nicheoverlap.RdFunctions nicheoverlap and nichedispl compute the overlap and centroid distance between pairs of resource distributions. In both cases resource relationships are given in the distance matrix D and the resource use data are given in data frame P1 (and in some modes also P2).
nichedispl(
P1,
P2 = NULL,
D = NULL,
q1 = NULL,
q2 = NULL,
mode = "multiple",
Np1 = NULL,
Np2 = NULL,
Nq1 = NULL,
Nq2 = NULL,
nboot = 1000,
alpha = 0.05
)
nicheoverlap(
P1,
P2 = NULL,
D = NULL,
q1 = NULL,
q2 = NULL,
mode = "multiple",
Np1 = NULL,
Np2 = NULL,
Nq1 = NULL,
Nq2 = NULL,
nboot = 1000,
alpha = 0.05
)Data frame containing the amount of usage that a set of species (in rows) make of a first set of resources (in columns)
Data frame containing the amount of usage that a set of species (in rows) make of a second set of resources (in columns). Not used if mode = "pairwise"
Object of type dist containing distance values between resources. If no distance matrix is provided (i.e. if D==NULL), the distances between resources is assumed to be maximum
Vector with the availability of each resource corresponding to P1
Vector with the availability of each resource corresponding to P2
Either mode = "single" (rows of matrices P1 and P2 are individual observations to be pooled, for example to compare the niche of two species each with its individual observations), mode = "multiple" (each row in P1 is compared to the corresponding row of P2, for example, to compare seasonal niche shifts in each species) or mode = "pairwise" (all rows in P1 are compared pairwise).
Vector with the number of observations per species from which the values in P1 come (in mode = "multiple" or mode = "pairwise").
Vector with the number of observations per species from which the values in P2 come (only in mode = "multiple").
The number of observations from which the values in q1 come.
The number of observations from which the values in q2 come.
Number of boostrap samples used to compute bias-corrected percentile confidence intervals.
Used to set the confidence level (i.e. alpha = 0.05 means 95 percent confidence interval).
Function nicheoverlap (resp. nichedispl) returns the overlap (resp. the distance between centroids) between the each pair of rows in P1 and P2. If mode = "multiple" or mode = "single" the values are returned as a data frame. If mode = "pairwise" a matrix of values is returned instead. If bootstrap confidence intervals are asked then the functions also compute the lower and upper bounds of a confidence interval obtained following the bias-corrected percentile method. Upper and lower bounds are returned as additional columns of the data frame in mode = "multiple" or mode = "single" or as additional matrices of a list in mode = "pairwise".
The method is described in De Caceres et al. (2011). If the distance matrix is not specified (i.e. if D=NULL) the function assumes that all resources are at a maximum distance (d=1). If the resource availability vector q1 (and q2 if supplied) is specified, then the values in P1 (and P2 if supplied) are taken as assessments of resource use and the species preference is calculated taking into account resource availability. Otherwise, resource use is equated to resource preference (i.e. all resources are considered equally available). The functions can compute bootstrap confidence intervals following the bias-corrected percentile method (Manly 2007). If mode = "multiple" and Np1 and Np2 are not null, bootstrap samples for a given niche are generated assuming a multinomial distribution with the proportions calculated from the corresponding row values in P1 (resp. P2), and the number of observations comes from the corresponding element in Np1 (resp. Np2). Similarly, if mode = "pairwise" and Np1 is not null, bootstrap samples for each niche are generated assuming a multinomial distribution with the proportions calculated from the corresponding row values in P1, and the number of observations comes from the corresponding element in Np1. Finally, if mode = "single" then the bootstrapped units are the rows of matrices P1 and P2. In both cases, if Nq1 (and Nq2) is indicated, the availability of resources is also bootstrapped. The bias-corrected percentile method is described for overlap niche measures in Mueller and Altenberg (1985).
Mueller, L.D. and L. Altenberg. 1985. Statistical Inference on Measures of Niche Overlap. Ecology 66:1204-1210.
Manly, B.F.J. 2007. Randomization, bootstrap and Monte Carlo methods in biology. Chapman and Hall texts in statistical science series. 2nd edition.
De Caceres, M., Sol, D., Lapiedra, O. and P. Legendre. (2011) A framework for estimating niche metrics using the resemblance between qualitative resources. Oikos 120: 1341-1350.
See nichevar for descriptors of single niches.
# Loads example data
data(birds)
# The overlap and displacement metrics using distances among
# resources and assuming equal availability of resources
nicheoverlap(birdsbreed, birdswinter, D = resourceD, mode="multiple")
#> O
#> sp1B vs sp1W 0.9180321
#> sp2B vs sp2W 0.9928700
#> sp3B vs sp3W 1.0000000
#> sp4B vs sp4W 0.9982815
#> sp5B vs sp5W 0.9168365
#> sp6B vs sp6W 0.9538433
#> sp7B vs sp7W 0.9843038
#> sp8B vs sp8W 0.4964025
#> sp9B vs sp9W NA
#> sp10B vs sp10W 0.7178288
nichedispl(birdsbreed, birdswinter, D = resourceD, mode="multiple")
#> CD
#> sp1B vs sp1W 0.24372798
#> sp2B vs sp2W 0.07334250
#> sp3B vs sp3W 0.00000000
#> sp4B vs sp4W 0.03636655
#> sp5B vs sp5W 0.24986173
#> sp6B vs sp6W 0.19374648
#> sp7B vs sp7W 0.10245775
#> sp8B vs sp8W 0.69797974
#> sp9B vs sp9W NA
#> sp10B vs sp10W 0.42080273
# The overlap and displacement metrics using distances among resources
# and computes 95 percent confidence intervals
nicheoverlap(birdsbreed, birdswinter, D = resourceD, mode="multiple",
Np1 = rowSums(birdsbreed), Np2 = rowSums(birdswinter), Nq1 = 100, Nq2 = 100)
#> O LC UC
#> sp1B vs sp1W 0.9180321 0.7778471 0.9889358
#> sp2B vs sp2W 0.9928700 0.9683249 0.9999344
#> sp3B vs sp3W 1.0000000 NA NA
#> sp4B vs sp4W 0.9982815 0.9782195 1.0000000
#> sp5B vs sp5W 0.9168365 0.7753018 0.9934085
#> sp6B vs sp6W 0.9538433 0.8561138 0.9934876
#> sp7B vs sp7W 0.9843038 0.9237544 0.9999563
#> sp8B vs sp8W 0.4964025 0.4709896 0.5755985
#> sp9B vs sp9W NA NA NA
#> sp10B vs sp10W 0.7178288 0.5485523 0.8835202
nichedispl(birdsbreed, birdswinter, D = resourceD, mode="multiple",
Np1 = rowSums(birdsbreed), Np2 = rowSums(birdswinter), Nq1 = 100, Nq2 = 100)
#> CD LC UC
#> sp1B vs sp1W 0.24372798 0.10558695 0.4052914
#> sp2B vs sp2W 0.07334250 NA NA
#> sp3B vs sp3W 0.00000000 NA NA
#> sp4B vs sp4W 0.03636655 NA NA
#> sp5B vs sp5W 0.24986173 0.06249290 0.4101080
#> sp6B vs sp6W 0.19374648 0.06069387 0.3023116
#> sp7B vs sp7W 0.10245775 NA NA
#> sp8B vs sp8W 0.69797974 0.60905766 0.7273310
#> sp9B vs sp9W NA NA NA
#> sp10B vs sp10W 0.42080273 0.23462515 0.5157774
# Same computations with different resource availability
q <- c(0.18, 0.24, 0.22, 0.21, 0.15)
nicheoverlap(birdsbreed, birdswinter, D = resourceD,
q1 = q, q2 = q, mode="multiple")
#> O
#> sp1B vs sp1W 0.9395885
#> sp2B vs sp2W 0.9918133
#> sp3B vs sp3W 1.0000000
#> sp4B vs sp4W 0.9986449
#> sp5B vs sp5W 0.8933637
#> sp6B vs sp6W 0.9727062
#> sp7B vs sp7W 0.9848794
#> sp8B vs sp8W 0.4919339
#> sp9B vs sp9W NA
#> sp10B vs sp10W 0.6624301
nichedispl(birdsbreed, birdswinter, D = resourceD,
q1 = q, q2 = q, mode="multiple")
#> CD
#> sp1B vs sp1W 0.20977627
#> sp2B vs sp2W 0.07753105
#> sp3B vs sp3W 0.00000000
#> sp4B vs sp4W 0.03321146
#> sp5B vs sp5W 0.27919844
#> sp6B vs sp6W 0.15151280
#> sp7B vs sp7W 0.10178862
#> sp8B vs sp8W 0.70300550
#> sp9B vs sp9W NA
#> sp10B vs sp10W 0.46117552
nicheoverlap(birdsbreed, birdswinter, D = resourceD,
q1 = q, q2 = q, mode="multiple",
Np1 = rowSums(birdsbreed), Np2 = rowSums(birdswinter),
Nq1 = 100, Nq2 = 100)
#> O LC UC
#> sp1B vs sp1W 0.9395885 0.8288781 0.9915897
#> sp2B vs sp2W 0.9918133 0.9612236 0.9999365
#> sp3B vs sp3W 1.0000000 NA NA
#> sp4B vs sp4W 0.9986449 0.9856759 1.0000000
#> sp5B vs sp5W 0.8933637 0.7093999 0.9866541
#> sp6B vs sp6W 0.9727062 0.9073125 0.9969542
#> sp7B vs sp7W 0.9848794 0.9335547 0.9999132
#> sp8B vs sp8W 0.4919339 0.4709896 0.5623808
#> sp9B vs sp9W NA NA NA
#> sp10B vs sp10W 0.6624301 0.4917245 0.8454383
nichedispl(birdsbreed, birdswinter, D = resourceD,
q1 = q, q2 = q, mode="multiple",
Np1 = rowSums(birdsbreed), Np2 = rowSums(birdswinter),
Nq1 = 100, Nq2 = 100)
#> CD LC UC
#> sp1B vs sp1W 0.20977627 0.086164161 0.3543351
#> sp2B vs sp2W 0.07753105 NA NA
#> sp3B vs sp3W 0.00000000 NA NA
#> sp4B vs sp4W 0.03321146 NA NA
#> sp5B vs sp5W 0.27919844 0.052423195 0.4440854
#> sp6B vs sp6W 0.15151280 0.050763270 0.2604657
#> sp7B vs sp7W 0.10178862 0.005094585 0.2383716
#> sp8B vs sp8W 0.70300550 0.609057660 0.7273310
#> sp9B vs sp9W NA NA NA
#> sp10B vs sp10W 0.46117552 0.318604171 0.5767065
# The overlap metrics using distances among rows of 'birdsbreed'
nicheoverlap(birdsbreed, D = resourceD, mode="pairwise")
#> sp1B sp2B sp3B sp4B sp5B sp6B sp7B
#> sp1B 1.0000000 0.6645078 0.8576099 0.9841992 0.9225876 0.4493889 0.9896160
#> sp2B 0.6645078 1.0000000 0.3558765 0.5803276 0.8508925 0.9155505 0.7246835
#> sp3B 0.8576099 0.3558765 1.0000000 0.9351235 0.7716197 0.2257267 0.8657498
#> sp4B 0.9841992 0.5803276 0.9351235 1.0000000 0.9012619 0.3873154 0.9798477
#> sp5B 0.9225876 0.8508925 0.7716197 0.9012619 1.0000000 0.6669124 0.9654380
#> sp6B 0.4493889 0.9155505 0.2257267 0.3873154 0.6669124 1.0000000 0.5220447
#> sp7B 0.9896160 0.7246835 0.8657498 0.9798477 0.9654380 0.5220447 1.0000000
#> sp8B 0.8717161 0.3989051 0.9987823 0.9444225 0.8008132 0.2648938 0.8843865
#> sp9B 0.9152202 0.8952192 0.6634003 0.8589282 0.9786929 0.6891189 0.9435430
#> sp10B 0.5428608 0.9486252 0.2892194 0.4735713 0.7571142 0.9416413 0.6144665
#> sp8B sp9B sp10B
#> sp1B 0.8717161 0.9152202 0.5428608
#> sp2B 0.3989051 0.8952192 0.9486252
#> sp3B 0.9987823 0.6634003 0.2892194
#> sp4B 0.9444225 0.8589282 0.4735713
#> sp5B 0.8008132 0.9786929 0.7571142
#> sp6B 0.2648938 0.6891189 0.9416413
#> sp7B 0.8843865 0.9435430 0.6144665
#> sp8B 1.0000000 0.6953454 0.3308257
#> sp9B 0.6953454 1.0000000 0.7846453
#> sp10B 0.3308257 0.7846453 1.0000000