Patterns of bird species richness explained by annual variation in remotely sensed Dynamic Habitat Indices
Bird species richness is highly dependent on the amount of energy available in an ecosystem, with more available energy supporting higher species richness. A good indicator for available energy is Gross Primary Productivity (GPP), which can be estimated from satellite data.Our question was how tempo...
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| Autores principales: | , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/fa33e5b61135464abdf7eed1e894ec7a |
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| Sumario: | Bird species richness is highly dependent on the amount of energy available in an ecosystem, with more available energy supporting higher species richness. A good indicator for available energy is Gross Primary Productivity (GPP), which can be estimated from satellite data.Our question was how temporal dynamics in GPP affect bird species richness. Specifically, we evaluated the potential of the Dynamic Habitat Indices (DHIs) derived from MODIS GPP data together with environmental and climatic variables to explain annual patterns in bird richness across the conterminous United States. By focusing on annual DHIs, we expand on previous applications of multi-year composite DHIs, and could evaluate lag-effects between changes in GPP and species richness.We used 8-day GPP data from 2003 to 2013 to calculate annual DHIs, which capture three aspects of vegetation productivity: (1) annual cumulative productivity, (2) annual minimum productivity, and (3) annual seasonality expressed as the coefficient of variation in productivity. For each year from 2003 to 2013, we calculated total bird species richness and richness within six functional guilds, based on North American Breeding Bird Survey data.The DHIs alone explained up to 53% of the variation in annual bird richness within the different guilds (adjusted deviance-squared D2adj = 0.20–0.52), and up to 75% of the variation (D2adj = 0.28–0.75) when combined with other environmental and climatic variables. Annual DHIs had the highest explanatory power for habitat-based guilds, such as grassland (D2adj = 0.67) and woodland breeding species (D2adj = 0.75). We found some inter-annual variability in the explanatory power of annual DHIs, with a difference of 5–7 percentage points in explained variation among years in DHI-only models, and 3–7 points for models combining DHI, environmental and climatic variables. Our results using lagged year models did not deviate substantially from same-year annual models.We demonstrate the relevance of annual DHIs for biodiversity science, as effective predictors of temporal variation in species richness patterns. We suggest that the use of annual DHIs can improve conservation planning, by conveying the range of patterns of biodiversity response to global changes, over time. |
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