Conservation prioritization requires knowledge about organism distribution and density. This information is often inferred from models that estimate the probability of species occurrence rather than from models that estimate species abundance, because abundance data are harder to obtain and model. However, occurrence and abundance may not display similar patterns and therefore development of robust, scalable, abundance models is critical to ensuring that scarce conservation resources are applied where they can have the greatest benefits. Motivated by a dynamic land conservation program, we develop and assess a general method for modeling relative abundance using citizen science monitoring data. Weekly estimates of relative abundance and occurrence were compared for prioritizing times and locations of conservation actions for migratory waterbird species in California, USA. We found that abundance estimates consistently provided better rankings of observed counts than occurrence estimates. Additionally, the relationship between abundance and occurrence was nonlinear and varied by species and season. Across species, locations prioritized by occurrence models had only 10-58% overlap with locations prioritized by abundance models, highlighting that occurrence models will not typically identify the locations of highest abundance that are vital for conservation of populations.

Fishery‐dependent data are integral to sustainable fisheries management. A paucity of fishery data leads to uncertainty about stock status, which may compromise and threaten the economic and food security of the users dependent upon that stock and increase the chances of overfishing. Recent developments in the technology available to collect, manage and analyse fishery‐relevant data provide a suite of possible solutions to update and modernize fisheries data systems and greatly expand data collection and analysis. Yet, despite the proliferation of relevant consumer technology, integration of technologically advanced data systems into fisheries management remains the exception rather than the rule. In this study, we describe the current status, challenges and future directions of high‐tech data systems in fisheries management in order to understand what has limited their adoption. By reviewing the application of fishery‐dependent data technology in multiple fisheries sectors globally, we show that innovation is stagnating as a result of lack of trust and cooperation between fishers and managers. We propose a solution based on a transdisciplinary approach to fishery management that emphasizes the need for collaborative problem‐solving among stakeholders. In our proposed system, data feedbacks are a key component to effective fishery data systems, ensuring that fishers and managers collect, have access to and benefit from fisheries data as they work towards a mutually agreed‐upon goal. A new approach to fisheries data systems will promote innovation to increase data coverage, accuracy and resolution, while reducing costs and allowing adaptive, responsive, near real‐time management decision‐making to improve fisheries outcomes.

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