than species that live in more temporally stable, deeper zones. Furthermore, species that tolerate spatial variability along the bathymetric axis, i.e. those that occur in multiple depth zones, have broader geographical ranges than species restricted to fewer depth zones. Main conclusions Within‐range environmental variability, through both space and time, is predictive of large geographical ranges for marine algae and gastropods. Analysis of species distributions across perpendicular gradients (e.g. depth and latitude) is a powerful approach to discerning the mechanisms that govern biogeographical patterns, and provides easily obtainable broad‐brush predictions regarding the biogeographical outcomes of global change.