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Abstract Environmental and anthropogenic factors affect the population dynamics of migratory species throughout their annual cycles. However, identifying the spatiotemporal drivers of migratory species' abundances is difficult because of extensive gaps in monitoring data. The collection of unstructured opportunistic data by volunteer (citizen science) networks provides a solution to address data gaps for locations and time periods during which structured, design‐based data are difficult or impossible to collect.To estimate population abundance and distribution at broad spatiotemporal extents, we developed an integrated model that incorporates unstructured data during time periods and spatial locations when structured data are unavailable. We validated our approach through simulations and then applied the framework to the eastern North American migratory population of monarch butterflies during their spring breeding period in eastern Texas. Spring climate conditions have been identified as a key driver of monarch population sizes during subsequent summer and winter periods. However, low monarch densities during the spring combined with very few design‐based surveys in the region have limited the ability to isolate effects of spring weather variables on monarchs.Simulation results confirmed the ability of our integrated model to accurately and precisely estimate abundance indices and the effects of covariates during locations and time periods in which structured sampling are lacking. In our case study, we combined opportunistic monarch observations during the spring migration and breeding period with structured data from the summer Midwestern breeding grounds. Our model revealed a nonstationary relationship between weather conditions and local monarch abundance during the spring, driven by spatially varying vegetation and temperature conditions.Data for widespread and migratory species are often fragmented across multiple monitoring programs, potentially requiring the use of both structured and unstructured data sources to obtain complete geographic coverage. Our integrated model can estimate population abundance at broad spatiotemporal extents despite structured data gaps during the annual cycle by leveraging opportunistic data.
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Grappling with uncertainty in ecological projections: a case study using the migratory monarch butterfly
Abstract Projecting species’ responses to future climate conditions is critical for anticipating conservation challenges and informing proactive policy and management decisions. However, best practices for choosing climate models for projection ensembles are currently in flux. We compared including a maximum number of models against trimming ensembles based on model validation. This was done within the emerging practice of ensemble building using an increasingly larger number of global climate models (GCMs) for future projections. We used recently reported estimates on primary drivers of population fluctuations for the migratory monarch butterfly (Danaus plexippus) to examine how multiple sources of uncertainty impact population forecasts for a well‐studied species. We compared mean spring temperature and precipitation observed in Texas from 1980 to 2005 with predictions from 16 GCMs to determine which of the models performed best. We then built tailored climate projections accumulating both temperature (in the form of growing degree days) and rainfall using both “complete” (all 16) and “trimmed” (best‐performing) ensembles based on three emission scenarios. We built the tailored projections of spring growing conditions to assess the range of possible climate outcomes and their potential impacts on monarch development. Results were similar when mean predictions were compared between trimmed and complete ensembles. However, when daily projections and uncertainty were accumulated over the entire spring season, we showed substantial differences between ensembles in terms of possible ecological outcomes. Ensembles that used all 16 GCMs included so much uncertainty that projections for future spring conditions ranged from being too cold to too hot for monarch development. GCMs based on best‐performing metrics provided much more useful information, projecting higher spring temperatures for developing monarch larvae in the future which could lead to larger summer populations but also suggesting risk from excessive heat. When there is a strong basis for identifying mechanistic drivers of population dynamics, our results support using a smaller subset of validated GCMs to bracket a range of the most defensible future environmental conditions tailored to the species of interest. Yet understating uncertainty remains a risk, and we recommend clearly articulating the rationale and consequences of selecting GCMs for long‐term projections.
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- PAR ID:
- 10362233
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Ecosphere
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2150-8925
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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