Objective: Predict the effects of climate change on avian abundance by
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Objective: Predict the effects of climate change on avian abundance by modeling the response of species to weather conditions over 16 years San Joaquin Experimental Range Dependent variable: Count -- summed over route for each observer each year. Explanatory variables (non-climate): • Year • Route • Sampling day (Julian date) Precipitation variables (9) Annual precipitation from 1 May – 30 April of counting year 2-, 3-, 4-, and 5-year running averages of precipitation Winter precipitation (Oct – March) of counting year and 1, 2, 3 years prior Temperature variables (5) Mean spring (Mar- May) temperature Mean summer (June – Aug) temperature Mean fall (Sept – Nov) Mean winter (Dec – Feb) temperature Minimum winter temp preceding counts ENSO – Southern Oscillation Index (SOI) (5 variables) Mean values for current calendar year and 1, 2 years prior Mean values for April – March of preceding year and 2 years prior high, positive values indicate cold, dry La Niña conditions low, negative values indicate warm, wet El Niño conditions Statistical Analysis • Nonparametric Poisson regression models (GAM with Loess smoothing, SPlus) to determine the functional shapes of relations between counts and explanatory variables. • Parametric overdispersed Poisson regression analysis (GLM, SAS) used functional shapes for the explanatory variables suggested by the nonparametric analyses (polynomial). ACWO -0.6 0.5 0.1 0.0 1.0 60 Dry &cold mp1 64 66 tsn 0.2 75 76 77 78 tja 79 80 -0.1 partial for route 0.1 0.1 -0.1 Precipitation Location -0.3 -0.3 lo(py1, span = 0.5) 0.2 0.0 -0.2 Summer temperature -0.6 -0.4 lo(tja) 62 G 0.0 F -1.0 A yr -2.0 Wet & hot E 2000 D 1995 C 1990 B 1985 Autumn temperature -0.2 SOI -0.1 lo(tsn, span = 0.8) 0.2 0.0 -0.2 0.0 0.2 lo(mp1, span = 0.6) Annual trend -0.4 lo(yr) 0.4 0.6 SPLUS partial residuals using GAM (62.8% reduction in deviance ) 10 15 20 py1 25 route Parametric Overdispersed Poisson model: variables selected with SAS Log(count)= a*year+b*year2+c*mp1+d*mp12+e*py1+f*py12+g*tsn+h*tsn2+l*tja+route ANHU 0.0 0.2 SOI -0.4 -0.2 lo(mp0, span = 0.75) 0.0 0.4 Annual trend -0.4 A -0.1 -0.5 0.0 0.5 1.0 Dry &cold mp0 0.2 Location -0.4 0.0 0.0 partial for route 0.4 Precipitation 0.1 -1.0 Wet & hot -0.2 lo(py1, span = 0.75) -1.5 G -2.0 F 2000 E 1995 yr D 1990 C 1985 B lo(yr) 0.4 SPLUS partial residuals using GAM (33.6% reduction in deviance ) 10 15 20 py1 25 route Parametric Overdispersed Poisson model: variables selected with SAS Log(count)= a*year+b*mp0+c*py1+route EUST -0.3 -0.1 120 12 14 16 days 18 20 22 24 pav3 0.2 Location -0.6 -0.2 partial for route 0.1 0.0 SOI -0.2 lo(mp0, span = 0.7) -0.2 Summer temperature -0.6 lo(tja) 0.2 0.4 A year 110 G 100 F 90 E 2000 D 1995 C 1990 B 1985 Precipitation 0.1 lo(pav3, span = 0.6) 0.3 lo(days) 0.1 0.4 0.2 0.0 -0.4 lo(year) Julian day Annual trend -0.1 0.6 SPLUS partial residuals using GAM (48.9% reduction in deviance ) 75 76 77 78 tja 79 80 -2.0 -1.0 0.0 mp0 0.5 1.0 route Parametric Overdispersed Poisson model: variables selected with SAS Log(count)= a*year+b*days+c*days2+ d*pav3+e*mp0+f*mp02+ g*tja+route NUWO 0.2 -0.6 120 75 76 15 20 py4 25 30 79 80 0.2 0.1 partial for route SOI Location -0.2 0.0 0.3 78 tja -0.1 lo(mp0, span = 0.7) 0.4 0.2 0.0 -0.4 lo(py4, span = 0.7) Precipitation 10 77 days A year 110 G 100 F 90 E 2000 D 1995 C 1990 Summer temperature B 1985 -0.2 0.0 -0.2 -0.2 lo(tja, span = 0.5) 0.2 Julian day lo(days) 0.2 Annual trend -0.6 lo(year) 0.6 0.4 SPLUS partial residuals using GAM (41.4% reduction in deviance ) -2.0 -1.0 0.0 mp0 0.5 1.0 route Parametric Overdispersed Poisson model: variables selected with SAS Log(count)= a*year+b*yr2+c*days+d*py4+e*mp0+f*tja+route OATI -0.2 120 75 76 20 py1 25 79 80 C 0.2 0.1 partial for route 0.0 0.2 0.1 lo(mp0, span = 0.6) SOI Location -0.2 -0.1 15 78 tja 0.0 0.2 0.1 0.0 -0.2 lo(py1, span = 0.7) Precipitation 10 77 days A year 110 G 100 F 90 E 2000 D 1995 B 1990 0.0 0.2 -0.3 1985 Summer temperature -0.4 -0.1 0.1 lo(days) 0.0 lo(tja, span = 0.6) Julian day 0.2 0.4 Annual trend -0.4 lo(year) 0.3 0.6 SPLUS partial residuals using GAM (58.4% reduction in deviance ) -2.0 -1.0 0.0 mp0 0.5 1.0 route Parametric Overdispersed Poisson model: variables selected with SAS Log(count)= a*year+b*yr2+c*days+d*day2+e*py1+f*mp0+g*tja+route WESJ 0.4 -0.4 120 75 76 77 78 79 80 tja 10 15 20 py1 25 0.1 partial for route 0.0 -0.1 0.10 SOI Location -0.3 -0.10 lo(mp0, span = 0.7) -0.2 0.0 0.2 Precipitation -0.4 lo(py1, span = 0.6) 0.4 C days A year 110 G 100 F 90 E 2000 D 1995 B 1990 0.2 lo(tja, span = 0.6) -0.10 1985 Summer temperature 0.0 0.20 Julian day 0.10 lo(days) 0.2 0.0 -0.4 lo(year) 0.4 Annual trend 0.0 0.6 SPLUS partial residuals using GAM (55% reduction in deviance ) -2.0 -1.0 0.0 mp0 0.5 1.0 route Parametric Overdispersed Poisson model: variables selected with SAS Log(count)= a*year+b*year2+c*days+d*days2+e*mp0+f*mp02+g*mp03+ h*tja+i*py1+j*py12+route SOI Summer temp Winter temp Acorn Woodpecker È Anna’s Hummingbird È È ÈÇ Ç Ash-throated Flycatcher Bushtit È California Quail È È Precip È Ç Ç È È Common Raven European Starling È House Finch È House Wren È È ÇÈ Lesser Goldfinch È È ÇÈ Mourning Dove È Nuttall’s Woodpecker È È Ç Oak Titmouse È È È Rufous-crowned Sparrow È È È White-breasted Nuthatch ÈÇ È ÇÈ È È È Western Scrub-jay È Ç Ç È Ç È Calendar year minimum temperatures at SJER 1935-2002 35 Minimum temperature 30 Tmin=-0.52.3+0.037*year P-value for slope = 0.071 25 20 15 10 1930 1940 1950 1960 1970 Year 1980 1990 2000 2010 Calendar year minimum temperatures at SJER 1935-2002 35 Loess smoothing Minimum temperature 30 25 20 15 10 1930 1940 1950 1960 1970 Year 1980 1990 2000 2010
