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At least 289 records · Page 16Linked to original sources

USGS1996

Overview of a simple model describing variation of dissolved organic carbon in an upland catchment

Hydrological mechanisms controlling the variation of dissolved organic carbon (DOC) were investigated in the Deer Creek catchment located near Montezuma, CO. Patterns of DOC in streamflow suggested that increased flows through the upper soil horizon during snowmelt are responsible for flushing this DOC-enriched interstitial water to the streams. We examined possible hydrological mechanisms to explain the observed variability of DOC in Deer Creek by first simulating the hydrological response of the catchment using TOPMODEL and then routing the predicted flows through a simple model that accounted for temporal changes in DOC. Conceptually the DOC model can be taken to represent a terrestrial (soil) reservoir in which DOC builds up during low flow periods and is flushed out when infiltrating meltwaters cause the water table to rise into this “reservoir”. Concentrations of DOC measured in the upper soil and in streamflow were compared to model simulations. The simulated DOC response provides a reasonable reproduction of the observed dynamics of DOC in the stream at Deer Creek.

Ecological Modelling↗

USGS2016

Landscape and flow metrics affecting the distribution of a federally-threatened fish: Improving management, model fit, and model transferability

Truncated distributions of pelagophilic fishes have been observed across the Great Plains of North America, with water use and landscape fragmentation implicated as contributing factors. Developing conservation strategies for these species is hindered by the existence of multiple competing flow regime hypotheses related to species persistence. Our primary study objective was to compare the predicted distributions of one pelagophil, the Arkansas River Shiner Notropis girardi , constructed using different flow regime metrics. Further, we investigated different approaches for improving temporal transferability of the species distribution model (SDM). We compared four hypotheses: mean annual flow (a baseline), the 75th percentile of daily flow, the number of zero-flow days, and the number of days above 55th percentile flows, to examine the relative importance of flows during the spawning period. Building on an earlier SDM, we added covariates that quantified wells in each catchment, point source discharges, and non-native species presence to a structured variable framework. We assessed the effects on model transferability and fit by reducing multicollinearity using Spearman’s rank correlations, variance inflation factors, and principal component analysis, as well as altering the regularization coefficient (β) within MaxEnt. The 75th percentile of daily flow was the most important flow metric related to structuring the species distribution. The number of wells and point source discharges were also highly ranked. At the default level of β, model transferability was improved using all methods to reduce collinearity; however, at higher levels of β, the correlation method performed best. Using β = 5 provided the best model transferability, while retaining the majority of variables that contributed 95% to the model. This study provides a workflow for improving model transferability and also presents water-management options that may be considered to improve the conservation status of pelagophils.

Great Plains of North America↗

USGS2020

The economics of territory selection

Territorial behavior is a fundamental and conspicuous behavior within numerous species, but the mechanisms driving territory selection remain uncertain. Theory and empirical precedent indicate that many animals select territories economically to satisfy resource requirements for survival and reproduction, based on benefits of food resources and costs of competition and travel. Costs of competition may vary by competitive ability, and costs of predation risk may also drive territory selection. Habitat structure, resource requirements, conspecific density, and predator distribution and abundance are likely to further influence territorial behavior. We developed a mechanistic, spatially-explicit, individual-based model to better understand how and why animals select particular territories. The model was based on optimal selection of individual patches for inclusion in a territory according to their net value, i.e., benefits (food resources) minus costs (travel, competition, predation risk). Simulations produced predictions for what may be observed empirically if such optimization drives placement and characteristics of territories. Simulations consisted of sequential, iterative selection of territories by simulated animals that interacted to defend and maintain territories. Results explain why certain patterns in space use are commonly observed, and when and why these patterns will differ from the norm. For example, more clumped or abundant food resources are predicted to result, on average, in smaller territories with more overlap. Strongly different resource requirements for individuals or groups in a population will directly affect space use and are predicted to cause different responses under identical conditions. Territories are predicted to decrease in size with increasing population density, which can enable a population’s density of territories to change at faster rates than their spatial distribution. Due to competition, less competitive territory-holders are generally predicted to have larger territories in order to accumulate sufficient resources, which could produce an ideal despotic distribution of territories. Interestingly, territory size and overlap are predicted to show a parabolic response to increases in predator densities, and territories are predicted to be larger where predators are more clumped in distribution. Our model’s predictions are consistent with many empirical observations, providing support for optimal patch selection as a mechanism for the economical territories of animals commonly observed in nature.

Ecological Modelling↗

USGS2005

Quantile regression reveals hidden bias and uncertainty in habitat models

We simulated the effects of missing information on statistical distributions of animal response that covaried with measured predictors of habitat to evaluate the utility and performance of quantile regression for providing more useful intervals of uncertainty in habitat relationships. These procedures were evaulated for conditions in which heterogeneity and hidden bias were induced by confounding with missing variables associated with other improtant processes, a problem common in statistical modeling of ecological phenomena. Simulations for a large ( N = 10 000) finite population representing grid locations on a landscape demonstrated various forms of hidden bias that might occur when the effect of a measured habitat variable on some animal was confounded with the effect of another unmeasured variable. Quantile (0 ≤ τ ≤ 1) regression parameters for linear models that excluded the important, unmeasured variable revealed bias relative to parameters from the generating model. Depending on whether interactions of the measured and unmeasured variables were negative (interference interactions) or positive (facilitation interactions) in simulations without spatial structuring, either upper (τ > 0.5) or lower (τ < 0.5) quantile regression parameters were less biased than mean rate parameters. Heterogeneous, nonlinear response patterns occurred with correlations between the measured and unmeasured variables. When the unmeasured variable was spatially structured, variation in parameters across quantiles associated with heterogeneous effects of the habitat variable was reduced by modeling the spatial trend surface as a cubic polynomial of location coordinates, but substantial hidden bias remained. Sampling ( n = 20–300) simulations demonstrated that regression quantile estimates and confidence intervals constructed by inverting weighted rank score tests provided valid coverage of these parameters. Local forms of quantile weighting were required for obtaining correct Type I error rates and confidence interval coverage. Quantile regression was used to estimate effects of physical habitat resources on a bivalve ( Macomona liliana ) in the spatially structured landscape on a sandflat in a New Zealand harbor. Confidence intervals around predicted 0.10 and 0.90 quantiles were used to estimate sampling intervals containing 80% of the variation in densities in relation to bed elevation. Spatially structured variation in bivalve counts estimated by a cubic polynomial trend surface remained after accounting for the nonlinear effects of bed elevation, indicating the existence of important spatially structured processes that were not adequately represented by the measured habitat variables.

Ecology↗

USGS2022

Know what you don't know: Embracing state uncertainty in disease-structured multistate models

Hidden Markov models (HMMs) are broadly applicable hierarchical models that derive their utility from separating state processes from observation processes yielding the data. Multistate models such as mark–recapture and dynamic multistate occupancy models are HMMs frequently used in ecology. In their early formulations, states, such as pathogen infection status, were assumed to be perfectly observed without ambiguity. However, state uncertainty is a pervasive feature of many ecological studies, and multievent models were developed to explicitly account for it. We developed a novel extended multievent mark–recapture model that incorporates state uncertainty at multiple levels of detection. Using a disease-structured example, both false negative and false positive state assignment errors are modelled at two levels of state assignment—the pathogen sampling process and the diagnostic process that samples are subjected to. We additionally describe methods to jointly model infection intensity to integrate heterogeneity in ecological parameters, such as mortality and infection dynamics, and the pathogen detection processes. We provide code to simulate and analyse datasets with various underlying ecological processes and fit our model to a mark–recapture dataset of Mixophyes fleayi (Fleay's barred frog) infected with the amphibian chytrid fungus ( Batrachochytrium dendrobatidis , Bd ). In our case study, we found evidence for various state assignment errors: the sampling protocol performed poorly in detecting Bd , pathogen detection was highly dependent on infection intensity and false positives were non-negligible. Incorporating state uncertainty yielded significantly higher estimates of infection prevalence and 4–5 times lower rates of infection state transitions compared to those obtained from a traditional multistate model. Our results highlight that incorporating state assignment errors improves inference on the ecological process, especially when sensitivity and specificity of the state assignment processes are low. The general model structure can be applied to other HMMs, providing a foundation for modelling state uncertainty in related models. For disease-structured multistate models, we recommend conducting robust design surveys and collecting samples during each capture event to facilitate incorporating pathogen detection errors.

Methods in Ecology and Evolution↗

USGS2007

Uncertainty in age-specific harvest estimates and consequences for white-tailed deer management

Age structure proportions (proportion of harvested individuals within each age class) are commonly used as support for regulatory restrictions and input for deer population models. Such use requires critical evaluation when harvest regulations force hunters to selectively harvest specific age classes, due to impact on the underlying population age structure. We used a stochastic population simulation model to evaluate the impact of using harvest proportions to evaluate changes in population age structure under a selective harvest management program at two scales. Using harvest proportions to parameterize the age-specific harvest segment of the model for the local scale showed that predictions of post-harvest age structure did not vary dependent upon whether selective harvest criteria were in use or not. At the county scale, yearling frequency in the post-harvest population increased, but model predictions indicated that post-harvest population size of 2.5 years old males would decline below levels found before implementation of the antler restriction, reducing the number of individuals recruited into older age classes. Across the range of age-specific harvest rates modeled, our simulation predicted that underestimation of age-specific harvest rates has considerable influence on predictions of post-harvest population age structure. We found that the consequence of uncertainty in harvest rates corresponds to uncertainty in predictions of residual population structure, and this correspondence is proportional to scale. Our simulations also indicate that regardless of use of harvest proportions or harvest rates, at either the local or county scale the modeled SHC had a high probability (>0.60 and >0.75, respectively) of eliminating recruitment into >2.5 years old age classes. Although frequently used to increase population age structure, our modeling indicated that selective harvest criteria can decrease or eliminate the number of white-tailed deer recruited into older age classes. Thus, we suggest that using harvest proportions for management planning and evaluation should be viewed with caution. In addition, we recommend that managers focus more attention on estimation of age-specific harvest rates, and modeling approaches which combine harvest rates with information from harvested individuals to further increase their ability to effectively manage deer populations under selective harvest programs. ?? 2006 Elsevier B.V. All rights reserved.

Ecological Modelling↗

USGS2016

Spatially explicit control of invasive species using a reaction-diffusion model

Invasive species, which can be responsible for severe economic and environmental damages, must often be managed over a wide area with limited resources, and the optimal allocation of effort in space and time can be challenging. If the spatial range of the invasive species is large, control actions might be applied only on some parcels of land, for example because of property type, accessibility, or limited human resources. Selecting the locations for control is critical and can significantly impact management efficiency. To help make decisions concerning the spatial allocation of control actions, we propose a simulation based approach, where the spatial distribution of the invader is approximated by a reaction–diffusion model. We extend the classic Fisher equation to incorporate the effect of control both in the diffusion and local growth of the invader. The modified reaction–diffusion model that we propose accounts for the effect of control, not only on the controlled locations, but on neighboring locations, which are based on the theoretical speed of the invasion front. Based on simulated examples, we show the superiority of our model compared to the state-of-the-art approach. We illustrate the use of this model for the management of Burmese pythons in the Everglades (Florida, USA). Thanks to the generality of the modified reaction–diffusion model, this framework is potentially suitable for a wide class of management problems and provides a tool for managers to predict the effects of different management strategies.

Ecological Modelling↗

USGS2023

Simulating the migration dynamics of juvenile salmonids through rivers and estuaries using a hydrodynamically driven enhanced particle tracking model

Juvenile salmonids migrate hundreds of kilometers from their natal streams to mature in the ocean. Throughout this migration, they respond to environmental cues such as local water velocities and other stimuli to direct and modulate their movements, often through heavily modified riverine and estuarine habitats. Management strategies in an uncertain future of climate change and altered land use regimes depend heavily on being able to reliably predict their ocean entry timings, route use, and survival rates through rivers and estuaries. We developed a spatially-explicit agent-based model of fish movement in response to hydrodynamic flows that uses movement dynamics gleaned from multi-dimensional tracking datasets of acoustically tagged juveniles moving through an urbanized, branched tidal estuary. We demonstrate how such models can be calibrated, and we apply it to the Sacramento-San Joaquin Delta in Central California. The quality of the out-of-sample validation of the model to predict juvenile salmon survival and route selection indicates that the model is versatile and flexible enough to be used in novel hydroclimatological conditions.

California↗

USGS2026

Improved estimates of relative occurrence and abundance using opportunistic surveys and presence-only observations: A zero-inflated integrated species distribution model

Modeling tools for estimating and forecasting shifts in species distributions are becoming increasingly valuable for conservation planning and response. This is especially true for wild bird populations, which have been declining across habitats and regions. Species distribution models (SDM) represent a diverse set of tools with options for addressing various sources of bias. Complex spatial processes associated with rare or clustered species can be accounted for using zero-inflated SDMs, whereas biased survey data can be integrated with additional data sources to improve estimates. Each option addresses an important and common source of bias, but the two SDM frameworks have not been implemented together previously. We present a novel zero-inflated extension of an integrated SDM framework for a Poisson regression (ZI-iSDM), allowing for the estimation of independent occurrence and abundance processes by integrating opportunistic survey and presence-only data. We validated performance of this ZI-iSDM using simulated datasets under different degrees of species rarity and density on the landscape as well as sampling bias in opportunistic datasets. We additionally applied this model to real survey data for multiple wild bird species using publicly available observation data combined with open-access environmental information to describe habitat associations. We found that integrating presence-only data, such as banding or harvest events, can compensate for potential deficiencies in opportunistic surveys by expanding sampling to be more representative of available and used habitat. Additionally, models that first differentiated occurrence and abundance using a ZI term were better suited for approximating distributions of spatially clustered species.

Ecological Modeling↗

USGS2001

An individual-based model of rabbit viral haemorrhagic disease on European wild rabbits (Oryctolagus cuniculus)

We developed an individual-based model of Rabbit Viral Hemorrhagic Disease (RVHD) for European wild rabbits ( Oryctolagus cuniculus L.), representing up to 1000 rabbits in four hectares. Model output for productivity and recruitment matched published values. The disease was density-dependent and virulence affected outcome. Strains that caused death after several days produced greater overall mortality than strains in which rabbits either died or recovered very quickly. Disease effect also depended on time of year. We also elaborated a larger scale model representing 25 km 2 and 100,000+ rabbits, split into a number of grid-squares. This was a more traditional model that did not represent individual rabbits, but employed a system of dynamic equations for each grid-square. Disease spread depended on probability of transmission between neighboring grid-squares. Potential recovery from a major population crash caused by the disease relied on disease virulence and frequency of recurrence. The model's dependence on probability of disease transmission between grid-squares suggests the way that the model represents the spatial distribution of the population affects simulation. Although data on RVHD in Europe are lacking, our models provide a basis for describing the disease in realistic detail and for assessing influence of various social and spatial factors on spread.

Ecological Modelling↗

USGS2011

Estimating age from recapture data: Integrating incremental growth measures with ancillary data to infer age-at-length

Estimating the age of individuals in wild populations can be of fundamental importance for answering ecological questions, modeling population demographics, and managing exploited or threatened species. Significant effort has been devoted to determining age through the use of growth annuli, secondary physical characteristics related to age, and growth models. Many species, however, either do not exhibit physical characteristics useful for independent age validation or are too rare to justify sacrificing a large number of individuals to establish the relationship between size and age. Length‐at‐age models are well represented in the fisheries and other wildlife management literature. Many of these models overlook variation in growth rates of individuals and consider growth parameters as population parameters. More recent models have taken advantage of hierarchical structuring of parameters and Bayesian inference methods to allow for variation among individuals as functions of environmental covariates or individual‐specific random effects. Here, we describe hierarchical models in which growth curves vary as individual‐specific stochastic processes, and we show how these models can be fit using capture–recapture data for animals of unknown age along with data for animals of known age. We combine these independent data sources in a Bayesian analysis, distinguishing natural variation (among and within individuals) from measurement error. We illustrate using data for African dwarf crocodiles, comparing von Bertalanffy and logistic growth models. The analysis provides the means of predicting crocodile age, given a single measurement of head length. The von Bertalanffy was much better supported than the logistic growth model and predicted that dwarf crocodiles grow from 19.4 cm total length at birth to 32.9 cm in the first year and 45.3 cm by the end of their second year. Based on the minimum size of females observed with hatchlings, reproductive maturity was estimated to be at nine years. These size benchmarks are believed to represent thresholds for important demographic parameters; improved estimates of age, therefore, will increase the precision of population projection models. The modeling approach that we present can be applied to other species and offers significant advantages when multiple sources of data are available and traditional aging techniques are not practical.

Loango National Park↗

USGS1978

Simulation model of Cryptomonas ovata population dynamics in southern Kootenay Lake, British Columbia

A one-dimensional transient model is developed to simulate observed population dynamics of a small flagellate ( Cryptomonas ovata ) in southern Kootenay Lake, B.C., during a 1-year period (1 June 1974-31 May 1975). The model considers advective displacement of C. ovata as water flows from its southern entrance into the lake and moves northward toward sampling sites. Specific growth rate is computed from an experimentally determined function of mean irradiance, water temperature, and nutrient (ammonium and phosphate) concentration in the upper 10 m of the water column. Cell losses are assumed to result from zooplankton grazing and inhibition due to presence of the blue-green alga Anabaena . The model simulates well the timing and magnitude of all observed population changes and, more importantly, it gives insight into the important mechanisms which regulate population density of C. ovata in this natural system.

British Columbia↗

USGS2017

Using dynamic population simulations to extend resource selection analyses and prioritize habitats for conservation

Prioritizing habitats for conservation is a challenging task, particularly for species with fluctuating populations and seasonally dynamic habitat needs. Although the use of resource selection models to identify and prioritize habitat for conservation is increasingly common, their ability to characterize important long-term habitats for dynamic populations are variable. To examine how habitats might be prioritized differently if resource selection was directly and dynamically linked with population fluctuations and movement limitations among seasonal habitats, we constructed a spatially explicit individual-based model for a dramatically fluctuating population requiring temporally varying resources. Using greater sage-grouse ( Centrocercus urophasianus) in Wyoming as a case study, we used resource selection function maps to guide seasonal movement and habitat selection, but emergent population dynamics and simulated movement limitations modified long-term habitat occupancy. We compared priority habitats in RSF maps to long-term simulated habitat use. We examined the circumstances under which the explicit consideration of movement limitations, in combination with population fluctuations and trends, are likely to alter predictions of important habitats. In doing so, we assessed the future occupancy of protected areas under alternative population and habitat conditions. Habitat prioritizations based on resource selection models alone predicted high use in isolated parcels of habitat and in areas with low connectivity among seasonal habitats. In contrast, results based on more biologically-informed simulations emphasized central and connected areas near high-density populations, sometimes predicted to be low selection value. Dynamic models of habitat use can provide additional biological realism that can extend, and in some cases, contradict habitat use predictions generated from short-term or static resource selection analyses. The explicit inclusion of population dynamics and movement propensities via spatial simulation modeling frameworks may provide an informative means of predicting long-term habitat use, particularly for fluctuating populations with complex seasonal habitat needs. Importantly, our results indicate the possible need to consider habitat selection models as a starting point rather than the common end point for refining and prioritizing habitats for protection for cyclic and highly variable populations.

Wyoming↗

USGS2020

Comparing environmental flow implementation options with structured decision making: Case study from the Willamette River, Oregon

Many frameworks have been used to identify environmental flows for sustaining river ecosystems or specific taxa in the face of widespread flow alteration for human use. However, these methods mostly focus on identifying suitable flows and largely ignore the important links between management actions, resulting flows, flow variability, and ecosystem or social responses. Structured decision making (SDM) could assist the comparison and implementation of environmental flows by providing a framework to compare effects of flow management actions on objectives via environmental flow science. We describe the SDM process and illustrate its application using a case study focused on comparing environmental flow scenarios for the mainstem Willamette River, Oregon, USA. In a short timeframe, SDM was successfully applied to identify management objectives, develop empirical and expert opinion based models predicting ecological responses, and compare scenarios while accounting for uncertainty and partial controllability. We found that no flow scenario was clearly preferred based on available knowledge, largely because river flows could only be partially controlled through available dam operations. Participants agreed that the SDM process was useful and that an additional iteration focused on refining predictive models and incorporating additional objectives could help better inform dam release decisions for the entire basin. In our view, SDM can provide managers with more realistic comparisons of environmental flows by accounting for partial controllability and uncertainty, which may result in greater implementation of available flow management actions.

Oregon↗

USGS2014

A Bayesian network approach to predicting nest presence of thefederally-threatened piping plover ( Charadrius melodus ) using barrier island features

Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modelling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model’s dataset. We found that model predictions were more successful when the range of physical conditions included in model development was varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modelling impacts of sea-level rise- or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.

Maryl↗

USGS2021

Landscape-scale drivers of endangered Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis) presence using an ensemble modeling approach

The Florida Everglades is a vast and iconic wetland ecosystem in the southern United States that has undergone dramatic changes from habitat degradation, development encroachment, and water impoundment. Starting in the past few decades, large restoration projects have been undertaken to restore the landscape, including improving conditions for threatened and imperiled taxa. One focus of restoration has been the marl prairie ecosystem, where the federally endangered Cape Sable Seaside Sparrow ( Ammospiza maritima mirabilis ; CSSS) resides. The CSSS is endemic to the Everglades where populations have been steadily declining, signaling the importance of decision support tools for natural resource managers for evaluating water management and restoration scenarios. Here we developed an ensemble logistic regression, combining a frequentist and Bayesian approach, to model CSSS presence and measure how environmental factors such as hydrometrics, fire occurrence, and vegetation structure impact CSSS habitat suitability. This is the first analysis to quantitatively assess the interdependent relationships between a broad range of environmental factors and CSSS presence across the landscape. Our results show that the probability of CSSS presence was highest in areas with dry conditions, hydroperiods between 80 and 120 days, percentages of canopy cover and woody vegetation less than 10%, and more than six years post-fire where 75% or more of the area was burned. Because the frequentist and Bayesian models had nearly identical spatial outputs with the Bayesian model having slightly higher validation metrics, we used the Bayesian approach as our final model (EverSparrow). The results from our analysis can provide a valuable decision support tool as natural resource managers work to restore the Everglades landscape.

Florida↗

USGS2026

Modeling the temperature-mediated link between demography and biomass in ectotherms

Population models can forecast the effects of environmental change, disturbance, and management decisions on populations of interest. Most population models focus on abundance of individuals, but biomass may be a more relevant quantity at the community or ecosystem scale, especially when investigating interaction of multiple stressors and cross-ecosystem energy flows. We developed a stage-structured population model that integrates a temperature-mediated tradeoff between development rate and final body size of invertebrates, allowing projection of both population size and standing stock biomass in ecosystems that experience disturbances. We parameterized the model for four aquatic invertebrate life-history types spanning a range of lifespan and susceptibility to disturbance. We observed several emergent properties typical of invertebrate populations but not explicitly included in the model, including self-organization of cohort structure under seasonal environments, a tradeoff between lifespan duration and rate of post-disturbance population recovery, and chaotic population dynamics under high growth rates. Sensitivity analysis revealed that although elevated mean temperature increased population size, total population biomass remained unchanged or even decreased due to declining individual biomass, which has implications for ecosystem processes under a changing climate. The seasonal timing of disturbance showed a strong interaction with temperature regime, with summer versus winter pulse disturbances affecting biomass and abundance in characteristically different ways. Linking invertebrate abundance and biomass dynamics to abiotic drivers through this time-varying matrix population model reveals how life-history shapes aquatic invertebrate response to shifting seasons, temperatures, and disturbance, which is key to understanding the impacts of climate change on aquatic ecosystems.

Ecological Modelling↗

USGS2006

Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods

Many efforts are underway to produce broad-scale forest attribute maps by modelling forest class and structure variables collected in forest inventories as functions of satellite-based and biophysical information. Typically, variants of classification and regression trees implemented in Rulequest's?? See5 and Cubist (for binary and continuous responses, respectively) are the tools of choice in many of these applications. These tools are widely used in large remote sensing applications, but are not easily interpretable, do not have ties with survey estimation methods, and use proprietary unpublished algorithms. Consequently, three alternative modelling techniques were compared for mapping presence and basal area of 13 species located in the mountain ranges of Utah, USA. The modelling techniques compared included the widely used See5/Cubist, generalized additive models (GAMs), and stochastic gradient boosting (SGB). Model performance was evaluated using independent test data sets. Evaluation criteria for mapping species presence included specificity, sensitivity, Kappa, and area under the curve (AUC). Evaluation criteria for the continuous basal area variables included correlation and relative mean squared error. For predicting species presence (setting thresholds to maximize Kappa), SGB had higher values for the majority of the species for specificity and Kappa, while GAMs had higher values for the majority of the species for sensitivity. In evaluating resultant AUC values, GAM and/or SGB models had significantly better results than the See5 models where significant differences could be detected between models. For nine out of 13 species, basal area prediction results for all modelling techniques were poor (correlations less than 0.5 and relative mean squared errors greater than 0.8), but SGB provided the most stable predictions in these instances. SGB and Cubist performed equally well for modelling basal area for three species with moderate prediction success, while all three modelling tools produced comparably good predictions (correlation of 0.68 and relative mean squared error of 0.56) for one species. ?? 2006 Elsevier B.V. All rights reserved.

Ecological Modelling↗