Disentangling spatial structure in ecological communities. Dan McGlinn & Allen Hurlbert.

Transcription

1 Disentangling spatial structure in ecological communities Dan McGlinn & Allen Hurlbert

2 The Unified Theories of Biodiversity 6 unified theories of diversity in the last 10 years (McGill 2010) Theories Patterns Maxent Species ranges Neutral Gen. Fractal Continuum Clustered poisson Metapopulation Community Assembly Endemics area Local SAD SAR Decay similarity

3 Community Assembly McGill (2010) Theories Maxent Neutral Gen. Fractal Continuum Clustered poisson Metapopulation Regional Species Pool Dispersal Filter Environmental Filter Local community Patterns Species ranges Endemics area Local SAD SAR Decay similarity

4 Three Shared Assertions McGill (2010) Theories Assertions Patterns Maxent Neutral Gen. Fractal Continuum Community Assembly 1. Interspecific variation in global abundance 2. Intraspecific aggregation Species ranges Endemics area Local SAD Clustered poisson Metapopulation 3. Interspecific independence SAR Decay similarity

5 Three Shared Assertions McGill (2010) Theories Assertions Patterns Maxent Neutral Gen. Fractal Continuum Community Assembly 1. Interspecific variation in global abundance 2. Intraspecific aggregation Species ranges Endemics area Local SAD Clustered poisson Metapopulation 3. Interspecific independence SAR Decay similarity

6 Spatial Map Species 1 Both Species Species 2

7 Spatial Map Species 1 Both Species Species 2 Community Matrix (x ik ) Sp. 1 Sp. 2 Sp. 3 Sp. s Samp Samp Samp Samp. n Covariance Matrix (c ij ) Sp. 1 Sp. 2 Sp. 3 Sp. s Sp. 1 + Sp Sp Sp. s Cov( x i, x j ) n = 1 n 1 k = 1 ( x ik x i ) ( x jk x j )

8 Community Variogram Spatially Implicit Covariance Matrix (c ij ) Spatially Explicit Covariance Matrix (c ij (h)) Sp. 1 + Sp. 1 Sp. 2 Sp. 3 Sp. s Sp c ij Sp Sp. s n = 1 ( x n 1 k = 1 ik x i ) ( x jk x j ) c ij ( h) 1 = ( x 2n h a, b h ab h 1 ia 2 x ib Spatial Lag, h )( x ja x h max jb ) Wagner (2003)

9 Spatial Map Species 1 Both Species Species 2 Intra-specific Aggregation Within-Sp. Var. Random Null

10 Spatial Map Species 1 Both Species Species 2 Intra-specific Aggregation Within-Sp. Var. Random Null Inter-specific Association Total Between-Sp. Cov. Spatial Null

11 Total Covariance is a Blunt Metric Multi-modal Leptokurtic Right-skewed Left-skewed 0 Observed Null 0 Cov( x i, x j h) 0 n 1 = ( x 2n h a, b h ab h ia x ib ) ( x ja 0 x jb )

12 Total Covariance is a Blunt Metric Multi-modal Leptokurtic Right-skewed Left-skewed 0 Observed Null Negative 1 = (1 0)(0 1) 0 Cov( x i, x j Positive +1 = (1 0)(1 0) 0 n 1 h) = ( x 2n h a, b h ab h ia x ib ) ( x ja 0 x jb ) Species 1 Both Species Segregation Units (aka, the C-score) Aggregation Units Species 2 Neither Species

13 Spatial Map Species 1 Both Species Species 2 Intra-specific Aggregation Within-Sp. Var. Random Null Inter-specific Association Total Between-Sp. Cov. Spatial Null Inter-specific Association Pos. Between-Sp. Cov. Pos. Spatial Null Neg. Between-Sp. Cov. Neg. Spatial Null

14 Purpose To Combine 1. Community variogram 2. Null modeling 3. Simulation model to describe and understand the drivers of avian community structure

15 Inferential Framework Empirical Community (Unknown Process Strength)? Simulated Community (Known Process Strength) Weak Med. Strong

16 Inferential Framework Empirical Community (Unknown Process Strength)? Simulated Community (Known Process Strength) Weak Med. Strong

17 Inferential Framework Empirical Community (Unknown Process Strength)? Simulated Community (Known Process Strength) Weak Med. Strong z Var z Cov+ & z Cov- Find Best Match z Var z Cov+ & z Cov- z Var z Cov+ & z Cov- z Var z Cov+ & z Cov-

18 Inferential Framework Empirical Community (Unknown Process Strength)? Simulated Community (Known Process Strength) Weak Med. Strong z Var z Cov+ & z Cov- Find Best Match z Var z Cov+ & z Cov- z Var z Cov+ & z Cov- z Var z Cov+ & z Cov- Standardized Effect Size, z Empirical Simulated Spatial Lag, h

19 Basic Approach Develop expectations via simulations for: Environmental filtering Niche width Dispersal limitation Dispersal width Compare simulations with empirical patterns: Compute standardized effect sizes Which parameters have the smallest total deviations

20 Sampling Design Route locations East & West comparison 5-year period 14,102 route surveys 100 x 100 km grid cells 1800 x 1800 km extents NDVI gradient 90% of the cells contain 2 BBS routes 20% land cover

21 Sampling Design NDVI & Sampling Grids East & West comparison 5-year period 14,102 route surveys 100 x 100 km grid cells 1800 x 1800 km extents NDVI gradient 90% of the cells contain 2 BBS routes 20% land cover

22 Simulation Framework Species Pool Landscape Individual based, spatially explicit Conceptualized by Graham Bell neutral.vp Smith and Lundholm (2010) Patch carrying capacity, K = 500 Habitat Patch

23 Simulation Framework Species Pool Landscape Individual based, spatially explicit Conceptualized by Graham Bell neutral.vp Smith and Lundholm (2010) Patch carrying capacity, K = 500 Birth and death are determined by environmental optima niche width, σ Envi = [0.01 to 1] Environmental Optima Probability NDVI

24 Simulation Framework Species Pool Landscape Individual based, spatially explicit Conceptualized by Graham Bell neutral.vp Smith and Lundholm (2010) Patch carrying capacity, K = 500 Birth and death are determined by environmental optima niche width, σ Envi = [0.01 to 1] Dispersal is determined by dispersal width, σ Disp = [0.01 to 1] Probability High Disperal Limitation Medium Disperal Limitation Low Disperal Limitation Distance from Natal Patch

25 Simulation Framework Species Pool Landscape Individual based, spatially explicit Conceptualized by Graham Bell neutral.vp Smith and Lundholm (2010) Patch carrying capacity, K = 500 Birth and death are determined by environmental optima niche width, σ Envi = [0.01 to 1] Dispersal is determined by dispersal width, σ Disp = [0.01 to 1] Migration from the pool Prevents random drift to monoculture Migration rate, m = σ Disp

26 Intra-specific Aggregation Total Inter-specific Association West Positive & Negative Inter-specific Association Variance Variance Empirical Pattern 95% C.I. of Null Model Covariance Covariance East Covariance Covariance Spatial lag (km)

27 Residual Surfaces West East Decreasing Dispersal Limitation (σ Disp ) Decreasing Environmental Filtering (σ Envi )

28 Intra-specific Aggregation Standard Effect Size, z Σabs(residuals) = 19 Observed z Simulated z Positive Inter-specific Association West Σabs(residuals) = Negative Inter-specific Association Σabs(residuals) = 29 Standard Effect Size, z Σabs(residuals) = 22 Observed z Simulated z East Σabs(residuals) = Spatial lag (km) Σabs(residuals) =

29 Summary of Results Fractal-like pattern of intraspecific aggregation West: positive associations at all scales Due to more aggregation units rather than fewer segregation units East: negative associations at all scales Due to more segregation units rather than fewer aggregation units Patterns consistent with Medium to strong environmental filtering Weak to medium dispersal limitation

30 Landscape Structure and Bird Communities Map of Human Footprint SEDAC 2010 Eastern Birds species likely segregating along suburban-forest gradient

31 Landscape Structure and Bird Communities Western Birds species likely aggregating in areas of high productivity

32 Conclusions & Future Directions Spatial patterns of aggregations & associations can help to yield insight into the drivers of community assembly

33 Conclusions & Future Directions Spatial patterns of aggregations & associations can help to yield insight into the drivers of community assembly Key to this insight is the coupling of a spatial null model with a spatially explicit simulation framework

34 Conclusions & Future Directions Spatial patterns of aggregations & associations can help to yield insight into the drivers of community assembly Key to this insight is the coupling of a spatial null model with a spatially explicit simulation framework Questions remain: Incorporating data on environmental variables Field-based measurements of dispersal kernels Addition of phylogenetic scale

35 Thanks! Allen Hurlbert Mike Palmer, James Stegen, & Jes Coyle Maurine Gilmore UNC & USU graduate students!

36 Motivating Problems Aggregations & associations do not necessarily imply strength of a particular process, a priori, although we usually have a short list in mind. Process can be inferred from experiments: numerical or manipulative

37 Motivating Problems Aggregations & associations do not necessarily imply strength of a particular process, a priori, although we usually have a short list in mind. Process can be inferred from experiments: numerical or manipulative Most macroecological patterns are insensitive to associations between species i.e., models of species independence are successful not because species are actually independent

38 Motivating Problems Aggregations & associations do not necessarily imply strength of a particular process, a priori, although we usually have a short list in mind. Process can be inferred from experiments: numerical or manipulative Most macroecological patterns are insensitive to associations between species i.e., models of species independence are successful not because species are actually independent Spatial components of aggregation and association are not typically quantified simultaneously Exception: The Community Variogram

39 Spatial Null Model Observed Distribution Random Null Spatial Null

40 Spatial Null Model Observed Distribution Random Null Spatial Null

41 Spatial Null Model Observed Distribution Random Null Spatial Null Spatial Null model is applied to each species independently to: maintain within species dependencies nullify between species dependencies

42 SARs & DDRs Log Area Log Area Log Spatial Lag Log Similarity Log Richness Log Similarity Log Richness Log Spatial Lag

43 Intra-specific Aggregation Total Inter-specific Association West Positive & Negative Inter-specific Association Variance Variance Empirical Pattern 95% C.I. of Null Model Covariance Covariance East Covariance Covariance Spatial lag (km)

44 Residual Surfaces Non-Spatial Analysis Spatial Analysis

45 Structure of the Environment Gradient Random Assumptions of most ecological models

46 Structure of the Environment Gradient Random Reality somewhere here

47 Empirical Dataset Breeding Bird Survey 5-year period ; 14,102 route surveys 100 x 100 km sampling grid Route locations Route Density Average Route Species Richness

48 Simulation Framework Species Pool Landscape Individual based, spatially explicit Conceptualized by Graham Bell neutral.vp Smith and Lundholm (2010) Patch carrying capacity, K = 500 Birth and death are determined by environmental optima niche width, σ Envi = [0.01 to 1] Dispersal is determined by dispersal width, σ Disp = [0.01 to 1]

49 Community Assembly 6 unified theories of diversity in the last 10 years (McGill 2010) Theories Neutral Regional Species Pool Dispersal Filter Environmental Filter Local community Patterns Species ranges Endemics area Local SAD SAR Decay similarity

50 Community Assembly Theories Regional Species Pool Patterns Species ranges Continuum Dispersal Filter Environmental Filter Local community Endemics area Local SAD SAR Decay similarity

51 Detection Problem Type I errors Falsely detecting an association when none exist given the species spatial patterns of aggregation Type II errors Failing to detect a associations even though there are strong patterns of positive and negative covariance

52 Are these species associated? Species 1 Both Species Species 2 Neither Species

53 Are these species associated? Species 1 Both Species Species 2 Neither Species

54 Are these species associated? Species 1 Both Species Species 2 Neither Species

55 Are these species associated? Species 1 Both Species Species 2 Neither Species