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USGS2025

How to model a new invader? US-invaded range models outperform global or combined range models after 100 occurrences

Invasive species are an economic and ecological burden, and efforts to limit their impact are greatly improved with reliable maps based on species distribution models (SDMs). However, the potential distribution of new invaders is difficult to anticipate because they are still spreading with few observations in their invaded habitat. Therefore, an accepted practice in predicting the distribution of invasive species has been to incorporate habitat information from its entire geographic distribution (invaded and native ranges) into SDMs. Yet, this approach, due to niche shifts, niche expansions, and data deficiencies, commonly misrepresents where an invasive species is found in its new range. Here, we use time series records (invasion stages) from 13 invasive plant species in North America to explore the tension between modeling invasive species using global range and invaded range data and to determine if there is a “tipping point” at which one SDM strategy performs better than the other in predicting the ultimate distribution. At the earliest invasion stage, models developed using both invaded range and global occurrences on average performed better and had less variability across species than other model strategies at this stage. However, after as few as 100 observations of an invasive plant had been made, US-invaded range models, on average, outperformed global range models and models that combined occurrences. By building models with global and US-scale predictors, we show that higher performance of invaded range models was in part because of greater data quality at the invaded-range scale. Our work demonstrates that after relatively few observations of an invasive species in its invaded range, it is more accurate to model its potential distribution using only information from the invaded range while disregarding information from other regions. This work develops a robust and comprehensive approach to modeling novel distributions of newly observed invasive species.

North America↗

USGS2013

Predicting what helminth parasites a fish species should have using Parasite Co-occurrence Modeler (PaCo)

Fish pathologists are often interested in which parasites would likely be present in a particular host. Parasite Co-occurrence Modeler (PaCo) is a tool for identifying a list of parasites known from fish species that are similar ecologically, phylogenetically, and geographically to the host of interest. PaCo uses data from FishBase (maximum length, growth rate, life span, age at maturity, trophic level, phylogeny, and biogeography) to estimate compatibility between a target host and parasite species–genera from the major helminth groups (Acanthocephala, Cestoda, Monogenea, Nematoda, and Trematoda). Users can include any combination of host attributes in a model. These unique features make PaCo an innovative tool for addressing both theoretical and applied questions in parasitology. In addition to predicting the occurrence of parasites, PaCo can be used to investigate how host characteristics shape parasite communities. To test the performance of the PaCo algorithm, we created 12,400 parasite lists by applying any possible combination of model parameters (248) to 50 fish hosts. We then measured the relative importance of each parameter by assessing their frequency in the best models for each host. Host phylogeny and host geography were identified as the most important factors, with both present in 88% of the best models. Habitat (64%) was identified in more than half of the best models. Among ecological parameters, trophic level (41%) was the most relevant while life span (34%), growth rate (32%), maximum length (28%), and age at maturity (20%) were less commonly linked to best models. PaCo is free to use at www.purl.oclc.org/fishpest .

Journal of Parasitology↗

USGS2019

Brook trout (Salvelinus fontinalis) movement and survival after removal of two dams on the West Branch of the Wolf River, Wisconsin

Dam removals allow fish to access habitats that may provide ecological benefits and risks, but the extent of fish movements through former dam sites has not been thoroughly evaluated for many species. We installed stationary PIT antennas in 2016 and 2017 to evaluate movements and survival of brook trout Salvelinus fontinalis in the West Branch of the Wolf River (WBWR) in central Wisconsin following removal of two dams and channel modifications designed to promote fish movement. These changes provided access to lacustrine habitats that might provide suitable winter habitat or act as ecological sinks. We used multistate models to estimate transition probabilities between river sections, to determine whether brook trout: (a) moved between multiple river sections and (b) entered lacustrine habitats as seasonal refuges, but eventually returned to lotic habitat. We also used a Cormack-Jolly-Seber model to evaluate whether apparent survival of brook trout in the WBWR was comparable to other populations. Few fish moved among river sections or used lacustrine habitat (<5% of tagged fish); most brook trout remained in sections where they were initially tagged, potentially due to quality habitat located throughout the river. Like other studies, brook trout in the WBWR appear to experience high mortality based on low number of detections, few physical recaptures and an estimated eight-month apparent survival rate of 0.27. In scenarios where fish can already access suitable habitat, removal of dams may not result in substantial increases in fish movement and colonisation of newly accessible habitat may not occur immediately.

Wisconsin↗

USGS2008

Modelling invasion for a habitat generalist and a specialist plant species

Predicting suitable habitat and the potential distribution of invasive species is a high priority for resource managers and systems ecologists. Most models are designed to identify habitat characteristics that define the ecological niche of a species with little consideration to individual species' traits. We tested five commonly used modelling methods on two invasive plant species, the habitat generalist Bromus tectorum and habitat specialist Tamarix chinensis, to compare model performances, evaluate predictability, and relate results to distribution traits associated with each species. Most of the tested models performed similarly for each species; however, the generalist species proved to be more difficult to predict than the specialist species. The highest area under the receiver-operating characteristic curve values with independent validation data sets of B. tectorum and T. chinensis was 0.503 and 0.885, respectively. Similarly, a confusion matrix for B. tectorum had the highest overall accuracy of 55%, while the overall accuracy for T. chinensis was 85%. Models for the generalist species had varying performances, poor evaluations, and inconsistent results. This may be a result of a generalist's capability to persist in a wide range of environmental conditions that are not easily defined by the data, independent variables or model design. Models for the specialist species had consistently strong performances, high evaluations, and similar results among different model applications. This is likely a consequence of the specialist's requirement for explicit environmental resources and ecological barriers that are easily defined by predictive models. Although defining new invaders as generalist or specialist species can be challenging, model performances and evaluations may provide valuable information on a species' potential invasiveness.

Diversity and Distributions↗

USGS2018

Submerged aquatic vegetation mapping in coastal Louisiana through development of a spatial likelihood occurrence (SLOO) model

Determining the spatial distribution of coastal foundation species is essential to accurately determine restoration goals, predict the ecological effects of climate change, and develop habitat management strategies. Mapping the distribution of submerged aquatic vegetation (SAV) species assemblages, which provide important habitat resource and ecological services in Louisiana, has been difficult due to the dynamic nature of SAV occurrence and the limited water clarity across much of the coast. Species distribution models (SDMs) link ecological conditions species occurrence across landscapes, and can predict the distribution of species across un-sampled or hard to sample areas and support the development of habitat maps. To predict SAV distribution in coastal Louisiana, a SDM was developed and projected across the landscape to create a spatial likelihood of occurrence (SLOO) model describing the probability of SAV presence in aquatic habitats. SAV presence and absence data were examined from over 500 field observations in relation to physical and hydrologic variables, including exposure, turbidity, water level, and salinity. A binary logistic regression model (p < 0.0001) identified three significant predictors of SAV presence: mean winter salinity, exposure, and turbidity. As each of these variables increased, the probability of SAV presence in the summer growing season decreased. The spatial application of this SDM helps to predict the likelihood of occurrence across the coastal landscape, creating a valuable tool to describe un-sampled SAV habitat and estimate future changes in habitat availability.

Louisiana↗

USGS2005

Nutritional condition of elk in rocky mountain national park

We tested the hypothesis that elk in Rocky Mountain National Park (RMNP) were at ecological carrying capacity by determining herd-specific levels of nutritional condition and fecundity. Ingesta-free body fat levels in adult cows that were lactating were 10.6% (s = 1.7; range = 6.2-15.4) and 7.7% (s = 0.5; range = 5.9-10.1) in November 2001 for the Horseshoe and Moraine Park herds, respectively. Cows that were not lactating were able to accrue significantly more body fat: 14.0% (s = 1.1; range = 7.7-19.3) and 11.5% (s = 0.8; range = 8.6-15.1) for the Horseshoe and Moraine Park herds, respectively. Cow elk lost most of their body fat over winter (April 2002 levels were 3.9% [s = 0.4] and 2.9% [s = 0.4] for the Horseshoe and Moraine Park herds, respectively). Nutritional condition indicated that both Horseshoe Park and Moraine Park elk were well below condition levels elk can achieve on very good-excellent nutrition (i.e., >15% body fat; Cook et al. 2004) and were comparable to other free-ranging elk populations. However, condition levels were higher than those expected at a "food-limited" carrying capacity, and a proportion of elk in each herd were able to achieve condition levels indicative of very good-excellent nutrition. Elk in RMNP are likely regulated and/or limited by a complex combination of density-independent (including significant heterogeneity in forage conditions across RMNP's landscape) and density-dependent processes, as condition levels contradict a simple density-dependent model of a population at ecological carrying capacity.

Western North American Naturalist↗

USGS2012

Ecology of zoonotic infectious diseases in bats: current knowledge and future directions

Bats are hosts to a range of zoonotic and potentially zoonotic pathogens. Human activities that increase exposure to bats will likely increase the opportunity for infections to spill over in the future. Ecological drivers of pathogen spillover and emergence in novel hosts, including humans, involve a complex mixture of processes, and understanding these complexities may aid in predicting spillover. In particular, only once the pathogen and host ecologies are known can the impacts of anthropogenic changes be fully appreciated. Cross-disciplinary approaches are required to understand how host and pathogen ecology interact. Bats differ from other sylvatic disease reservoirs because of their unique and diverse lifestyles, including their ability to fly, often highly gregarious social structures, long lifespans and low fecundity rates. We highlight how these traits may affect infection dynamics and how both host and pathogen traits may interact to affect infection dynamics. We identify key questions relating to the ecology of infectious diseases in bats and propose that a combination of field and laboratory studies are needed to create data-driven mechanistic models to elucidate those aspects of bat ecology that are most critical to the dynamics of emerging bat viruses. If commonalities can be found, then predicting the dynamics of newly emerging diseases may be possible. This modelling approach will be particularly important in scenarios when population surveillance data are unavailable and when it is unclear which aspects of host ecology are driving infection dynamics.

Zoonoses and Public Health↗

USGS2008

Understanding and predicting ecological dynamics: Are major surprises inevitable

Ecological surprises, substantial and unanticipated changes in the abundance of one or more species that result from previously unsuspected processes, are a common outcome of both experiments and observations in community and population ecology. Here, we give examples of such surprises along with the results of a survey of well-established field ecologists, most of whom have encountered one or more surprises over the course of their careers. Truly surprising results are common enough to require their consideration in any reasonable effort to characterize nature and manage natural resources. We classify surprises as dynamic-, pattern-, or intervention-based, and we speculate on the common processes that cause ecological systems to so often surprise us. A long-standing and still growing concern in the ecological literature is how best to make predictions of future population and community dynamics. Although most work on this subject involves statistical aspects of data analysis and modeling, the frequency and nature of ecological surprises imply that uncertainty cannot be easily tamed through improved analytical procedures, and that prudent management of both exploited and conserved communities will require precautionary and adaptive management approaches.

Ecology↗

USGS2016

Estimating the effects of 17α-ethinylestradiol on stochastic population growth rate of fathead minnows: a population synthesis of empirically derived vital rates

Urban freshwater streams in arid climates are wastewater effluent dominated ecosystems particularly impacted by bioactive chemicals including steroid estrogens that disrupt vertebrate reproduction. However, more understanding of the population and ecological consequences of exposure to wastewater effluent is needed. We used empirically derived vital rate estimates from a mesocosm study to develop a stochastic stage-structured population model and evaluated the effect of 17α-ethinylestradiol (EE2), the estrogen in human contraceptive pills, on fathead minnow Pimephales promelas stochastic population growth rate. Tested EE2 concentrations ranged from 3.2 to 10.9 ng L −1 and produced stochastic population growth rates (λ S ) below 1 at the lowest concentration, indicating potential for population decline. Declines in λ S compared to controls were evident in treatments that were lethal to adult males despite statistically insignificant effects on egg production and juvenile recruitment. In fact, results indicated that λ S was most sensitive to the survival of juveniles and female egg production. More broadly, our results document that population model results may differ even when empirically derived estimates of vital rates are similar among experimental treatments, and demonstrate how population models integrate and project the effects of stressors throughout the life cycle. Thus, stochastic population models can more effectively evaluate the ecological consequences of experimentally derived vital rates.

Ecotoxicology↗

USGS2007

Application of a coupled ecosystem-chemical equilibrium model, DayCent-Chem, to stream and soil chemistry in a Rocky Mountain watershed

Atmospheric deposition of sulfur and nitrogen species have the potential to acidify terrestrial and aquatic ecosystems, but nitrate and ammonium are also critical nutrients for plant and microbial productivity. Both the ecological response and the hydrochemical response to atmospheric deposition are of interest to regulatory and land management agencies. We developed a non-spatial biogeochemical model to simulate soil and surface water chemistry by linking the daily version of the CENTURY ecosystem model (DayCent) with a low temperature aqueous geochemical model, PHREEQC. The coupled model, DayCent-Chem, simulates the daily dynamics of plant production, soil organic matter, cation exchange, mineral weathering, elution, stream discharge, and solute concentrations in soil water and stream flow. By aerially weighting the contributions of separate bedrock/talus and tundra simulations, the model was able to replicate the measured seasonal and annual stream chemistry for most solutes for Andrews Creek in Loch Vale watershed, Rocky Mountain National Park. Simulated soil chemistry, net primary production, live biomass, and soil organic matter for forest and tundra matched well with measurements. This model is appropriate for accurately describing ecosystem and surface water chemical response to atmospheric deposition and climate change. ?? 2006 Elsevier B.V. All rights reserved.

Ecological Modelling↗

USGS2025

Predicting invasiveness of freshwater fishes imported into North America: Regional differences in models and outcomes

Biological invasions driven by international trade heighten the urgency for development of invasion risk models, as the traits and parameters that consistently predict successful invasion remain unresolved. For four regions of North America that include parts of the United States and Canada (Sacramento-San Joaquin River Basins, Lower Colorado River Basin, Great Lakes Region, Mid-Atlantic Region), we construct and compare classification tree models to reveal robust predictors for the establishment and ecological impact stages of freshwater fish invasion. We subsequently apply the models to identify invasive fish species in trade and conduct pathway analyses to determine which trades (aquarium, biological supply, live bait, live food, water garden) and source continents pose the greatest risk to each region. Model results differed by invasion stage and region. Across regions, establishment models shared climate-related predictors including climate match and temperature tolerance. Three of the four impact models contained prior establishment success. The greatest number of species (548) were predicted to establish in the Sacramento-San Joaquin while the fewest (5) were predicted to establish in the Mid-Atlantic. Forty species were predicted to establish in multiple regions, five of which were also predicted to have high impact. The aquarium trade and Asia supplied the most species predicted to establish. Taken together, the results highlight region-specific models, indicating no universal model predicts invasion. Climate-related and prior establishment variables were most useful to risk assessments. The regional models, and identified high-risk pathways and potential invaders, could be applied to prevent future fish invasions in North America.

Laurentian Great Lakes Region, Lower Colorado Rive↗

USGS2021

At-sea feeding ecology of parasitic lampreys

This paper synthesizes information on the at-sea ecology of ten anadromous lampreys, with emphasis on trophic ecology. The at-sea ecology of these lampreys concerns the juvenile stage, in which growth is most rapid. Anadromous lampreys can be categorized into four groups, based on feeding modalities: 1) scavenger (Caspian lamprey, Caspiomyzon wagneri ); 2) parasite-predator (Pacific lamprey, Entosphenus tridentatus ); 3) predators (western river lamprey, Lampetra ayresii ; European river lamprey, L. fluviatilis ; Arctic lamprey, Lethenteron camtschaticum ; pouched lamprey, Geotria australis ; and Argentinian pouched lamprey, G. macrostoma ); and 4) parasites (sea lamprey, Petromyzon marinus ; Chilean lamprey, Mordacia lapicida ; and short-headed lamprey, M. mordax ). This paper discusses direct evidence for lamprey feeding ecology, as observed through lamprey-induced wounds on hosts and prey, and lamprey attachments on hosts and prey; and indirect evidence for feeding ecology, via analyses of fatty acids, stable isotopes, contaminants, and bioenergetics modelling. A part of the information presented on feeding ecology is from landlocked sea lamprey, and in some instances this information can be generalizable to anadromous populations. For most anadromous lampreys, but particularly for Southern Hemisphere taxa, little is known about their feeding ecology at sea. Duration of the trophic marine phase and habitat use are still subjects of debate. Species identified as lamprey hosts can be demersal or pelagic, possibly reflecting marine habitat preferences. To unlock understanding of the marine phase of anadromous lampreys, direct evidence of feeding ecology should be coupled with natural (i.e., biomarkers) and artificial (e.g., biologgers) markers to identify habitat use, movement patterns and dispersal.

Journal of Great Lakes Research↗

USGS2011

Floristic composition, beta diversity, and nestedness of reference sites for restoration of xeroriparian areas

In re storation ecology, reference sites serve as models for areas to be restored and can provide a standard of comparison for restoration project outcomes. When reference sites are located a relatively long distance from associated restoration projects, differences in climate, disturbance history, and biogeography can increase beta diversity and may decrease the relevance of reference sites. Variation in factors at the scale of individual reference sites such as patch size, microclimate, barriers to dispersal, or soil chemistry can result in reference site species composition that is a nested subset of the regional species pool. In the western United States, restoration of riparian areas, particularly those occupied by Tamarix spp., has become a priority; however, little is known about suitable native replacement vegetation communities for relatively dry and saline riparian terraces that comprise many of the sites where Tamarix is removed prior to restoration activities. We studied plant communities on riparian terraces along five rivers in New Mexico, USA, to (1) determine whether the floristic composition of reference sites can be predicted by easily measured soil variables such as pH, salinity (electric conductivity), and texture; (2) examine the extent of distance decay in the compositional similarity of xeroriparian plant communities in the southwestern United States; and (3) determine the degree of nestedness in xeroriparian plant communities in relationship to soil variables. We found that sites clustered into groups based largely on variation in soil salinity and texture. Vegetation across all sites was highly nested with dominant, salt‐tolerant species found on most soil groups and salt‐intolerant subordinate species restricted to low‐salinity soils. The identity of subordinate species was largely site dependent, causing all sites to have the same low degree of similarity regardless of the distance between them. We conclude that, when planning restoration projects on dry and saline riparian sites, soil salinity and texture are good predictors of which species will be most suited to the area being restored, but a candidate species pool should be developed from the nearest possible reference sites, particularly for subordinate species.

New Mexico↗

USGS2012

Application of empirical predictive modeling using conventional and alternative fecal indicator bacteria in eastern North Carolina waters

Coastal and estuarine waters are the site of intense anthropogenic influence with concomitant use for recreation and seafood harvesting. Therefore, coastal and estuarine water quality has a direct impact on human health. In eastern North Carolina (NC) there are over 240 recreational and 1025 shellfish harvesting water quality monitoring sites that are regularly assessed. Because of the large number of sites, sampling frequency is often only on a weekly basis. This frequency, along with an 18–24 h incubation time for fecal indicator bacteria (FIB) enumeration via culture-based methods, reduces the efficiency of the public notification process. In states like NC where beach monitoring resources are limited but historical data are plentiful, predictive models may offer an improvement for monitoring and notification by providing real-time FIB estimates. In this study, water samples were collected during 12 dry (n = 88) and 13 wet (n = 66) weather events at up to 10 sites. Statistical predictive models for Escherichiacoli (EC), enterococci (ENT), and members of the Bacteroidales group were created and subsequently validated. Our results showed that models for EC and ENT (adjusted R2 were 0.61 and 0.64, respectively) incorporated a range of antecedent rainfall, climate, and environmental variables. The most important variables for EC and ENT models were 5-day antecedent rainfall, dissolved oxygen, and salinity. These models successfully predicted FIB levels over a wide range of conditions with a 3% (EC model) and 9% (ENT model) overall error rate for recreational threshold values and a 0% (EC model) overall error rate for shellfish threshold values. Though modeling of members of the Bacteroidales group had less predictive ability (adjusted R 2 were 0.56 and 0.53 for fecal Bacteroides spp. and human Bacteroides spp., respectively), the modeling approach and testing provided information on Bacteroidales ecology. This is the first example of a set of successful statistical predictive models appropriate for assessment of both recreational and shellfish harvesting water quality in estuarine waters.

North Carolina↗

USGS2016

Predicting animal home-range structure and transitions using a multistate Ornstein-Uhlenbeck biased random walk

The home‐range concept is central in animal ecology and behavior, and numerous mechanistic models have been developed to understand home range formation and maintenance. These mechanistic models usually assume a single, contiguous home range. Here we describe and implement a simple home‐range model that can accommodate multiple home‐range centers, form complex shapes, allow discontinuities in use patterns, and infer how external and internal variables affect movement and use patterns. The model assumes individuals associate with two or more home‐range centers and move among them with some estimable probability. Movement in and around home‐range centers is governed by a two‐dimensional Ornstein‐Uhlenbeck process, while transitions between centers are modeled as a stochastic state‐switching process. We augmented this base model by introducing environmental and demographic covariates that modify transition probabilities between home‐range centers and can be estimated to provide insight into the movement process. We demonstrate the model using telemetry data from sea otters ( Enhydra lutris ) in California. The model was fit using a Bayesian Markov Chain Monte Carlo method, which estimated transition probabilities, as well as unique Ornstein‐Uhlenbeck diffusion and centralizing tendency parameters. Estimated parameters could then be used to simulate movement and space use that was virtually indistinguishable from real data. We used Deviance Information Criterion (DIC) scores to assess model fit and determined that both wind and reproductive status were predictive of transitions between home‐range centers. Females were less likely to move between home‐range centers on windy days, less likely to move between centers when tending pups, and much more likely to move between centers just after weaning a pup. These tendencies are predicted by theoretical movement rules but were not previously known and show that our model can extract meaningful behavioral insight from complex movement data.

Ecology↗

USGS2024

Integrated distance sampling models for simple point counts

Point counts (PCs) are widely used in biodiversity surveys but, despite numerous advantages, simple PCs suffer from several problems: detectability, and therefore abundance, is unknown; systematic spatiotemporal variation in detectability yields biased inferences, and unknown survey area prevents formal density estimation and scaling-up to the landscape level. We introduce integrated distance sampling (IDS) models that combine distance sampling (DS) with simple PC or detection/nondetection (DND) data to capitalize on the strengths and mitigate the weaknesses of each data type. Key to IDS models is the view of simple PC and DND data as aggregations of latent DS surveys that observe the same underlying density process. This enables the estimation of separate detection functions, along with distinct covariate effects, for all data types. Additional information from repeat or time-removal surveys, or variable survey duration, enables the separate estimation of the availability and perceptibility components of detectability with DS and PC data. IDS models reconcile spatial and temporal mismatches among data sets and solve the above-mentioned problems of simple PC and DND data. To fit IDS models, we provide JAGS code and the new “ IDS() ” function in the R package unmarked . Extant citizen-science data generally lack the information necessary to adjust for detection biases, but IDS models address this shortcoming, thus greatly extending the utility and reach of these data. In addition, they enable formal density estimation in hybrid designs, which efficiently combine DS with distance-free, point-based PC or DND surveys. We believe that IDS models have considerable scope in ecology, management, and monitoring.

Ecology↗

USGS2020

Zero or not? Causes and consequences of zero-flow stream gage readings

Streamflow observations can be used to understand, predict, and contextualize hydrologic, ecological, and biogeochemical processes and conditions in streams. Stream gages are point measurements along rivers where streamflow is measured, and are often used to infer upstream watershed‐scale processes. When stream gages read zero, this may indicate that the stream has dried at this location; however, zero‐flow readings can also be caused by a wide range of other factors. Our ability to identify whether or not a zero‐flow gage reading indicates a dry fluvial system has far reaching environmental implications. Incorrect identification and interpretation by the data user can lead to inaccurate hydrologic, ecological, and/or biogeochemical predictions from models and analyses. Here, we describe several causes of zero‐flow gage readings: frozen surface water, flow reversals, instrument error, and natural or human‐driven upstream source losses or bypass flow. For these examples, we discuss the implications of zero‐flow interpretations. We also highlight additional methods for determining flow presence, including direct observations, statistical methods, and hydrologic models, which can be applied to interpret causes of zero‐flow gage readings and implications for reach‐ and watershed‐scale dynamics. Such efforts are necessary to improve our ability to understand and predict surface flow activation, cessation, and connectivity across river networks. Developing this integrated understanding of the wide range of possible meanings of zero‐flows will only attain greater importance in a more variable and changing hydrologic climate.

WIREs Water↗

USGS2005

Multi-scale remote sensing of coral reefs

In this chapter we present how both direct and indirect remote sensing can be integrated to address two major coral reef applications - coral bleaching and assessment of biodiversity. This approach reflects the current non-linear integration of remote sensing for environmental assessment of coral reefs, resulting from a rapid increase in available sensors, processing methods and interdisciplinary collaborations (Andréfouët and Riegl, 2004). Moreover, this approach has greatly benefited from recent collaborations of once independent investigations (e.g., benthic ecology, remote sensing, and numerical modeling).

Florida↗