Identifying Preservation and Restoration Priority Areas for Desert Fishes in an Increasingly Invaded World

A commonly overlooked aspect of conservation planning assessments is that wildlife managers are increasingly focused on habitats that contain non-native species. We examine this management challenge in the Gila River basin (150,730 km²), and present a new planning strategy for fish conservation. By applying a hierarchical prioritization algorithm to >850,000 fish records in 27,181 sub-watersheds we first identified high priority areas (PAs) termed “preservation PAs” with high native fish richness and low non-native richness; these represent traditional conservation targets. Second, we identified “restoration PAs” with high native fish richness that also contained high numbers of non-native species; these represent less traditional conservation targets. The top 10 % of preservation and restoration PAs contained common native species (e.g., Catostomus clarkii, desert sucker; Catostomus insignis, Sonora sucker) in addition to native species with limited distributions (i.e., Xyrauchen texanus, razorback sucker; Oncorhynchus gilae apache, Apache trout). The top preservation and restoration PAs overlapped by 42 %, indicating areas with high native fish richness range from minimally to highly invaded. Areas exclusively identified as restoration PAs also encompassed a greater percentage of native species ranges than would be expected by the random addition of an equivalent basin area. Restoration PAs identified an additional 19.0 and 26.6 % of the total ranges of two federally endangered species—Meda fulgida (spikedace) and Gila intermedia (Gila chub), respectively, compared to top preservation PAs alone—despite adding only 5.8 % of basin area. We contend that in addition to preservation PAs, restoration PAs are well suited for complementary management activities benefiting native fishes.     

Quantitative approaches to the analysis of stable isotope food web data

Ecologists use stable isotopes (delta13C, delta15N) to better understand food webs and explore trophic interactions in ecosystems. Traditionally, delta13C vs. delta15N bi-plots have been used to describe food web structure for a single time period or ecosystem. Comparisons of food webs across time and space are increasing, but development of statistical approaches for testing hypotheses regarding food web change has lagged behind. Here we present statistical methodologies for quantitatively comparing stable isotope food web data. We demonstrate the utility of circular statistics and hypothesis tests for quantifying directional food web differences using two case studies: an arthropod salt marsh community across a habitat gradient and a freshwater fish community from Lake Tahoe, USA, over a 120-year time period. We calculated magnitude and mean angle of change (theta) for each species in food web space using mean delta13C and delta15N of each species as the x, y coordinates. In the coastal salt marsh, arthropod consumers exhibited a significant shift toward dependence on Spartina, progressing from a habitat invaded by Phragmites to a restored Spartina habitat. In Lake Tahoe, we found that all species from the freshwater fish community shifted in the same direction in food web space toward more pelagic-based production with the introduction of nonnative Mysis relicta and onset of cultural eutrophication. Using circular statistics to quantitatively analyze stable isotope food web data, we were able to gain significant insight into patterns and changes in food web structure that were not evident from qualitative comparisons. As more ecologists incorporate a food web perspective into ecosystem analysis, these statistical tools can provide a basis for quantifying directional food web differences from standard isotope data.     

Life history theory predicts fish assemblage response to hydrologic regimes

The hydrologic regime is regarded as the primary driver of freshwater ecosystems, structuring the physical habitat template, providing connectivity, framing biotic interactions, and ultimately selecting for specific life histories of aquatic organisms. In the present study, we tested ecological theory predicting directional relationships between major dimensions of the flow regime and life history composition of fish assemblages in perennial free-flowing rivers throughout the continental United States. Using long-term discharge records and fish trait and survey data for 109 stream locations, we found that 11 out of 18 relationships (61%) tested between the three life history strategies (opportunistic, periodic, and equilibrium) and six hydrologic metrics (two each describing flow variability, predictability, and seasonality) were statistically significant (P < or = 0.05) according to quantile regression. Our results largely support a priori hypotheses of relationships between specific flow indices and relative prevalence of fish life history strategies, with 82% of all significant relationships observed supporting predictions from life history theory. Specifically, we found that (1) opportunistic strategists were positively related to measures of flow variability and negatively related to predictability and seasonality, (2) periodic strategists were positively related to high flow seasonality and negatively related to variability, and (3) the equilibrium strategists were negatively related to flow variability and positively related to predictability. Our study provides important empirical evidence illustrating the value of using life history theory to understand both the patterns and processes by which fish assemblage structure is shaped by adaptation to natural regimes of variability, predictability, and seasonality of critical flow events over broad biogeographic scales.     

Assessing ecosystem vulnerability to invasive rusty crayfish (Orconectes rusticus)

Despite the widespread introduction of nonnative species and the heterogeneity of ecosystems in their sensitivity to ecological impacts, few studies have assessed ecosystem vulnerability to the entire invasion process, from arrival to establishment and impacts. Our study addresses this challenge by presenting a probabilistic, spatially explicit approach to predicting ecosystem vulnerability to species invasions. Using the freshwater-rich landscapes of Wisconsin, USA, we model invasive rusty crayfish (Orconectes rusticus) as a function of exposure risk (i.e., likelihood of introduction and establishment of O. rusticus based on a species distribution model) and the sensitivity of the recipient community (i.e., likelihood of impacts on native O. virilis and O. propinquus based on a retrospective analysis of population changes). Artificial neural networks predicted that approximately 10% of 4200 surveyed lakes (n = 388) and approximately 25% of mapped streams (23 523 km total length) are suitable for O. rusticus introduction and establishment. A comparison of repeated surveys before vs. post-1985 revealed that O. virilis was six times as likely and O. propinquus was twice as likely to be extirpated in streams invaded by O. rusticus, compared to streams that were not invaded. Similarly, O. virilis was extirpated in over three-quarters of lakes invaded by O. rusticus compared to half of the uninvaded lakes, whereas no difference was observed for O. propinquus. We identified 115 lakes (approximately 3% of lakes) and approximately 5000 km of streams (approximately 6% of streams) with a 25% chance of introduction, establishment, and extirpation by O. rusticus of either native congener. By identifying highly vulnerable ecosystems, our study offers an effective strategy for prioritizing on-the-ground management action and informing decisions about the most efficient allocation of resources. Moreover, our results provide the flexibility for stakeholders to identify priority sites for prevention efforts given a maximum level of acceptable risk or based on budgetary or time restrictions. To this end, we incorporate the model predictions into a new online mapping tool with the intention of closing the communication gap between academic research and stakeholders that requires information on the prospects of future invasions.     

Trait synergisms and the rarity, extirpation, and extinction risk of desert fishes

Understanding the causes and consequences of species extinctions is a central goal in ecology. Faced with the difficult task of identifying those species with the greatest need for conservation, ecologists have turned to using predictive suites of ecological and life-history traits to provide reasonable estimates of species extinction risk. Previous studies have linked individual traits to extinction risk, yet the nonadditive contribution of multiple traits to the entire extinction process, from species rarity to local extirpation to global extinction, has not been examined. This study asks whether trait synergisms predispose native fishes of the Lower Colorado River Basin (USA) to risk of extinction through their effects on rarity and local extirpation and their vulnerability to different sources of threat. Fish species with "slow" life histories (e.g., large body size, long life, and delayed maturity), minimal parental care to offspring, and specialized feeding behaviors are associated with smaller geographic distribution, greater frequency of local extirpation, and higher perceived extinction risk than that expected by simple additive effects of traits in combination. This supports the notion that trait synergisms increase the susceptibility of native fishes to multiple stages of the extinction process, thus making them prone to the multiple jeopardies resulting from a combination of fewer individuals, narrow environmental tolerances, and long recovery times following environmental change. Given that particular traits, some acting in concert, may differentially predispose native fishes to rarity, extirpation, and extinction, we suggest that management efforts in the Lower Colorado River Basin should be congruent with the life-history requirements of multiple species over large spatial and temporal scales.     

Cross-correlation bias in lag analysis of aquatic time series

Cross-correlation analysis is the most valuable and widely used statistical tool for evaluating the strength and direction of time-lagged relationships between ecological variables. Although it is well understood that temporal autocorrelation can inflate estimates of cross correlations and cause high rates of incorrectly concluding that lags exist among time series (i.e. type I error), in this study we show that a problem we term intra-multiplicity can cause substantial bias in cross-correlation analysis even in the absence of autocorrelation. Intra-multiplicity refers to the numerous time lags examined and cross-correlation coefficients computed within a pair of time series during cross-correlation analysis. We show using Monte Carlo simulations that intra-multiplicity can spuriously inflate estimates of cross correlations by identifying incorrect time lags. Further, unlike autocorrelation, which generally identifies lags close to the true lag, intra-multiplicity can erroneously identify lags anywhere in the time series and commonly results in a direction change of the correlation (i.e. positive or negative). Using Monte Carlo simulations we develop formulas that quantify the bias introduced by intra-multiplicity as a function of sample size, true cross correlation between the series, and the number of time lags examined. A priori these formulas enable researchers to determine the sample size needed to minimize the biases introduced by intra-multiplicity. A posteriori the formulas can be used to predict the expected bias and type I error rate associated with the data at hand, as well as the maximum number of time lags that can be analyzed to minimize the effects of intra-multiplicity. We examine the relationship between commercial catch of chum salmon and surface temperatures of the North Pacific (1925–1992) to illustrate the problems of intra-multiplicity in fisheries studies and the application of our formulas. These analyses provide a more robust framework to assess the temporal relationships between ecological variables. Received: 28 July 2000 / Accepted: 6 December 2000