Empirical Analysis of Factors Influencing Elephant Population Increases: A Case Study of Tanzania National Park

This study employs empirical analysis through linear regression to investigate the determinants of elephant population increases (EP). Utilizing a dataset comprising 255 observations, the research evaluates the impact of various predictor variables such as Elephant crime decrease (ECD), Elephant death decrease (EDD), Elephant poaching decrease (EPD), Elephant conflict increase (ECI), Elephant tracking improved (ETI), Patrol coverage increase (PCI), GIS coordination (GC), and Communication improves (CI) on Elephant population dynamics. The regression model's overall significance (F (8, 246) = 41.4, Prob > F = 0) indicates a robust explanation of variance in EP by the combined effect of predictors. Notably, Elephant death decrease, increased patrol coverage, and improved communication emerge as statistically significant predictors of EP. The findings underscore the importance of targeted interventions in reducing elephant mortality, enhancing patrolling efforts, and strengthening communication strategies for wildlife conservation management. This research contributes to the understanding of factors influencing elephant population dynamics, thereby assisting stakeholders such as wildlife conservation agencies, government bodies, and researchers in informed decision-making.


INTRODUCTION
Globally, Elephants, as one of the most iconic and charismatic megafaunas play a crucial role in maintaining ecosystem balance and biodiversity in conservation globally (Jones et al., 2018).However, the population of elephants has been under significant threat due to various anthropogenic factors such as habitat loss, poaching, human-wildlife conflicts, and inadequate conservation measures (Lamarque et al., 2017).According to the International Union for the Conservation of Nature (IUCN), African elephants (Loxodonta africana) are listed as vulnerable, with populations declining at an alarming rate (IUCN Red List, 2020).Africa harbors the majority of the world's elephant population, making it a critical region for elephant conservation efforts (Wittemyer et al., 2014).However, despite concerted conservation initiatives, the continent has witnessed a sharp decline in elephant numbers over the past few decades.Rampant poaching driven by the illegal ivory trade, habitat fragmentation, and increasing human-wildlife conflicts pose significant challenges to elephant survival across various African countries (Maisels et al., 2013).Tanzania, located in East Africa, boasts one of the largest elephant populations on the continent.However, the country has experienced a substantial decline in elephant numbers in recent years due to widespread poaching and habitat degradation (Chase et al., 2016).Factors such as inadequate law enforcement, weak governance, and socio-economic pressures exacerbate the threats faced by elephants in Tanzania (Lindsey et al., 2013).Despite government-led conservation efforts and international support, the decline in elephant populations persists, raising concerns about the long-term viability of elephant populations in the country.
In light of these challenges, understanding the factors influencing elephant population dynamics is essential for effective conservation planning and management.Empirical analysis using regression modelling provides a valuable tool for identifying key drivers of elephant population increases, thereby informing evidence-based conservation strategies that can be tailored to address specific challenges faced at the global, African, and national levels.

LITERATURE REVIEW
Globally, elephants, as keystone species, have garnered considerable attention in conservation discourse owing to their ecological significance and cultural importance (Choudhury, 2019)

METHODOLOGY Description of the study area
The study focuses on three parks within the Tanzania National Parks system: Ruaha, Tarangire, and Serengeti.These parks were chosen due to their involvement in donor-funded projects introducing various Geographic Information System (GIS) technologies.Tanzania National Parks (TANAPA), a parastatal organization, manages these parks according to regulations such as the National Parks Act and the Wildlife Conservation Act.TANAPA oversees 21 national parks covering 10% of Tanzania's land area, aiming to conserve and manage wildlife while enforcing related laws.Notably, the elephant populations in the study parks estimated numbers 5,160 in Serengeti, 3,282 in Tarangire, and 4,210 in Ruaha comprise over half of Tanzania's total elephant population, highlighting the parks' significance in biodiversity conservation efforts.

Data Analysis
Linear Regression Analysis: The primary method employed in this research is linear regression analysis using STATA.This statistical technique allows for the examination of the relationship between the dependent variable (Elephant population increases) and multiple independent variables (predictors).The regression equation was estimated to assess the strength and direction of the impact of each predictor variable on Elephant population increases.

Model Fit Assessment
The overall fit of the regression model was evaluated using the F-test, with significance determined at a predetermined alpha level (α = 0.05).The F-statistic tests the null hypothesis that all regression coefficients are equal to zero, indicating that the model as a whole does not provide a better fit than a model with no predictors.

Coefficient Estimation
Coefficients for each predictor variable were estimated to quantify the magnitude and direction of their relationship with Elephant population increases.Standard errors, t-values, and p-values were computed to assess the statistical significance of each coefficient.Predictor variables with p-values less than 0.05 were considered statistically significant.

Model Diagnostics
Diagnostic tests were conducted to evaluate the assumptions of linear regression, including the normality of residuals, homoscedasticity, and multicollinearity among predictor variables.Residual analysis and goodness-of-fit measures such as adjusted R-squared and root mean squared error (RMSE) were used to assess overall model performance and the accuracy of predictions.

Stakeholder Engagement
Throughout the research process, engagement with stakeholders including wildlife conservation agencies, government bodies, and researchers was facilitated to ensure the relevance and applicability of the findings.Stakeholder inputs were sought during the formulation of research questions, data collection, analysis, and interpretation stages.Ethical Considerations Ethical guidelines were followed throughout the research process, ensuring the confidentiality and privacy of data sources.Permission was obtained from relevant authorities for the use of proprietary datasets, and efforts were made to acknowledge and respect the contributions of stakeholders involved in data collection and conservation efforts.

Linear regression analysis
The linear regression analysis was conducted using STATA on a dataset consisting of 255 observations.The model aimed to explore the relationship between Elephant population increases (EP) and several

CONCLUSION AND POLICY RECOMMENDATION Conclusion
The findings from the linear regression analysis provide valuable insights into the factors influencing Elephant population increases (EP).The regression model demonstrated a statistically significant relationship between EP and several predictor variables, including Elephant crime decrease (ECD), Elephant death decrease (EDD), Elephant poaching decrease (EPD), Elephant conflict increase (ECI), Elephant tracking improved (ETI), Patrol coverage increase (PCI), GIS coordination (GC), and Communication improvements (CI).Specifically, Elephant death decrease, increased patrol coverage, and improved communication emerged as significant predictors of EP, highlighting the importance of targeted interventions in reducing Elephant mortality, enhancing patrolling efforts, and strengthening communication strategies for wildlife conservation management.These findings underscore the complexity of factors shaping Elephant population dynamics and emphasize the need for multi-faceted approaches to address conservation challenges.While the regression model accounted for a substantial portion of the variance in EP, there may be additional unmeasured factors contributing to Elephant population increases.Future research should explore these factors to provide a comprehensive understanding of Elephant conservation dynamics.

Figure 1 :
Figure 1:study areas map showing Serengeti, Tarangire and Ruaha National parks predictor variables.The overall model fit was statistically significant (F (8, 246) = 41.4,Prob > F = 0), indicating that the combined effect of the predictor variables significantly explained the variance in Elephant population increases.The regression equation is given by EP = -0.0416-0.2179CI + 0.0554GC + 0.2891PCI + 0.1656ETI + 0.0953ECI -0.0915EPD + 0.4706EDD + 0.1776ECD.The coefficients for Elephant crime decrease (ECD), Elephant death decrease (EDD), Elephant poaching decrease (EPD), Elephant conflict increase (ECI), Elephant tracking improved (ETI), Patrol coverage increase (PCI), GIS coordination (GC), and Communication improves (CI) were estimated, indicating the strength and direction of their impact on Elephant population increases.The results revealed that Elephant death decrease (EDD), Patrol coverage increase (PCI), and Communication improvement (CI) were statistically significant predictors of Elephant population increases, as indicated by their p-values (P>t) being less than 0.05.The adjusted R-squared value of 0.5599 suggests that the model accounts for approximately 55.99% of the variance in Elephant population increases after adjusting for the number of predictors.The root mean squared error (Root MSE) is 0.58536, representing the standard deviation of the residuals.The data suggests that Elephant death decrease, increased patrol coverage, and improved communication are associated with higher Elephant population increases.Stakeholders involved in this analysis might include wildlife conservation agencies, government bodies, and researchers interested in understanding the factors influencing Elephant population dynamics.

wildlife conflicts in African countries
(Røskaft et al., 2018)8., 2018).Conservation strategies aimed at mitigating these challenges often involve a combination of law enforcement, community engagement, and innovative technologies to monitor and protect elephant populations effectively(Daskin & Pringle, 2018).Tanzania's elephant populations face significant threats from poaching syndicates, habitat encroachment, and conflicts with local communities(Røskaft et al., 2018).Despite being endowed with extensive protected areas, such as the Serengeti and Selous ecosystems, the country continues to grapple with escalating wildlife crimes and inadequate conservation enforcement(Duffy et al., 2016).Research focusing on Tanzania's elephant populations has highlighted the importance of addressing governance challenges, strengthening law enforcement efforts, and promoting community-based conservation initiatives to safeguard elephant habitats and mitigate human-elephant conflicts(Nelson et al., 2020).The literature underscores the urgent need for collaborative and evidence-based approaches to address the multifaceted challenges confronting elephant populations globally, across Africa, and within Tanzania(Røskaft et al., 2018).Empirical analyses, such as regression modeling, offer valuable insights into the factors shaping elephant population dynamics, thereby informing targeted conservation interventions tailored to specific contexts and challenges.
• Enhanced Law Enforcement: Strengthening anti-poaching efforts and wildlife crime enforcement is imperative to combat illegal activities targeting Elephants.Increased collaboration between law enforcement agencies, conservation organizations, and local communities can bolster surveillance and enforcement efforts to deter poaching activities.• Investment in Patrol Coverage: Allocating resources towards expanding and improving patrol coverage in Elephant habitats can help mitigate human-wildlife conflicts and prevent illegal activities.Utilizing technology-enabled monitoring systems and increasing ranger presence in vulnerable areas can enhance the protection of Elephant populations.• Communication and Community Engagement: Implementing effective communication strategies and engaging local communities in conservation initiatives are essential for fostering positive attitudes towards Elephants and promoting coexistence.Education and awareness programs should emphasize the ecological importance of Elephants and the benefits of conservation efforts to local communities.• Integrated Conservation Planning: Adopting an integrated approach to conservation planning that considers the interconnectedness of ecosystems, wildlife populations, and human livelihoods is crucial for sustainable Elephant conservation.Conservation strategies should prioritize habitat preservation, land-use planning, and ecosystem restoration to ensure the long-term viability of Elephant populations.• Policy Coordination and Collaboration: Encouraging collaboration between government agencies, non-governmental organizations, research institutions, and local stakeholders is essential for implementing effective conservation policies.Coordinated efforts at the national and regional levels can facilitate the sharing of resources, expertise, and best practices to address conservation challenges comprehensively.