Quantitative indicators are a common means of assessing the complex dimensions of a sustainable freshwater system, and framing scientific knowledge for policy and decision makers. There is an abundance of indicators in use, but considerable variation in terms of what is being measured and how indicators are applied, making it difficult for end-users to identify suitable assessment methods. We review 95 water-related indices and analyze them along their normative, procedural, and systemic dimensions to better understand how problems are being defined, highlight overlaps and differences, and identify the context(s) in which a particular index is useful. We also analyze the intended use, end-users, and geographic scale of application for each index. We find that risk assessment is the most common application (n = 25), with indices in this group typically focusing either on hazard identification (biophysical assessments) or vulnerability of human populations. Indices that measure freshwater ecological health are not explicitly linking these indicators to ecosystem services, and in fact the concept of ecosystem services is rarely (n = 3) used for indicator selection. Resource managers are the most common group of intended end-users (n = 25), but while 28 indices involved consultation with potential end-users, 11 did not specify an intended use. We conclude that indices can be applied as solution-oriented tools, evaluating scenarios and identifying tradeoffs among services and beneficiaries, rather than only assessing and monitoring existing conditions. Finally, earlier engagement of end-users is recommended to help researchers find the right balance among indices’ salience, legitimacy, and credibility and thus improve their decision relevance.

Successful conservation plans are not solely achieved by acquiring optimally designed reserves. Ongoing monitoring and management of the biodiversity in those reserves is an equally important, but often neglected or poorly executed, part of the conservation process. In this paper we address one of the first and most important steps in designing a monitoring program – deciding what to monitor. We present a strategy for prioritizing species for monitoring and management in multispecies conservation plans. We use existing assessments of threatened status, and the degree and spatial and temporal extent of known threats to link the prioritization of species to the overarching goals and objectives of the conservation plan. We consider both broad and localized spatial scales to capture the regional conservation context and the practicalities of local management and monitoring constraints. Spatial scales that are commensurate with available data are selected. We demonstrate the utility of this strategy through application to a set of 85 plants and animals in an established multispecies conservation plan in San Diego County, California, USA. We use the prioritization to identify the most prominent risk factors and the habitats associated with the most threats to species. The protocol highlighted priorities that had not previously been identified and were not necessarily intuitive without systematic application of the criteria; many high‐priority species have received no monitoring attention to date, and lower‐priority species have. We recommend that in the absence of clear focal species, monitoring threats in highly impacted habitats may be a way to circumvent the need to monitor all the targeted species.

Species distribution and ecological niche models (hereafter SDMs) are popular tools with broad applications in ecology, biodiversity conservation, and environmental science. Many SDM applications require projecting models in environmental conditions non‐analog to those used for model training (extrapolation), giving predictions that may be statistically unsupported and biologically meaningless. We introduce a novel method, Shape, a model‐agnostic approach that calculates the extrapolation degree for a given projection data point by its multivariate distance to the nearest training data point. Such distances are relativized by a factor that reflects the dispersion of the training data in environmental space. Distinct from other approaches, Shape incorporates an adjustable threshold to control the binary discrimination between acceptable and unacceptable extrapolation degrees. We compared Shape's performance to five extrapolation metrics based on their ability to detect analog environmental conditions in environmental space and improve SDMs suitability predictions. To do so, we used 760 virtual species to define different modeling conditions determined by species niche tolerance, distribution equilibrium condition, sample size, and algorithm. All algorithms had trouble predicting species niches. However, we found a substantial improvement in model predictions when model projections were truncated independently of extrapolation metrics. Shape's performance was dependent on extrapolation threshold used to truncate models. Because of this versatility, our approach showed similar or better performance than the previous approaches and could better deal with all modeling conditions and algorithms. Our extrapolation metric is simple to interpret, captures the complex shapes of the data in environmental space, and can use any extrapolation threshold to define whether model predictions are retained based on the extrapolation degrees. These properties make this approach more broadly applicable than existing methods for creating and applying SDMs. We hope this method and accompanying tools support modelers to explore, detect, and reduce extrapolation errors to achieve more reliable models. Keywords: environmental novelty, extrapolation, Mahalanobis distance, model prediction, non‐analog environmental data, transferability

Habitat loss is widely considered the greatest threat to biodiversity. However, habitat loss brings with it myriad other threats that exacerbate impacts to biodiversity. For instance, altered fire regime is associated with habitat loss and fragmentation with unknown consequences to biodiversity. Plant functional groups that rely on fire to complete their life cycle may be adversely affected by disruptions to the natural fire regime, particularly when coupled with population declines due to habitat loss. We used a spatially explicit stochastic population model linked with fire hazard functions to investigate the cumulative effects of habitat loss, fragmentation, and altered fire regime on the expected minimum abundance of a long‐lived obligate‐seeding shrub,Ceanothus greggiivar.perplexans. This species is endemic to the California Floristic Province, a biodiversity hotspot, and is representative of a functional group of plants found in many fire‐prone ecosystems. We tested the impact of a range of different fire frequencies under three different combinations of fuel accumulation and weather. The best average fire return interval for population abundance was consistently in the range of 30–50 years. However, observed average fire return intervals in highly fragmented areas can be approximately 20 years or less, and model results show this to be detrimental toC. greggiipopulations. Results also show that if fires are uncorrelated across habitat fragments then the impact of altered fire regime on populations is worse than the impact of habitat fragmentation because of spatial and temporal decoupling of fire events across the landscape. However, the negative impacts of altered fire regime are outweighed by habitat loss as fragmentation increases. Our results show that large unplanned fires, operating under an altered fire regime, are ultimately detrimental to perennial obligate‐seeding shrubs in fragmented landscapes.