Accounting for biotic interactions through alpha‐diversity constraints in stacked species distribution models

Abstract

and tend to overpredict the number of species that can coexist at a given location and time 32(hereafter, the alpha-capacity). We developed an extension of SDMs that integrates species-33 level and community-level modelling to account for the above drivers. 34 2. The alpha-adjusted SDM takes the Probabilities of Occurrence (PoO) for all species of a 35 community -capacity and adjusts the PoO, such that: a. their sum will equal 36 the alpha-capacity as predicted by probability theory; and b. the adjusted PoO are dependent 37 upon the relative suitability of each species for that site. The new method was tested using 38 community data comprising 87 freshwater invertebrate species in an LTER watershed in 39Germany. We explored the ability of the method to predict alpha and beta-diversity patterns. 40We further focused on the effect on model performance at the species-level of the error 41 associated with modelling alpha-capacity, of differences in gamma diversity (the size of the 42 community) and of the type of community (random or guild-based). 433. The models that predicted alpha-capacity contained considerable error, and thus adjusting the 44 PoO according to the modelled alpha-capacity resulted with decreased performance at the 45 species level. However, when using the observed alpha-capacity to mimic a good alpha-capacity 46 model, the alpha-adjusted SDMs usually resulted in increased performance. We further found 47 that the alpha-adjusted SDM was better than the original SDM at predicting beta-diversity 48 patterns, especially when using similarity indices that are sensitive to double absences. 49 4. Using the alpha-adjusted SDM approach may increase the predictive performance at the species 50 and community levels if alpha-capacity can be assessed or modelled with sufficient accuracy, 51 especially in relatively small communities of closely interacting species. With better models to 52 predict alpha-capacity being developed, alpha-adjusted SDM has considerable potential to 53 provide more realistic predictions of species-distribution patterns. 54 3

Disentangling the processes driving tree community assembly in a tropical biodiversity hotspot (New Caledonia)

“…New Guinea, the largest within the southern hemisphere), and stem density is among the highest reported in the southwest Pacific (Ibanez et al., ). Despite New Caledonia's high species diversity, our study suggests that communities are at least in part limited by the carrying capacity of local environment due, for example, to limited available resources at local scale or geometric constraints (Calabrese et al., ; Gavish et al., ; Pouteau et al., ).…”

“…We based this step on two primary assumptions: (a) environmental constraints limit the number of species that can coexist in a community (saturation hypothesis, see Mateo et al., ) and (b) a species that has a higher value of habitat suitability will have a greater probability of occurring in a site (D'Amen, Dubuis, et al., ; D'Amen, Pradervand, & Guisan, ; Gavish et al., ). Given the above assumptions, community composition was determined by ranking species in decreasing order of their predicted probability up to the local species richness prediction.…”

“…We based this step on two primary assumptions: (a) environmental constraints limit the number of species that can coexist in a community (saturation hypothesis, see Mateo et al, 2017) and (b) a species that has a higher value of habitat suitability will have a greater probability of occurring in a site (D'Amen, Dubuis, et al, 2015;D'Amen, Pradervand, & Guisan, 2015;Gavish et al, 2017).…”

A pathway for multivariate analysis of ecological communities using copulas

“…SDMs can fail to capture the nature of inter‐specific associations reliably, particularly for organisms having low probabilities of occurrence (Zurell et al, ). Observational data are often too sparse to model rare taxa well individually, but richness (number of species per sampling unit) can be modeled as a Poisson (or Poisson‐binomial) random variable (Calabrese, Certain, Kraan, & Dormann, ; Gavish et al, ). Thus, future model developments could include richness as an additional response variable in a multivariate copula.…”

Combining eight research areas to foster the uptake of ecosystem‐based management in fresh waters

“…Improvements to addressing the uncertainty of SDM outputs include their correction for biotic interactions, which are at present only implicitly incorporated (models and parameter inference are based on current observational data that inherently include the effects of biotic interactions), the integration of species movement, dispersal, and history, and the effects of genetic differentiation (Qi et al, ). For example, Gavish et al () showed for the benthic invertebrate community from the Kinzig catchment (central Germany) that, in certain cases, SDMs that incorporate surrogates for biotic interactions increase the predictive performance at the species and community levels. Hence, the continued development of SDMs will provide further opportunities for their successful uptake in EBM frameworks.…”