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The Millicoma Dace Rhinichthys cataractae is a form of Longnose Dace endemic to the Coos River drainage in southwestern Oregon. Sparse species records in the Oregon State University Ichthyology Collection and database and infrequent recent encounters prompted surveys to assess the current status and distribution of the species. In 2014, we surveyed locations that had historically supported Millicoma Dace using backpack electrofishing to describe their current distribution and abundance at these locations. In 2015, we extended these surveys further upstream in the South Coos River basin, outside of the documented historical range. We used an N-mixture model to estimate abundance and capture probability for Millicoma Dace at each sampling location. We evaluated the effects of habitat covariates on both capture probability and abundance at each sample site. We found Millicoma Dace were widespread throughout their historical range and in the South Coos River sites outside of their documented historical range. We only found Millicoma Dace associated with native fishes; we did not collect any nonnative fish during our surveys. We collected Millicoma Dace exclusively from swift-water habitats, which were relatively uncommon in the basin, and found them typically associated with cobble or boulder substrates. Millicoma Dace were most abundant in the South Fork Coos and West Fork Millicoma River subbasins. We estimated capture probabilities for Millicoma Dace ranging from 9% when substrate was dominated by bedrock to 28% when substrate was dominated by cobble or gravel. Abundance estimates ranged from 1 to 560 dace per sampling location with a total estimated abundance (sum of site estimates) of over 3200 dace for the sites we sampled. [ABSTRACT FROM AUTHOR]

Monitoring animal populations is central to wildlife and fisheries management, and the use of N-mixture models toward these efforts has markedly increased in recent years. Nevertheless, relatively little work has evaluated estimator performance when basic assumptions are violated. Moreover, diagnostics to identify when bias in parameter estimates from N-mixture models is likely is largely unexplored. We simulated count data sets using 837 combinations of detection probability, number of sample units, number of survey occasions, and type and extent of heterogeneity in abundance or detectability. We fit Poisson N-mixture models to these data, quantified the bias associated with each combination, and evaluated if the parametric bootstrap goodness-of-fit (GOF) test can be used to indicate bias in parameter estimates. We also explored if assumption violations can be diagnosed prior to fitting N-mixture models. In doing so, we propose a new model diagnostic, which we term the quasi-coefficient of variation (QCV). N-mixture models performed well when assumptions were met and detection probabilities were moderate (i.e., ≥0.3), and the performance of the estimator improved with increasing survey occasions and sample units. However, the magnitude of bias in estimated mean abundance with even slight amounts of unmodeled heterogeneity was substantial. The parametric bootstrap GOF test did not perform well as a diagnostic for bias in parameter estimates when detectability and sample sizes were low. The results indicate the QCV is useful to diagnose potential bias and that potential bias associated with unidirectional trends in abundance or detectability can be diagnosed using Poisson regression. This study represents the most thorough assessment to date of assumption violations and diagnostics when fitting N-mixture models using the most commonly implemented error distribution. Unbiased estimates of population state variables are needed to properly inform management decision making. Therefore, we also discuss alternative approaches to yield unbiased estimates of population state variables using similar data types, and we stress that there is no substitute for an effective sample design that is grounded upon well-defined management objectives. [ABSTRACT FROM AUTHOR]

Warner Sucker ( Catostomus warnerensis), endemic to the lakes and tributaries of the Warner basin, southeastern Oregon, USA, was listed as threatened by the US Fish and Wildlife Service in 1985 because of habitat fragmentation and threats from introduced nonnative fish. To determine progress towards recovery and document factors that may prevent recovery, we evaluated the distribution, abundance, and movements of this species between 2006 and 2013. The species exhibits 2 distinct life-history types, a lake morph and a stream morph. The abundance of suckers in the Warner Valley lakes has decreased since the mid-1990s, and the lake fish assemblages were dominated by nonnative predatory fishes. The basin experienced drought conditions from 2007 through 2010, and the lakes partially dried which affected both sucker distribution and movement. During the drought, when lake levels were low and receding, we found little evidence of sucker recruitment in the lake populations. In 2011, stream discharge was high and the lakes refilled, and in 2012 we documented successful sucker recruitment. Warner Suckers in the tributaries were widely distributed and relatively abundant, and the stream habitat was in good condition. We did not document substantial downstream movement of suckers during the spring and summer months and found little evidence of mixing between the stream and lake populations. We documented the upstream movement of large numbers of suckers in 2 tributary streams during the spawning season. We also described associations between sucker distribution and habitat characteristics in the tributaries. Our analysis indicated support for the threatened listing and suggests that recovery efforts would be most effective by focusing on improvements to both upstream and downstream passage at irrigation diversion dams and on irrigation efficiencies. [ABSTRACT FROM AUTHOR]