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At least 1,135 records · Page 63Linked to original sources
USGS2017

Response of fish population dynamics to mitigation activities in a large regulated river

Extensive water development in large rivers has precipitated many negative ecological effects on native fish populations. Mitigation for such development often focuses on restoring biological integrity through remediation of the physical and chemical properties of regulated rivers. However, evaluating and defining the success of those programs can be difficult. We modeled the influence of mitigation-related environmental factors on growth and recruitment of two ecologically important native fish species (Largescale Sucker Catostomus macrocheilus and Mountain Whitefish Prosopium williamsoni ) in the Kootenai River, Idaho. Artificial nutrient (phosphorus) addition best predicted the variability in annual growth of both species. Nutrient addition was positively related to Largescale Sucker growth but negatively related to Mountain Whitefish growth. The best model explained 82% of the annual variability in incremental growth for Largescale Suckers and 61% of the annual variability for Mountain Whitefish. Year-class strength of Largescale Suckers was not closely related to any of the environmental variables evaluated; however, year-class strength of Mountain Whitefish was closely associated with nutrient addition, discharge, and temperature. Most research has focused on biotic assemblages to evaluate the effects of mitigation activities on fishes, but there is an increased need to identify the influence of rehabilitation activities on fish population dynamics within those assemblages. Here, we demonstrate how fish growth can serve as an indicator of rehabilitation success in a highly regulated large river. Future fish restoration projects can likely benefit from a change in scope and from consideration of an evaluation framework involving the response of population rate functions to mitigation.

Idaho↗
USGS1990

Comparisons of patch-use models for wintering American tree sparrows

Optimal foraging theory has stimulated numerous theoretical and empirical studies of foraging behavior for >20 years. These models provide a valuable tool for studying the foraging behavior of an organism. As with any other tool, the models are most effective when properly used. For example, to obtain a robust test of a foraging model, Stephens and Krebs (1986) recommend experimental designs in which four questions are answered in the affirmative. First, do the foragers play the same "game" as the model? Sec- ond, are the assumptions of the model met? Third, does the test rule out alternative possibilities? Finally, are the appropriate variables measured? Negative an- swers to any of these questions could invalidate the model and lead to confusion over the usefulness of foraging theory in conducting ecological studies. Gaines (1989) attempted to determine whether American Tree Sparrows ( Spizella arborea ) foraged by a time (Krebs 1973) or number expectation rule (Gibb 1962), or in a manner consistent with the predictions of Charnov's (1976) marginal value theorem (MVT). Gaines (1989: 118) noted appropriately that field tests of foraging models frequently involve uncontrollable circumstances; thus, it is often difficult to meet the assumptions of the models. Gaines also states (1989: 118) that "violations of the assumptions are also in- formative but do not constitute robust tests of predicted hypotheses," and that "the problem can be avoided by experimental analyses which concurrently test mutually exclusive hypotheses so that alter- native predictions will be eliminated if falsified." There is a problem with this approach because, when major assumptions of models are not satisfied, it is not justifiable to compare a predator's foraging behavior with the model's predictions. I submit that failing to follow the advice offered by Stephens and Krebs (1986) can invalidate tests of foraging models.

The Auk↗
USGS2022

Evaluating the effect of expert elicitation techniques on population status assessment in the face of large uncertainty

Population projection models are important tools for conservation and management. They are often used for population status assessments, for threat analyses, and to predict the consequences of conservation actions. Although conservation decisions should be informed by science, critical decisions are often made with very little information to support decision-making. Conversely, postponing decisions until better information is available may reduce the benefit of a conservation decision. When empirical data are limited or lacking, expert elicitation can be used to supplement existing data and inform model parameter estimates. The use of rigorous techniques for expert elicitation that account for uncertainty can improve the quality of the expert elicited values and therefore the accuracy of the projection models. One recurring challenge for summarizing expert elicited values is how to aggregate them. Here, we illustrate a process for population status assessment using a combination of expert elicitation and data from the ecological literature. We discuss the importance of considering various aggregation techniques, and illustrate this process using matrix population models for the wood turtle ( Glyptemys insculpta ) to assist U.S. Fish and Wildlife Service decision-makers with their Species Status Assessment. We compare estimates of population growth using data from the ecological literature and four alternative aggregation techniques for the expert-elicited values. The estimate of population growth rate based on estimates from the literature (λ mean = 0.952, 95% CI: 0.87–1.01) could not be used to unequivocally reject the hypotheses of a rapidly declining population nor the hypothesis of a stable, or even slightly growing population, whereas our results for the expert-elicited estimates supported the hypothesis that the wood turtle population will decline over time. Our results showed that the aggregation techniques used had an impact on model estimates, suggesting that the choice of techniques should be carefully considered. We discuss the benefits and limitations associated with each method and their relevance to the population status assessment. We note a difference in the temporal scope or inference between the literature-based estimates that provided insights about historical changes, whereas the expert-based estimates were forward looking. Therefore, conducting an expert-elicitation in addition to using parameter estimates from the literature improved our understanding of our species of interest.

Journal of Environmental Management↗
USGS2025

Model‐based decomposition of spatially varying temporal shifts in seasonal streamflow across north temperate US rivers.

Anthropogenically forced climate shifts disrupt the seasonal behavior of climatic and hydrologic processes. The seasonality of streamflow has significant implications for the ecology of riverine ecosystems and for meeting societal demands for water resources. We develop a hierarchical Bayesian model of daily streamflow to quantify how the shape of annual hydrographs are changing and to evaluate temporal trends in model-based hydrologic indices related to flow timing and magnitude shifts. We apply this model to 1,112 gages across the Northern US over the years 1965–2022. We identify large-scale patterns in temporal changes to streamflow profiles that are consistent with regional changes in hydroclimate, including decreasing seasonal flow variability in the Pacific Northwest and increasing winter flows in the northeastern United States. Within these regions we also observe fine-scale heterogeneity in streamflow timing and magnitude shifts, both of which have potentially significant implications for riverine ecosystem function and the ecosystem services they provide.

northern United States↗
USGS2008

Advances and limitations of individual-based models to analyze and predict dynamics of mangrove forests: A review

Mangrove ecosystems are considered vulnerable to climate change as coastal development limits the ecosystem services and adaptations important to their survival. Although they appear rather simple in terms of species diversity, their ecology is complex due to interacting geophysical forces of tides, surface runoff, river and groundwater discharge, waves, and constituents of sediment, nutrients and saltwater. These interactions limit developing a comprehensive framework for science-based sustainable management practices. A suite of models have been developed independently by various academic and government institutions worldwide to understand the dynamics of mangrove ecosystems and to provide ecological forecasting capabilities under different management scenarios and natural disturbance regimes. The models have progressed from statistical tables representing growth and yield to more sophisticated models describing various system components and processes. Among these models are three individual-based models (IBMs) (FORMAN, KIWI, and MANGRO). A comparison of models’ designs reveal differences in the details of process description, particularly, regarding neighbor competition among trees. Each model has thus its specific range of applications. Whereas FORMAN and KIWI are most suitable to address mangrove forest dynamics of stands, MANGRO focuses on landscape dynamics on larger spatial scale. A comparison of the models and a comparison of the models with empirical knowledge further reveal the general needs for further field and validation studies to advance our ecological understanding and management of mangrove wetlands.

Aquatic Botany↗
USGS2017

Large-scale modeled contemporary and future water temperature estimates for 10774 Midwestern U.S. Lakes

Climate change has already influenced lake temperatures globally, but understanding future change is challenging. The response of lakes to changing climate drivers is complex due to the nature of lake-atmosphere coupling, ice cover, and stratification. To better understand the diversity of lake responses to climate change and give managers insight on individual lakes, we modelled daily water temperature profiles for 10,774 lakes in Michigan, Minnesota, and Wisconsin for contemporary (1979–2015) and future (2020–2040 and 2080–2100) time periods with climate models based on the Representative Concentration Pathway 8.5, the worst-case emission scenario. In addition to lake-specific daily simulated temperatures, we derived commonly used, ecologically relevant annual metrics of thermal conditions for each lake. We include all supporting lake-specific model parameters, meteorological drivers, and archived code for the model and derived metric calculations. This unique dataset offers landscape-level insight into the impact of climate change on lakes.

Michigan, Minnesota, Wisconsin↗
USGS2022

Shifting precipitation regimes alter the phenology and population dynamics of low latitude ectotherms

Predicting how species respond to changes in climate is critical to conserving biodiversity. Modeling efforts to date have largely centered on predicting the effects of warming temperatures on temperate species phenology. In and near the tropics, the effects of a warming planet on species phenology are more likely to be driven by changes in the seasonal precipitation cycle rather than temperature. To demonstrate the importance of considering precipitation-driven phenology in ecological studies, we present a case study wherein we construct a mechanistic population model for a rare subtropical butterfly (Miami blue butterfly, Cyclargus thomasi bethunebakeri ) and use a suite of global climate models to project butterfly populations into the future. Across all iterations of the model, the trajectory of Miami blue populations is uncertain. We identify both biological uncertainty (unknown diapause survival rate) and climate uncertainty (ambiguity in the sign of precipitation change across climate models), and their interaction as key factors that determine persistence vs. extinction. Despite uncertainty, the most optimistic iteration of the model predicts that Miami blue butterfly populations will decline under the higher emissions scenario (RCP 8.5). The lack of climate model agreement across the projection ensemble suggests that investigations into the effect of climate change on precipitation-driven phenology require a higher level of rigor in the uncertainty analysis compared to analogous studies of temperature. For tropical species, a mechanistic approach that incorporates both biological and climate uncertainty is the best path forward to understand the effect shifting precipitation regimes have on phenology and population dynamics.

Florida↗
USGS2018

Occupancy modeling species–environment relationships with non‐ignorable survey designs

Statistical models supporting inferences about species occurrence patterns in relation to environmental gradients are fundamental to ecology and conservation biology. A common implicit assumption is that the sampling design is ignorable and does not need to be formally accounted for in analyses. The analyst assumes data are representative of the desired population and statistical modeling proceeds. However, if data sets from probability and non‐probability surveys are combined or unequal selection probabilities are used, the design may be non‐ignorable. We outline the use of pseudo‐maximum likelihood estimation for site‐occupancy models to account for such non‐ignorable survey designs. This estimation method accounts for the survey design by properly weighting the pseudo‐likelihood equation. In our empirical example, legacy and newer randomly selected locations were surveyed for bats to bridge a historic statewide effort with an ongoing nationwide program. We provide a worked example using bat acoustic detection/non‐detection data and show how analysts can diagnose whether their design is ignorable. Using simulations we assessed whether our approach is viable for modeling data sets composed of sites contributed outside of a probability design. Pseudo‐maximum likelihood estimates differed from the usual maximum likelihood occupancy estimates for some bat species. Using simulations we show the maximum likelihood estimator of species–environment relationships with non‐ignorable sampling designs was biased, whereas the pseudo‐likelihood estimator was design unbiased. However, in our simulation study the designs composed of a large proportion of legacy or non‐probability sites resulted in estimation issues for standard errors. These issues were likely a result of highly variable weights confounded by small sample sizes (5% or 10% sampling intensity and four revisits). Aggregating data sets from multiple sources logically supports larger sample sizes and potentially increases spatial extents for statistical inferences. Our results suggest that ignoring the mechanism for how locations were selected for data collection (e.g., the sampling design) could result in erroneous model‐based conclusions. Therefore, in order to ensure robust and defensible recommendations for evidence‐based conservation decision‐making, the survey design information in addition to the data themselves must be available for analysts. Details for constructing the weights used in estimation and code for implementation are provided.

Ecological Applications↗
USGS2018

Evidence and opportunities for integrating landscape ecology into natural resource planning across multiple-use landscapes

Enhancing natural resource management has been a focus of landscape ecology since its inception, but numerous authors argue that landscape ecology has not yet been effective in achieving the underlying goal of planning and designing sustainable landscapes. We developed nine questions reflecting the application of fundamental research topics in landscape ecology to the landscape planning process and reviewed two recent landscape-scale plans in western North America for evidence of these concepts in plan decisions. Both plans considered multiple resources, uses, and values, including energy development, recreation, conservation, and protection of cultural and historic resources. We found that land use change and multiscale perspectives of resource uses and values were very often apparent in planning decisions. Pattern-process relationships, connectivity and fragmentation, ecosystem services, landscape history, and climate change were reflected less frequently. Landscape sustainability was considered only once in the 295 decisions reviewed, and outputs of landscape models were not referenced. We suggest six actionable opportunities for further integrating landscape ecology concepts into landscape planning efforts: 1) use landscape sustainability as an overarching goal, 2) adopt a broad ecosystem services framework, 3) explore the role of landscape history more comprehensively, 4) regularly consider and accommodate potential effects of climate change, 5) use landscape models to support plan decisions, and 6) promote a greater presence of landscape ecologists within agencies that manage large land bases and encourage active involvement in agency planning efforts. Together these actions may improve the defensibility, durability, and sustainability of landscape plan decisions.

Current Landscape Ecology Reports↗
USGS2009

Trend estimation in populations with imperfect detection

1. Trends of animal populations are of great interest in ecology but cannot be directly observed owing to imperfect detection. Binomial mixture models use replicated counts to estimate abundance, corrected for detection, in demographically closed populations. Here, we extend these models to open populations and illustrate them using sand lizard Lacerta agilis counts from the national Dutch reptile monitoring scheme. 2. Our model requires replicated counts from multiple sites in each of several periods, within which population closure is assumed. Counts are described by a hierarchical generalized linear model, where the state model deals with spatio-temporal patterns in true abundance and the observation model with imperfect counts, given that true state. We used WinBUGS to fit the model to lizard counts from 208 transects with 1–10 (mean 3) replicate surveys during each spring 1994–2005. 3. Our state model for abundance contained two independent log-linear Poisson regressions on year for coastal and inland sites, and random site effects to account for unexplained heterogeneity. The observation model for detection of an individual lizard contained effects of region, survey date, temperature, observer experience and random survey effects. 4. Lizard populations increased in both regions but more steeply on the coast. Detectability increased over the first few years of the study, was greater on the coast and for the most experienced observers, and highest around 1 June. Interestingly, the population increase inland was not detectable when the observed counts were analysed without account of detectability. The proportional increase between 1994 and 2005 in total lizard abundance across all sites was estimated at 86% (95% CRI 35–151). 5. Synthesis and applications . Open-population binomial mixture models are attractive for studying true population dynamics while explicitly accounting for the observation process, i.e. imperfect detection. We emphasize the important conceptual benefit provided by temporal replicate observations in terms of the interpretability of animal counts.

Journal of Applied Ecology↗
USGS2018

Ecosystem responses to elevated CO2 using airborne remote sensing at Mammoth Mountain, California

We present an exploratory study examining the use of airborne remote-sensing observations to detect ecological responses to elevated CO 2 emissions from active volcanic systems. To evaluate these ecosystem responses, existing spectroscopic, thermal, and lidar data acquired over forest ecosystems on Mammoth Mountain volcano, California, were exploited, along with in situ measurements of persistent volcanic soil CO 2 fluxes. The elevated CO 2 response was used to statistically model ecosystem structure, composition, and function, evaluated via data products including biomass, plant foliar traits and vegetation indices, and evapotranspiration (ET). Using regression ensemble models, we found that soil CO 2 flux was a significant predictor for ecological variables, including canopy greenness (normalized vegetation difference index, NDVI), canopy nitrogen, ET, and biomass. With increasing CO 2 , we found a decrease in ET and an increase in canopy nitrogen, both consistent with theory, suggesting more water- and nutrient-use-efficient canopies. However, we also observed a decrease in NDVI with increasing CO 2 (a mean NDVI of 0.27 at 200 g m −2 d −1 CO 2 reduced to a mean NDVI of 0.10 at 800 g m −2 d −1 CO 2 ). This is inconsistent with theory though consistent with increased efficiency of fewer leaves. We found a decrease in above-ground biomass with increasing CO 2 , also inconsistent with theory, but we did also find a decrease in biomass variance, pointing to a long-term homogenization of structure with elevated CO 2 . Additionally, the relationships between ecological variables changed with elevated CO 2 , suggesting a shift in coupling/decoupling among ecosystem structure, composition, and function synergies. For example, ET and biomass were significantly correlated for areas without elevated CO 2 flux but decoupled with elevated CO 2 flux. This study demonstrates that (a) volcanic systems show great potential as a means to study the properties of ecosystems and their responses to elevated CO 2 emissions and (b) these ecosystem responses are measurable using a suite of airborne remotely sensed data.

California↗
USGS2020

Integrating broad‐scale data to assess demographic and climatic contributions to population change in a declining songbird

Climate variation and trends affect species distribution and abundance across large spatial extents. However, most studies that predict species response to climate are implemented at small spatial scales or are based on occurrence‐environment relationships that lack mechanistic detail. Here, we develop an integrated population model (IPM) for multi‐site count and capture‐recapture data for a declining migratory songbird, Wilson's warbler ( Cardellina pusilla ), in three genetically distinct breeding populations in western North America. We include climate covariates of vital rates, including spring temperatures on the breeding grounds, drought on the wintering range in northwest Mexico, and wind conditions during spring migration. Spring temperatures were positively related to productivity in Sierra Nevada and Pacific Northwest genetic groups, and annual changes in productivity were important predictors of changes in growth rate in these populations. Drought condition on the wintering grounds was a strong predictor of adult survival for coastal California and Sierra Nevada populations; however, adult survival played a relatively minor role in explaining annual variation in population change. A latent parameter representing a mixture of first‐year survival and immigration was the largest contributor to variation in population change; however, this parameter was estimated imprecisely, and its importance likely reflects, in part, differences in spatio‐temporal distribution of samples between count and capture‐recapture data sets. Our modeling approach represents a novel and flexible framework for linking broad‐scale multi‐site monitoring data sets. Our results highlight both the potential of the approach for extension to additional species and systems, as well as needs for additional data and/or model development.

Ecology and Evolution↗
USGS2020

Impacts of sea-level rise on the tidal reach of California coastal rivers using the Coastal Storm Modeling System (CoSMoS)

In coastal rivers, the interactions between tides and fluvial discharge affect local ecology, sedimentation, river dynamics, river mouth configuration, and the flooding potential in adjacent wetlands and low-lying areas. With sea-level rise, the tidal reach within coastal rivers can expand upstream, impacting river dynamics and increasing flood risk across a much greater area. Rivers along the Pacific coast of California are constrained by coastal mountain ranges, in contrast to the typical large and low-gradient river deltas and estuaries often used in tidal reach studies. Yet, the impacts of tides are ever-present and considerable to these rivers' habitats and neighboring communities. The impacts of a range of sea-level rise, discharge, and coastal storm conditions on the upstream extent of a spring tidal influence and the associated flood potential are assessed for several coastal rivers across California using coupled Delft3D FLOW-WAVE hydrodynamic simulations that explicitly account for the interactions between waves, currents, surge, and water levels in this study. Projected changes in tidal reach among the various rivers are characterized and implications for adjacent communities and stakeholders are discussed.

California↗
USGS2008

Modeling and dynamic monitoring of ecosystem performance in the Yukon River Basin

Central Alaska is ecologically sensitive and experiencing stress in response to marked regional warming. Resource managers would benefit from an improved ability to monitor ecosystem processes in response to climate change, fire, insect damage, and management policies and to predict responses to future climate scenarios. We have developed a method for analyzing ecosystem performance as represented by the growing season integral of normalized difference vegetation index (NDVI), which is a measure of greenness that can be interpreted in terms of plant growth or photosynthetic activity (gross primary productivity). The approach illustrates the status and trends of ecosystem changes and separates the influences of climate and local site conditions from the influences of disturbances and land management. We emphasize the ability to quantify ecosystem processes, not simply changes in land cover, across the entire period of the remote sensing archive (Wylie and others, 2008). The method builds upon remotely sensed measures of vegetation greenness for each growing season. By itself, however, a time series of greenness often reflects annual climate variations in temperature and precipitation. Our method seeks to remove the influence of climate so that changes in underlying ecological conditions are identified and quantified. We define an "expected ecosystem performance" to represent the greenness response expected in a particular year given the climate of that year. We distinguish "performance anomalies" as cases where the ecosystem response is significantly different from the expected ecosystem performance. Maps of the performance anomalies (fig. 1) and trends in the anomalies give valuable information on the ecosystems for land managers and policy makers at a resolution of 1 km to 250 m.

Fact Sheet↗
USGS2021

Improving inferences about private land conservation by accounting for incomplete reporting

Private lands provide key habitat for imperiled species and are core components of function protected area networks; yet, their incorporation into national and regional conservation planning has been challenging. Identifying locations where private landowners are likely to participate in conservation initiatives can help avoid conflict and clarify trade-offs between ecological benefits and sociopolitical costs. Empirical, spatially explicit assessment of the factors associated with conservation on private land is an emerging tool for identifying future conservation opportunities. However, most data on private land conservation are voluntarily reported and incomplete, which complicates these assessments. We used a novel application of occupancy models to analyze the occurrence of conservation easements on private land. We compared multiple formulations of occupancy models with a logistic regression model to predict the locations of conservation easements based on a spatially explicit social-ecological systems framework. We combined a simulation experiment with a case study of easement data in Idaho and Montana (United States) to illustrate the utility of the occupancy framework for modeling conservation on private land. Occupancy models that explicitly accounted for variation in reporting produced estimates of predictors that were substantially less biased than estimates produced by logistic regression under all simulated conditions. Occupancy models produced estimates for the 6 predictors we evaluated in our case study that were larger in magnitude, but less certain than those produced by logistic regression. These results suggest that occupancy models result in qualitatively different inferences regarding the effects of predictors on conservation easement occurrence than logistic regression and highlight the importance of integrating variable and incomplete reporting of participation in empirical analysis of conservation initiatives. Failure to do so can lead to emphasizing the wrong social, institutional, and environmental factors that enable conservation and underestimating conservation opportunities in landscapes where social norms or institutional constraints inhibit reporting.

Idaho, Montana↗
USGS2007

Occupancy estimation and modeling with multiple states and state uncertainty

The distribution of a species over space is of central interest in ecology, but species occurrence does not provide all of the information needed to characterize either the well-being of a population or the suitability of occupied habitat. Recent methodological development has focused on drawing inferences about species occurrence in the face of imperfect detection. Here we extend those methods by characterizing occupied locations by some additional state variable ( e. g., as producing young or not). Our modeling approach deals with both detection probabilities,1 and uncertainty in state classification. We then use the approach with occupancy and reproductive rate data from California Spotted Owls ( Strix occidentalis occidentalis ) collected in the central Sierra Nevada during the breeding season of 2004 to illustrate the utility of the modeling approach. Estimates of owl reproductive rate were larger than naive estimates, indicating the importance of appropriately accounting for uncertainty in detection and state classification.

Ecology↗
USGS2021

Terrestrial ecological risk analysis via dietary exposure at uranium mine sites in the Grand Canyon watershed (Arizona, USA)

The U.S. Department of the Interior recently included uranium (U) on a list of mineral commodities that are considered critical to economic and national security. The uses of U for commercial and residential energy production, defense applications, medical device technologies, and energy generation for space vehicles and satellites are known, but the environmental impacts of uranium extraction are not always well quantified. We conducted a screening-level ecological risk analysis based on exposure to mining-related elements via diets and incidental soil ingestion for terrestrial biota to provide context to chemical characterization and exposures at breccia pipe U mines in northern Arizona. Relative risks, calculated as hazard quotients (HQs), were generally low for all biological receptor models. Our models screened for risk to omnivores and insectivores (HQs>1) but not herbivores and carnivores. Uranium was not the driver of ecological risk; arsenic, cadmium, copper, and zinc were of concern for biota consuming ground-dwelling invertebrates. Invertebrate species composition should be considered when applying these models to other mining locations or future sampling at the breccia pipe mine sites. Dietary concentration thresholds (DCTs) were also calculated to understand food concentrations that may lead to ecological risk. The DCTs indicated that critical concentrations were not approached in our model scenarios, as evident in the very low HQs for most models. The DCTs may be used by natural resource and land managers as well as mine operators to screen or monitor for potential risk to terrestrial receptors as mine sites are developed and remediated in the future.

Arizona↗
USGS1990

The evolution of sex-change mechanisms in fishes

Five distinct sex-change mechanisms are identified among sequentially hermaphroditic fishes based on socio-ecological characteristics. The primary determinants of the sex-change mechanisms appear to be social organization and mating system, which in turn depend on resource distribution in space and time. The ability of a single individual to control all mating in the social unit, which is related to the size of the social unit, differentiates three suppression mechanisms from two induction mechanisms. Sex-change suppression, which is characteristic of species with small group size and rigid dominance hierarchies, refers to inevitable sex change in the absence of group dominance. Ability to migrate between resource patches differentiates protogynous suppression (e.g. in Labroides dimidiatus ) from protandrous suppression (e.g. in Amphiprion spp.). Early sex change appears to have evolved from protogynous suppression under special conditions involving the loss of mating control by a single dominant individual in certain species (e.g. Centropyge spp. ). Sex-change induction, which is characteristic of species with large social groups lacking rigid dominance hierarchies, refers to the requirement that sex change must be induced by specific characteristics of (or changes in) the social group, regardless of dominance status. Ability to distinguish sex, or its importance, differentiates sex-ratio induction (e.g. Anthias squamipinnis ) from size-ratio induction (e.g. Thalassoma spp.). Alternative models account for the possibility that all cases of sex change require stimulation from smaller conspecifics (universal induction-inhibition model) or that all fish have the genetic capacity to switch mechanisms, depending on changing ecological conditions and resulting changes in mating system (behavioral-scaling model). Neurophysiological models suggest that induction mechanisms, which require at least two categories of environmental stimuli, may have evolved from the simpler suppression mechanisms, which require only one kind of input from the environment.

Environmental Biology of Fishes↗