Jardim, L. L. C. Z. | Search bioRxiv

bioRxiv2025

JAGUAR CONSERVATION UNITS AS EFFECTIVE TOOL IN A PAYMENT FOR ECOSYSTEM SERVICES SCHEME FOR APEX PREDATOR

This paper proposes a redefinition of Jaguar Conservation Units, emphasizing the role of private rural properties in jaguar populations in Brazil while mitigating conflicts with the species and allowing a more effective implementation of payment for the species environmental services. Jaguars prey domestic livestock, leading to retaliatory killings by ranchers. In this context, the study aims to analyze the environmental and economic parameters associated with the ecosystem services provided by jaguars. The ultimate goal is to develop a regional Payment for Environmental Services (PES) scheme to support jaguar conservation by promoting forest code compliance, maintaining habitat connectivity, and compensating cattle predation, using data from Mato Grosso do Sul (MS), Brazil. Our findings demonstrate the advantages of implementing such program and reveal that the cost of maintaining jaguar populations through a PES, with funds from several independent sources, would be feasible and important for one of the largest jaguar populations in Brazil. HIGHLIGHTSO_LIAnalysis of environmental and economic parameters linked to jaguar ecosystem services. C_LIO_LIDevelopment of a regional Payment for Environmental Services (PES) scheme in Mato Grosso do Sul. C_LIO_LIEvidence of the feasibility and importance of PES to maintain one of the largest jaguar populations in Brazil. C_LI

ecology↗

bioRxiv2016

Challenging the Raunkiaeran shortfall and the consequences of using imputed databases

O_LIGiven the prevalence of missing data on species traits - Raunkiaeran shorfall -- and its importance for theoretical and empirical investigations, several methods have been proposed to fill sparse databases. Despite its advantages, imputation of missing data can introduce biases. Here, we evaluate the bias in descriptive statistics, model parameters, and phylogenetic signal estimation from imputed databases under different missing and imputing scenarios.\nC_LIO_LIWe simulated coalescent phylogenies and traits under Brownian Motion and different Ornstein-Uhlenbeck evolutionary models. Missing values were created using three scenarios: missing completely at random, missing at random but phylogenetically structured and missing at random but correlated with some other variable. We considered four methods for handling missing data: delete missing values, imputation based on observed mean trait value, Phylogenetic Eigenvectors Maps and Multiple Imputation by Chained Equations. Finally, we assessed estimation errors of descriptive statistics (mean, variance), regression coefficient, Morans correlogram and Blombergs K of imputed traits.\nC_LIO_LIWe found that percentage of missing data, missing mechanisms, Ornstein-Uhlenbeck strength and handling methods were important to define estimation errors. When data were missing completely at random, descriptive statistics were well estimated but Morans correlogram and Blombergs K were not well estimated, depending on handling methods. We also found that handling methods performed worse when data were missing at random, but phylogenetically structured. In this case adding phylogenetic information provided better estimates. Although the error caused by imputation was correlated with estimation errors, we found that such relationship is not linear with estimation errors getting larger as the imputation error increases.\nC_LIO_LIImputed trait databases could bias ecological and evolutionary analyses. We advise researchers to share their raw data along with their imputed database, flagging imputed data and providing information on the imputation process. Thus, users can and should consider the pattern of missing data and then look for the best method to overcome this problem. In addition, we suggest the development of phylogenetic methods that consider imputation uncertainty, phylogenetic autocorrelation and preserve the level of phylogenetic signal of the original data.\nC_LI

evolutionary biology↗