Rodent-mediated plant community competition:what happens to the seeds after entering the adjacent stands?

Background:Seed dispersal by scatter-hoarding animals can affect the developmental dynamics of plant communities.However,how animals might participate in plant inter-community competition has rarely been investigated.Forest community junction is an area where the competition between plant communities is most prominent and animal activity is more frequent.At present,little is known about how scatter-hoarding animals might assist competitions by adjacent plant communities.Thus,for 3 years(2015–2017),we tracked the fate of 2880 tagged seeds(Quercus aliena var.acuteserrata,Pinus tabuliformis,and P.armandii seed)placed near an edge where the forest composition changes from a pine forest to an oak forest in northwestern China.Results:We found that the seed fates differed when Quercus and Pinus seeds entered adjacent stands.In contrast to Pinus seeds,acorns that entered pine forests were characterized by higher caching rates and longer dispersal distances.Pinus seeds had the highest probability of being predated(85%)by rodents,and eleven Q.aliena var.acuteserrata seedlings were established in pine forests,although none survived in the later stages.In addition,rodents exhibited obvious selectivity in terms of the microhabitats for the seed caching sites.Conclusions:Seed fates differed when Quercus and Pinus seeds entered adjacent stands.The predation pressure by rodents on the seeds of Pinus species limited the germination of seeds and seedling establishment in oak forests.The different seed fates after their bidirectional dispersal could affect the differences in natural regeneration between pine and oak forests,and they might increase the recruitment rates for oak at the edge of an adjacent community.Rodent-mediated seed dispersal could potential unintentionally affect the competition between plant communities.

Wild Gibbon Optimization Algorithm

Complex optimization problems hold broad significance across numerous fields and applications.However,as the dimensionality of such problems increases,issues like the curse of dimensionality and local optima trapping also arise.To address these challenges,this paper proposes a novel Wild Gibbon Optimization Algorithm(WGOA)based on an analysis of wild gibbon population behavior.WGOAcomprises two strategies:community search and community competition.The community search strategy facilitates information exchange between two gibbon families,generating multiple candidate solutions to enhance algorithm diversity.Meanwhile,the community competition strategy reselects leaders for the population after each iteration,thus enhancing algorithm precision.To assess the algorithm’s performance,CEC2017 and CEC2022 are chosen as test functions.In the CEC2017 test suite,WGOA secures first place in 10 functions.In the CEC2022 benchmark functions,WGOA obtained the first rank in 5 functions.The ultimate experimental findings demonstrate that theWildGibbonOptimization Algorithm outperforms others in tested functions.This underscores the strong robustness and stability of the gibbonalgorithm in tackling complex single-objective optimization problems.